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Publication numberUS20050080613 A1
Publication typeApplication
Application numberUS 10/921,954
Publication dateApr 14, 2005
Filing dateAug 20, 2004
Priority dateAug 21, 2003
Also published asCA2536262A1, CA2536265A1, CA2536265C, CA2536270A1, CN1839386A, CN1871597A, CN1871597B, CN1871603A, CN1871603B, CN100580666C, EP1661031A1, EP1661031A4, EP1665091A1, EP1665091A4, EP1665092A1, EP1665092A4, US7509313, US7895221, US20050080776, US20050080780, US20110202563, WO2005020091A1, WO2005020092A1, WO2005020093A1
Publication number10921954, 921954, US 2005/0080613 A1, US 2005/080613 A1, US 20050080613 A1, US 20050080613A1, US 2005080613 A1, US 2005080613A1, US-A1-20050080613, US-A1-2005080613, US2005/0080613A1, US2005/080613A1, US20050080613 A1, US20050080613A1, US2005080613 A1, US2005080613A1
InventorsMatthew Colledge, Pierre Belzile, Jeremy Barnes
Original AssigneeMatthew Colledge, Pierre Belzile, Jeremy Barnes
Export CitationBiBTeX, EndNote, RefMan
External Links: USPTO, USPTO Assignment, Espacenet
System and method for processing text utilizing a suite of disambiguation techniques
US 20050080613 A1
Abstract
The invention relates to a system and method for processing natural language text utilizing disambiguation components to identify a disambiguated sense for the text. For the method, it comprises applying a selection of the components to the text to identify a local disambiguated sense for the text. Each component provides a local disambiguated sense of the text with a confidence score and a probability score. The disambiguated sense is determined utilizing a selection of local disambiguated senses. The invention also relates to a system and method for generating sense-tagged text. For the method, it comprises steps of: disambiguating a quantity of documents utilizing a disambiguation component; generating a confidence score and a probability score for a sense identified for a word provided by the component; if the confidence score for the sense for the word is below a set threshold, the sense is ignored; and if the confidence score for the sense for the word is above the set threshold, the sense is added to the sense-tagged text.
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Claims(18)
1. A method of processing natural language text utilizing a plurality of disambiguation components to identify a disambiguated sense for said text, said method comprising steps of:
applying a selection of components from said plurality of disambiguation components to said text to identify a local disambiguated sense for said text,
wherein
each component of said selection provides a local disambiguated sense of said text with a confidence score and a probability score; and
said disambiguated sense is determined utilizing a selection of local disambiguated senses from said selection.
2. The method of processing natural language text as claimed in claim 1, wherein said selection of components are sequentially activated and controlled by a central module.
3. The method of processing natural language text as claimed in claim 2, further comprising
identifying a second selection of components from said plurality of components;
applying said second selection to said text to refine said disambiguated sense,
wherein
each component of said second selection provide a second local disambiguated sense of said text with a second confidence score and a second probability score; and
said disambiguated sense is determined utilizing a selection of second local disambiguated senses from said second selection.
4. The method of processing natural language text as claimed in claim 3, further comprising
after applying said selection to said text and prior to applying said second selection to refine said disambiguated sense, eliminating a sense from said disambiguated sense having a confidence score below a threshold.
5. The method of processing natural language text as claimed in claim 4, wherein when a particular component of said plurality of components is present in said selection and said second selection, at least one of its confidence and probability scores is adjusted when applying said second selection to said text.
6. The method of processing natural language text as claimed in claim 4, wherein said selection and said second selection of components are identical.
7. The method of processing natural language text as claimed in claim 4, wherein said confidence score of said each component is generated by a confidence function utilizing a trait of each component.
8. The method of processing natural language text as claimed in claim 4, wherein after applying said selection of components to said text to identify a local disambiguated sense for said text, said method further comprising
for each said component of said selection, generating a probability distribution for its disambiguated sense; and
merging all probability distributions for said selection.
9. The method of processing natural language text as claimed in claim 8, wherein said selection of component disambiguates said text using context of said text identified from one of domain; user history; and specified contexts.
10. The method of processing natural language text as claimed in claim 8, further comprising after applying said selection to said text, refining a knowledge base of each component in said selection utilizing said disambiguated sense.
11. The method of processing natural language text as claimed in claim 4, wherein at least one of said selection of components provides results only for coarse sense s.
12. The method of processing natural language text as claimed in claim 4, wherein results of said selection of components are combined into one result utilizing a merging algorithm.
13. The method of processing natural language text as claimed in claim 12, wherein said process utilizes a first stage comprising merging of coarse senses, and a second stage comprising merging of fine senses within each coarse sense grouping.
14. The method of processing natural language text as claimed as claimed in claim 13, wherein said merging process utilizes a weighted sum of probability distributions, and said weights are the confidence score associated with said distribution, and wherein said merging process comprises a weighted average of confidence scores, and said weights are again the confidence scores associated with said distribution.
15. A method of generating sense-tagged text, said method comprising steps of:
disambiguating a quantity of documents utilizing a disambiguation component;
generating a confidence score and a probability score for a sense identified for a word provided by said component;
if said confidence score for said sense for said word is below a set threshold, said sense is ignored; and
if said confidence score for said sense for said word is above said set threshold, said sense is added to said sense-tagged text.
16. A method of processing natural language text utilizing a plurality of disambiguation components to identify a disambiguated sense or senses for said text, said method comprising steps of:
defining an accuracy target for disambiguation; and
applying a selection of components from said plurality of disambiguation components to meet said accuracy target.
17. A method of processing natural language text utilizing a plurality of disambiguation components to identify a disambiguated sense for said text, said method comprising steps of:
identifying a set of senses for said text; and
identifying and removing an unwanted sense from said set.
18. A method of processing natural language text utilizing a plurality of disambiguation components to identify a disambiguated sense for said text, said method comprising steps of:
identifying a set of senses for said text; and
identifying and removing a specified amount of ambiguity from said set of senses.
Description
RELATED APPLICATION

This application claims the benefit of U.S. Provisional Application No. 60/496,681 filed on Aug. 21, 2003.

FIELD OF THE INVENTION

The present invention relates to disambiguating natural language text, such as queries to an Internet search engine, web pages and other electronic documents, and disambiguating textual output of a speech to text system.

BACKGROUND

Word sense disambiguation is the process of determining the meaning of words in text. For example, the word “bank” can mean a financial institution, an embankment, or an aerial manoeuvre (or several other meanings). When humans listen to or read naturally expressed language, they automatically select the correct meaning of each word based on the context in which it is expressed. A word sense disambiguator is a computer-based system for accomplishing this task, and is a critical component of technology for making naturally expressed language understandable to computers.

A word sense disambiguator is used in applications which require or which can be improved by making use of the meaning of the words in the text. Such applications include but are not limited to: Internet search and other information retrieval applications; document classification; machine translation; and speech recognition.

It is accepted by those skilled in the art that, although humans perform word sense disambiguation effortlessly, and this is a critical step in understanding naturally expressed language, no system has yet been developed to accomplish word sense disambiguation of general texts to an accuracy sufficient to permit deployment in such applications. Even current advanced word sense disambiguation systems may have an accuracy of only approximately 33%, thereby making their results too inaccurate for many applications.

There is a need for word sense disambiguation system and method which addresses deficiencies in the prior art.

SUMMARY OF THE INVENTION

In a first aspect, a method of processing natural language text utilizing disambiguation components to identify a disambiguated sense or senses for the text is provided. The method comprises applying a selection of the components to the text to identify a local disambiguated sense for the text. Each component provides a local disambiguated sense of the text with a confidence score and a probability score. The disambiguated sense is determined utilizing a selection of local disambiguated senses.

In the method, the components are sequentially activated and controlled by a central module.

The method may further comprise identifying a second selection of components; and applying the second selection to the text to refine the disambiguated sense (or senses). Each component in the second selection provides a second local disambiguated sense (or senses) of the text with a second confidence score and a second probability score. The disambiguated sense (or senses) is determined utilizing a selection of the second local disambiguated senses.

In the method, after applying the selection to the text and prior to applying the second selection to refine the disambiguated sense (or senses), the further step of eliminating a sense from the disambiguated sense having a confidence score below a threshold may be executed.

In the method, when a particular component is present in the selection and the second selection, its confidence and probability scores may be adjusted when applying the second selection to the text.

In the method, the selection and the second selection of components may be identical.

In the method, the confidence score of the each component may be generated by a confidence function utilizing a trait of each component.

After applying the selection of components to the text to identify a local disambiguated sense (or senses) for the text, for each component of the selection, the method may generate a probability distribution for its disambiguated sense (or senses). Further the method may merge all probability distributions for the selection.

In the method, the selection of component disambiguates the text using context of the text may be identified from one of the following contexts: domain; user history; and specified context.

After applying the selection to the text, the method may refine a knowledge base of each component in the selection utilizing the disambiguated sense (or senses).

In the method at least one of the selection of components provides results only for coarse senses.

In the method, results of the selection of components may be combined into one result utilizing a merging algorithm.

In the method, the process may utilize a first stage comprising merging of coarse senses, and a second stage comprising merging of fine senses within each coarse sense grouping.

In the method, the merging process may utilize a weighted sum of probability distributions, and the weights may be the confidence score associated with the distribution. Further, the merging process may comprise a weighted average of confidence scores, and the weights are again the confidence scores associated with the distribution.

In another aspect, a method of processing natural language text utilizing disambiguation components to identify a disambiguated sense for the text is provided. The method comprises steps of: defining an accuracy target for disambiguation; and applying a selection of components from the plurality of disambiguation components to meet the accuracy target.

In another aspect, a method of processing natural language text utilizing disambiguation components to identify a disambiguated sense for the text is provided. The method comprises steps of: identifying a set of senses for the text; and identifying and removing an unwanted sense from the set.

In another aspect a method of processing natural language text utilizing disambiguation components to identify a disambiguated sense for the text is provided. The method comprises steps of: identifying a set of senses for the text; and identifying and removing an amount of ambiguity from the set of senses.

In another second aspect, a method of generating sense-tagged text is provided. The method comprises steps of: disambiguating a quantity of documents utilizing a disambiguation component; generating a confidence score and a probability score for a sense identified for a word provided by the component; if the confidence score for the sense for the word is below a set threshold, the sense is ignored; and if the confidence score for the sense for the word is above the set threshold, the sense is added to the sense-tagged text.

In other aspects various combinations of sets and subsets of the above aspects are provided.

BRIEF DESCRIPTION OF THE DRAWINGS

The foregoing and other aspects of the invention will become more apparent from the following description of specific embodiments thereof and the accompanying drawings which illustrate, by way of example only, the principles of the invention. In the drawings, where like elements feature like reference numerals (and wherein individual elements bear unique alphabetical suffixes):

FIG. 1 is a schematic representation of words and word senses associated with an embodiment of a text processing system;

FIG. 2 is a schematic representation of a representative semantic relationship or words for with the system of FIG. 1;

FIG. 3 is a schematic representation of an embodiment of a text processing system providing word sense disambiguation;

FIG. 4 is a block diagram of a word sense disambiguator module, control file optimizer, and database elements of the text processing system of FIG. 3.

FIG. 5 is a diagram of data structures used to represent the semantic relationships of FIG. 2 for the system of FIG. 3;

FIG. 6 is a flow diagram of a text processing process performed by the embodiment of FIG. 3;

FIG. 7 is flow diagram of a process for a disambiguating step of the text processing process of FIG. 6;

FIG. 8 is a data flow diagram for the control file optimizer of FIG. 4; and

FIG. 9 is a flow diagram of a bootstrapping process associated with the text processing system of FIG. 3.

DESCRIPTION OF EMBODIMENTS

The description which follows, and the embodiments described therein, are provided by way of illustration of an example, or examples, of particular embodiments of the principles of the present invention. These examples are provided for the purposes of explanation, and not limitation, of those principles and of the invention. In the description, which follows, like parts are marked throughout the specification and the drawings with the same respective reference numerals.

The following terms will be used in the following description, and have the meanings shown below:

Computer readable storage medium: hardware for storing instructions or data for a computer. For example, magnetic disks, magnetic tape, optically readable medium such as CD ROMs, and semi-conductor memory such as PCMCIA cards. In each case, the medium may take the form of a portable item such as a small disk, floppy diskette, cassette, or it may take the form of a relatively large or immobile item such as hard disk drive, solid state memory card, or RAM.

Information: documents, web pages, emails, image descriptions, transcripts, stored text etc. that contain searchable content of interest to users, for example, contents related to news articles, news group messages, web logs, etc.

Module: a software or hardware component that performs certain steps and/or processes; may be implemented in software running on a general-purpose processor.

Natural language: a formulation of words intended to be understood by a person rather than a machine or computer.

Network: an interconnected system of devices configured to communicate over a communication channel using particular protocols. This could be a local area network, a wide area network, the Internet, or the like operating over communication lines or through wireless transmissions.

Query: a list of keywords indicative of desired search results; may utilize Boolean operators (e.g. “AND”, “OR”); may be expressed in natural language.

Text: textual information represented in its usual form within a computer or associated storage device. Unless otherwise specified, it is assumed to be expressed in natural language.

Search engine: a hardware or software component to provide search results regarding information of interest to a user in response to text from the user. The search results may be ranked and/or sorted by relevance.

Sense-tagged text: text in which some or all of the words have been marked with a word sense or senses signifying the meaning of the word in the text.

Sense-tagged corpus: is a collection of sense-tagged text for which the senses and possibly linguistic information such as part of speech tags of some or all words have been marked. The accuracy of the specification of the senses and other linguistic information must be similar to that which would be achieved by a human lexicographer. Thus, if sense-tagged text is generated by a machine, then the accuracy of word senses that are marked by the machine must similar that of a human lexicographer performing word sense disambiguation.

The embodiment relates to natural language processing, and in particular to processing natural language text as a step in an application which requires or can be improved by making use of the meaning of the words in the text. This process is known generally as word sense disambiguation. Applications include but are not limited to:

1. Internet search and other information retrieval applications; both in disambiguating queries to better specify the user's request, and in disambiguating documents to select more relevant results. When working with large sets of data, such as a database of documents or web pages on the Internet, the volume of available data can make it difficult to find information of relevance. Various methods of searching are used in an attempt to find relevant information in such stores of information. Some of the best known systems are Internet search engines, such as Yahoo (trademark) and Google (trademark) which allow users to perform keyword-based searches. These searches typically involve matching keywords entered by the user with keywords in an index of web pages. One reason for some difficulties encountered in performing such searches is the ambiguity of words used in natural language. Specifically, difficulties are often encountered because one word can have several meanings, and each meaning can have multiple synonyms or paraphrases. For example, “Java bean” is matched by a search engine to documents which simply contain these two words. By disambiguating “Java bean” to mean “coffee bean” instead of the “Java Bean” computer technology by Sun Microsystems, a disambiguator would allow documents about this computer technology to be excluded from the results, and would similarly allow documents concerning coffee beans to be included in the results.

2. Document classification; in allowing documents to be clustered based upon precise criteria of meaning as opposed to their textual content. For example, consider an application which automatically sorted email messages into folders each pertaining to a topic specified by a user. One such folder might be entitled “programming tools”, and contain any emails that mentioned any form of “programming tool”. The use of word sense disambiguation in this application would allow emails that contained related information, but did not contain words matching the title of the folder to be accurately classified as belonging in the folder or not. For example, the words “Java object” could be placed in the folder because it contains a sense of “Java” meaning a programming language, whereas an email containing the terms “Java coffee” or “tools to use in designing a conference program” could be rejected because, in the first case, the word “Java” is disambiguated to mean a type of coffee, and, in the second case, the word “program” refers to an event, which is a meaning not associated with computer programming. Such an effect could be optionally achieved by giving the senses present in a disambiguated email to a machine learning algorithm, rather than just providing the words as is currently done by state-of-the-art applications. The accuracy of the classification would increase as a result, and the application would appear more intelligent and be more useful to the user.

3. Machine translation; in knowing the precise meanings of words before they are translated, so that the correct translation can be provided for words with multiple possible translations. For example, the word “bank” in English may translate into the French “banque” if it means “financial institution”, but “rive” if it means “river bank”. In order to perform an accurate translation of such a word, it is necessary to select a meaning. It will be recognised by those skilled in the art that a large percentage of the errors in prior art machine translation systems are made due to the selection of the wrong senses of words being translated. The addition of word sense disambiguation to such a system would improve accuracy by reducing or eliminating the errors of this type that are made by today's state-of-the-art systems.

4. Speech recognition; in allowing utterances with words or combinations of words that sound the same but are written differently to be correctly interpreted. Most speech recognition systems include a recognition component that analyses the phonetics of a phrase and outputs several possible sequences of words that could have been pronounced. For example, “I asked to people” and “I asked two people” are pronounced the same, and would both be output as possible sequences of words by such a recognition component. Most speech recognition systems then include a module which selects which of the possible word sequences is the most probable, and outputs this sequence as the result. This module typically operates by selecting the word sequence that matches most closely with word sequences that are known to be uttered. Word sense disambiguation could improve the operation of such a module by selecting the word sequence that leads to the most consistent interpretation. For example, consider a speech recognition system which generated two alternative interpretations for an utterance: “I scream in flat endings” or “Ice cream is fattening”. A word sense disambiguator would select between these two interpretations which sound the same, in exactly the same manner as it would disambiguate between two possible interpretations in text which are spelled the same,

5. Text to speech (speech synthesis), in allowing words with multiple pronunciations to be pronounced correctly. For example, “I saw her sow the seeds” and “The old sow was slaughtered for bacon” both contain the word “sow”, which is pronounced differently in each sentence. A text to speech application needs to know which interpretation applies to each word in order to correctly utter each sentence. A word sense disambiguation module could determine that the sense of “sow” in the first sentence was the verb “to sow” and in the second sentence was “a female hog”. The application would then have the information necessary to pronounce each sentence correctly.

Before describing specific aspects of the embodiment, some background on relationships between words and their word senses is provided. Referring to FIG. 1, relationship between words and word senses is shown generally by the reference 100. As seen in this example, certain words have multiple senses. Among many other possibilities, the word “bank” may represent: (i) a noun referring to a financial institution; (ii) a noun referring to a river bank; or (iii) a verb referring to an action to save money. Similarly, the word “interest” has multiple meanings including: (i) a noun representing an amount of money payable relating to an outstanding investment or loan; (ii) a noun representing special attention given to something; or (iii) a noun representing a legal right in something.

The embodiment assigns senses to words. In particular, the embodiment defines two senses of words: coarse and fine. A fine sense defines a precise meaning and usage of a word. Each fine sense applies within a particular part of speech category (noun, verb, adjective or adverb). A coarse sense defines a broad concept associated with a word, and may be associated with more than one part of speech category. Each coarse sense contains one or more fine senses, and each fine sense belongs to one coarse sense. A word can have more than one fine and more than one coarse sense. A fine sense is classified under the coarse sense because the fine sense of the word matches the generic concept associated with the coarse sense definition. Table 1 illustrates the relationship between a word, its coarse senses and its fine senses. As an example to illustrate the distinction between fine and coarse senses, the fine senses for the word “bank” respect the distinction between the verb “to bank” as in “to bank a plane” and the noun “a bank” as in “the pilot performed a bank”, whereas these two senses are grouped together under the more general coarse sense “Manoeuvre”.

TABLE 1
Word Coarse Sense Fine Senses
Bank Financial Institutions Financial institution (Noun)
Building where banking is done
(Noun)
Perform Business with a Bank
(Verb)
Ground formations Land beside water (Noun)
Ridge of earth (Noun)
Slope in road (Noun)
Manoeuvre Flight manoeuvre (Noun)
Tip laterally (Verb)
Gambling Funds held by a gambling house
(Noun)
act as a banker in gambling
(Verb)

Referring to FIG. 2, example semantic relationships between word senses are shown. These semantic relationships are precisely defined types of associations between two words based on meaning. The relationships are between word senses, which are specific meanings of words. For example, a bank (in the sense of a river bank) is a type of terrain and a bluff (in the sense of a noun meaning a land formation) is also a type of terrain. A bank (in the sense of river bank) is a type of incline (in the sense of grade of the land). A bank in the sense of a financial institution is synonymous with a “banking company” or a “banking concern.” A bank is also a type of financial institution, which is in turn a type of business. A bank (in the sense of financial institution) is related to interest (in the sense of money paid on investments) and is also related to a loan (in the sense of borrowed money) by the generally understood fact that banks pay interest on deposits and charge interest on loans.

It will be understood that there are many other types of semantic relationships that may be used. Although known in the art, following are some examples of semantic relationships between words: Words which are in synonymy are words which are synonyms to each other. A hypernym is a relationship where one word represents a whole class of specific instances. For example “transportation” is a hypernym for a class of words including “train”, “chariot”, “dogsled” and “car”, as these words provide specific instances of the class. Meanwhile, a hyponym is a relationship where one word is a member of a class of instances. From the previous list, “train” is a hyponym of the class “transportation”. A meronym is a relationship where one word is a constituent part of, the substance of, or a member of something. For example, for the relationship between “leg” and “knee”, “knee” is a meronym to “leg”, as a knee is a constituent part of a leg. Meanwhile, a holonym a relationship where one word is the whole of which a meronym names a part. From the previous example, “leg” is a holonym to “knee”. Any semantic relationships that fall into these categories may be used. In addition, any known semantic relationships that indicate specific semantic and syntactic relationships between word senses may be used.

It will be recognized that use of word sense disambiguation in a search engine addresses the problem of retrieval relevance. Furthermore, users often express text as they would express language. However, since the same meaning can be described in many different ways, users encounter difficulties when they do not express text in the same specific manner in which the relevant information was initially classified.

For example if the user is seeking information about “Java” the island, and is interested in “holidays” on Java (island), the user would not retrieve useful documents that had been categorized using the keywords “Java” and “vacation”. The embodiment addresses this issue. It has been recognized that deriving precise synonyms and sub-concepts for each key term in a naturally expressed text increases the volume of retrieved relevant retrievals. If this were performed using a thesaurus without word sense disambiguation, the result could be worsened. For example, semantically expanding the word “Java” without first establishing its precise meaning would yield a massive and unwieldy result set with results potentially selected based on word senses as diverse as “Indonesia” and “computer programming”. The embodiment provides systems and methods of interpreting meaning of each word which are semantically expanded to produce a comprehensive and simultaneously more precise result set.

Referring to FIG. 3, text processing system associated with an embodiment is shown generally at reference 10. The system takes as input a text file 12. The text file 12 contains natural language text, such as a query, a document, the output of a speech to text system, or any source of natural language text in electronic form.

The system includes text processing engine 20. The text processing engine 20 may be implemented as dedicated hardware, or as software operating on a general purpose processor. The text processing engine may also operate on a network.

The text processing engine 20 generally includes a processor 22. The engine may also be connected, either directly thereto, or indirectly over a network or other such communication means, to a display 24, an interface 26, and a computer readable storage medium 28. The processor 22 is coupled to the display 24 and to the interface 26, which may comprise user input devices such as a keyboard, mouse, or other suitable devices. If the display 24 is touch sensitive, then the display 24 itself can be employed as the interface 26. The computer readable storage medium 28 is coupled to the processor 22 for providing instructions to the processor 22 to instruct and/or configure processor 22 to perform steps or algorithms related to the operation of text processing engine 20, as further explained below. Portions or all of the computer readable storage medium 28 may be physically located outside of the text processing engine 20 to accommodate, for example, very large amounts of storage. Persons skilled in the art will appreciate that various forms of text processing engines can be used with the present invention.

Optionally, and for greater computational speed, the text processing engine 20 may include multiple processors operating in parallel or any other multi-processing arrangement. Such use of multiple processors may enable the text processing engine 20 to divide tasks among various processors. Furthermore, the multiple processors need not be physically located in the same place, but rather may be geographically separated and interconnected over a network as will be understood by those skilled in the art.

Text processing engine 20 includes a database 30 for storing a knowledge base and component linguistic resources used by the text processing engine 20. The database 30 stores the information in a structured format to allow computationally efficient storage and retrieval as will be understood by those skilled in the art. The database 30 may be updated by adding additional keyword senses or by referencing existing keyword senses to additional documents. The database 30 may be divided and stored in multiple locations for greater efficiency.

A central component of text processing engine 20 is word sense disambiguation (WSD) module 32, which processes words from an input document or text into word senses. A word sense is a given interpretation ascribed to a word, in view of the context of its usage and its neighbouring words. For example, the word “book” in the sentence “Book me a flight to New York” is ambiguous, because “book” can be a noun or a verb, each with multiple potential meanings. The result of processing of the words by the WSD module 32 is a disambiguated document or disambiguated text comprising word senses rather than ambiguous or uninterpreted words. WSD module 32 distinguishes between word senses for each word in the document or text. WSD module 32 identifies which specific meaning of the word is the intended meaning using a wide range of interlinked linguistic techniques to analyze the syntax (e.g. part of speech, grammatical relations) and semantics (e.g. logical relations) in context. It may use a knowledge base of word senses which expresses explicit semantic relationships between word senses to assist in performing the disambiguation.

Referring to FIG. 4, further detail on database 30 is provided.

To assist in disambiguating words into word senses, the embodiment utilizes knowledge base 400 of word senses capturing relationships of words as described above for FIG. 2. Knowledge base 400 is associated with database 30 and is accessed to assist WSD module 32 in performing word sense disambiguation as well as provide the inventory of possible senses of words in a text. While prior art dictionaries, and lexical databases such as WordNet (trademark), have been used in systems, knowledge base 400 provides an enhanced inventory of words, word senses, and semantic relations. For example, while prior art dictionaries contain only definitions of words for each of their word senses, knowledge base 400 also contains information on relations between word senses. These relations includes the definition of the sense and the associated part of speech (noun, verb, etc.), fine sense synonyms, antonyms, hyponyms, meronyms, pertainyms, similar adjectives relations and other relationships known in the art. Knowledge base 400 also contains additional semantic relations not contained in other prior art lexical databases: (i) additional relations between word senses, such as the grouping of fine senses into coarse senses, “instance of” relations, classification relations, and inflectional and derivational morphological relations; (ii) corrections of errors in data obtained from published sources; and (iii) additional words, word senses, and relations that are not present in other prior art knowledge bases.

In addition to containing an inventory of words and word senses (fine and coarse) for each word and concepts, as well as over 40 specific types of semantic links between them, database 30 also provides a repository for component resources 402 used by linguistic components 502 and WSD components 504. Some component resources are shared by several components while other resources are specific to a given component. In the embodiment, the component resources include: general models, domain specific models, user models and session models. General models contain general domain information, such as a probability distribution of senses for each word for any text of unknown domain. They are trained using data from several domains. WSD components 504 and linguistic components 502 utilize these resources as necessary. For example, a component may use these resources on all requests or may use it only when the request cannot be completed using more specific models. Domain-specific models are trained from domain specific information. They are useful for modelling usage of specialized meanings of words in various domains. For example, the word “Java” has different meaning for travel agents and computer programmers. These resources allow the building of statistical models for each group. User models are trained for a specific user. The models may be given and maybe learnt over time. The user models can be constructed by the application or automatically by the word sense disambiguation system. Session models provide information regarding multiple requests regrouped within a session. For example, several word sense disambiguation requests may be related to the same topic during an information retrieval session using a search engine. The session models can be constructed by the application or automatically by WSD module 32.

Database 30 also contains sense-tagged corpus 404. Sense-tagged corpus 404 may optionally be split up into sub-units used for training components, training confidence functions for components and training the control file optimizer, as described further below.

Referring to FIG. 5, further detail on knowledge base 400 is provided. In the embodiment, knowledge base 400 is a generalized graph data structure and is implemented as a table of nodes 402 and a table of edge relations 404 associating two nodes together. Each is described in turn. Annotations of arbitrary data types may be attached to each node or edge. In other embodiments, other data structures, such as linked lists, may be used to implement knowledge base 400.

In table 402, each node is an element in a row of table 402. In the embodiment, a record for each node has as many as the following fields: an ID field 406, a type field 408 and an annotation field 410. There are two types of entries in table 402: a word and a word sense definition. For example, the word “bank” in ID field 406A is identified as a word by the “word” entry in type field 408A. Also, exemplary table 402 provides several definitions of words. To catalog the definitions and to distinguish definition entries in table 402 from word entries, labels are used to identify definition entries. For example, entry in ID field 406B is labeled “LABEL001”. A corresponding definition in type field 408B identifies the label as a “fine sense” word relationship. A corresponding entry in annotation filed 410B identifies the label as “Noun. A financial institution”. As such, a “bank” can now be linked to this word sense definition. Furthermore an entry for the word “brokerage” may also be linked to this word sense definition. Alternate embodiments may use a common word with a suffix attached to it, in order to facilitate recognition of the word sense definition. For example, an alternative label could be “bank/n1”, where the “/n1” suffix identifies the label as a noun (n) and the first meaning for that noun. It will be appreciated that other label variations may be used. Other identifiers to identify adjectives, adverbs and others may be used. The entry in type field 408 identifies the type associated with the word. There are several types available for a word, including: word, fine sense and coarse sense. Other types may also be provided. In the embodiment, when an instance of a word has a fine sense, that instance also has an entry in annotation field 410 to provide further particulars on that instance of the word.

Edge/Relations table 404 contains records indicating relationships between two entries in nodes table 402. Table 404 has the following entries: From node ID column 412, to node ID column 414, type column 416 and annotation column 418. Columns 412 and 414 are used to link two entries in table 402 together. Column 416 identifies the type of relation that links the two entries. A record has the ID of the origin and the destination node, the type of the relation, and may have annotations based on the type. Types of relations include “root word to word”, “word to fine sense”, “word to coarse sense”, “coarse to fine sense”, “derivation”, “hyponym”, “category”, “pertainym”, “similar”, “has part”. Other relations may also be tracked therein. Entries in annotation column 418 provide a (numeric) key to uniquely identify an edge type going from a word node to either a coarse node or fine node for a given part-of-speech.

Referring to FIG. 4, further detail on WSD module 32 is provided. WSD module 32 comprises control file optimizer 514, iterative component sequencer (ICS) 500, linguistic components 502, and WSD components 504.

Turning first to WSD components 504 and linguistic components 502, common characteristics and features of WSD components 504 and linguistic components 502 (“components”) are now described. Results generated by a particular component are preferably rated using a probability distribution and a confidence score. The probability distribution allows a component to return a probability figure indicating the likelihood that any possible answer is correct. In the case of WSD components 504, possible answers comprise possible senses of words in the text. In the case of linguistic components 502, possible answers depend on the task being performed by the linguistic component; for example, possible answers for part-of-speech tagger 502F are the set of possible part of speech tags for each word. The confidence score provides an indication of a level of confidence of the algorithm in the probability distribution. As such, an answer having a high probability and a high confidence score indicates that the algorithm has identified a single answer as most probable and it is highly likely that the identified answer is accurate. If an answer has a high probability score and a low confidence, then although the algorithm has identified a single answer as most probable, its confidence score indicates that it may not be correct. In the case of WSD components 504, a low confidence score may indicate that the component is lacking information that it needed to disambiguate this particular word. It is important that each component have a good confidence function. A component with a low overall accuracy but a good confidence function is able to contribute to the system accuracy despite its low overall accuracy, as the confidence function will identify correctly the subset of words for which the answers supplied by the component can be trusted.

The confidence function considers internal operating features of the component and its algorithm and evaluates potential weaknesses of accuracy of the algorithm. For example, if an algorithm relies on statistical probabilities, it would tend to produce incorrect results when probabilities were calculated from very few examples. Accordingly, for that algorithm, the confidence score will use a variable containing the number of examples used by the algorithm. A confidence function may contain several variables, even hundreds of variables. The function is usually created by using the variables as input into a classification or regression algorithm (statistical, such as a generalized linear model, or based upon machine learning, such as a neural network) familiar to those skilled in the art. The data used to train the classification or regression algorithm is preferably obtained by running the WSD algorithm over a portion of sense-tagged corpus 404 that has been set aside for this purpose.

Many of the components employ statistical techniques based on machine learning concepts or other statistical techniques which will be familiar to those skilled in the art. It will be appreciated by those skilled in the art that such components require use training data, in order to construct their statistical models. For example, the priors component 504A utilizes many sense-tagged examples of each word in order to determine what is the statistically most likely sense for that particular word. In the embodiment, the training data is provided by sense-tagged corpus 404, which is known by those skilled in the art as a “training corpus”.

Further detail is now provided on features of WSD components 504. Each WSD component 504 attempts to associate the correct senses to words in text using a particular word sense disambiguation algorithm. Each WSD component 504 may run more than one time during the course of a disambiguation. The system provides semantic word data or other forms of data in database 30 that each of the algorithms needs in order to perform disambiguation. As noted earlier, each WSD component 504 has an algorithm that executes a particular type of disambiguation and generates a probability score and a confidence score with its results. The WSD components include but are not limited to: priors component 504A; example memory component 504B; n-gram component 504C; concept overlapping component 504E; heuristic word sense component 504F; frequent words component 504G; and dependency component 504H. Each component has a specialized knowledge base associated with its particular operation. Each component produces a confidence function as detailed above. Details of each component are described below. Each technique is generally known in the art, unless specific aspects are provided herein. It will also be appreciated that not all of the WSD components described in the embodiment may be necessary to accomplish accurate word sense disambiguation, but that some combination of different techniques is required.

For priors component 504A, it utilizes a priors algorithm to predict word senses by utilizing statistical data on frequency of appearances of various word senses. Specifically the algorithm assigns a probability to each word sense based on the frequency of the word sense in a sense-tagged corpus 404. These frequencies are preferably stored in the component resources 402.

For example memory component 504B, it utilizes an example memory algorithm to predict words senses for phrases (or word sequences). Preferably it attempts to predict word senses of all the words in a sequence. Phrases typically are defined as a series of consecutive words. A phrase can be two words long up to a full sentence. The algorithm accesses a list of phrases (word sequences) which provide a deemed correct sense for each word in that phrase. Preferably, the list comprises sentence fragments from sense-tagged corpus 404 that occurred multiple times where the senses for each of the fragment occurrence was identical. Preferably, when an analyzed phrase contains a word which has a sense which differs from a sense previously attributed to that word in that phrase, senses in the analyzed phrase are rejected and are not retained in the list of word sequences.

When disambiguating text, the example memory algorithm identifies whether parts of the text or text match the previously identified recurring sequences of words which have been retained in the list of word sequences. If there is a match, the module assigns the word senses of the sequence to the matching words in the text.

For n-gram component 504C, it utilizes an n-gram algorithm which operates over a fixed range of words and only attempts to predict a sense of a single word once at a time, in contrast to the example memory algorithm. The n-grams algorithm predicts word senses for a head word by matching features immediately surrounding the word in a very narrow window. Such features include: lemma, part of speech, coarse of fine word sense, and a name entity type. While the algorithm may examine n words before or following a target word, typically, n is set at two words. With n being set at 2, the algorithm utilizes a list of word pairs with a correct sense associated with each word. This list is derived from word pairs from sense-tagged corpus 404 that occurred multiple times, where the senses for each of the word pair occurrence was identical. However, when a sense of at least one word differs, such word pair senses are rejected and are not retained in the list. When disambiguating text, the algorithm matches word pairs from the text or text being processed with word pair present in the list maintained by the algorithm. A match is identified when a word pair is found and the sense of one of the two words is already present in the text or text being processed. When a match is identified, it is assigned the sense relating to the second word in the word pair being processed.

The component resource associated with the n-grams algorithm is trained over sense-tagged corpus 404, and is part of component resources 402. The n-grams component resource includes a statistical model which identifies when an n-gram has been seen sufficiently frequently to become a valid sense predictor. Several predictors from the knowledge base may by triggered by a pattern of words. These predictors may reinforce a common sense or may actually generate multiple possible senses with a given probability distribution.

For concept overlapping component 504E, it has a concept overlapping algorithm which predicts a sense for words by choosing the senses which match most closely the general topic of the text segment. In the embodiment, the topic of the text segment is defined as the set of all non-removed senses for all words in text segment, and topical similarity is assessed by comparing the topic of the text segment which is being disambiguated with the topics extracted from the sense tagged corpus 404 for each word sense, and choosing the sense of each word with the highest such similarity. One such method of comparison is the dot-product or cosine metric. There are many other techniques for making use of topic similarity to disambiguate text, as will be familiar to those skilled in the art.

For heuristic word sense component 504F, it has a heuristic word sense algorithm which predicts a sense of words using human-generated rules which may use intrinsic language properties and semantic links in the knowledge base. For example, the senses “language” in terms of“a spoken human language” and “Indonesian” are related in the knowledge base by the relation “Indonesian is a language”. A sentence containing both “language” and “Indonesian” would have the word “language” disambiguated by this component. Typically, such a relation has been manually verified, thereby providing a high confidence in accuracy.

For frequent words component 504G, it has a frequent words algorithm which identifies the senses of the most frequently occurring words. In English, the 500 most frequently occurring words account for almost a third of the words encountered in normal text. For each of these words, a large amount of training examples are available in sense-tagged corpus 404. Accordingly, it is possible to train using supervised machine learning methods specific sense predictors for each word. In the embodiment, the machine learning method used to train the component is boosting, and the features used include the words and parts of speech of the words in immediate proximity to the target word to be disambiguated. Other features and machine learning techniques may be used to accomplish the same goal, as will be familiar to those skilled in the art.

For dependency component 504H, it has a dependency algorithm which utilizes a sense prediction model based on the semantic dependencies in a sentence. By determining that a word is a head word in a dependency, and optionally the sense of the head word, it predicts the sense of its dependant words. Similarly, having determined that a word is a dependent and optionally the sense of the dependent word, it can predict the sense of the head word. For example in the text fragment “drive the car”, the head word is “drive” and the dependant is “car”. Knowledge of the sense of “car” will be sufficient to predict the sense of “drive” as “drive a vehicle”.

It will be appreciated that other techniques for word sense disambiguation become available from time to time as the scientific research in the field progresses, and that such other techniques could equally be included as new WSD components within the system. It will by appreciated that a single WSD component may not be sufficient to disambiguate text with high accuracy. To address this issue, the embodiment utilizes multiple techniques to disambiguate text. The techniques described above specify an exemplary combination which is capable of performing high accuracy word sense disambiguation. Other techniques may also be used.

Turning now to linguistic components 502, each component 502 provides a text processing function which can be applied to text to determine a certain type of linguistic information. This information is then provided to the WSD components 504 for disambiguation. The operation of each of the linguistic components 502 will be familiar to one skilled in the art. The linguistic components 502 include:

Tokenizer 502A which splits input text into individual words and symbols. Tokenizer 502A processes the input text as a sequences of characters and breaks the input text into a series of tokens, where a token is the smallest sequence of characters that can form a word.

Sentence boundary detector 502B which identifies sentence boundaries in the input text. It uses rules and data (e.g., list of abbreviations) to identify the possible sentence breaks in the input text.

Morpher 502C which identifies a lemma, i.e. a base form, of a word. In the embodiment, the lemma defines the fine sense and coarse sense inventories of the word. For example, for the inflected word “jumping” the morpher identifies its base form “jump”.

Parser 502D which identifies relationships between the words in the input text. Parser 502D identifies grammatical structures and phrases in the input text. The result of this operation is a parse tree, which is a concept very well known in the field. Some relationships include “subject of the verb” and “object of the verb”. From the phrases, a list of syntactic and semantic dependencies can later be extracted. Parser 502D also produces part of speech tags that are used to update the part of speech distribution. Parser information is also used to select possible compounds.

Dependency extractor 502J uses the parse tree to generate a list of syntactic and semantic dependencies, which will be familiar to those skilled in the art. The semantic dependencies are used by a number of other components to enhance their models. Dependencies are extracted in the following manner:

1. Parser 502D is used to generate a syntactic parse tree, including syntactic heads for each phrase.

2. Using set of heuristics, as will be familiar to those skilled in the art, semantic heads are generated for each phrase. Semantic heads differ from syntactic heads as the semantic rules give preference to semantically important elements (like nouns and verbs) while syntactic heads give preference to syntactically important elements like prepositions.

3. Once a semantic head (word or phrase) is identified, sister words and phrases are considered to form dependencies with the head.

Named-entity recogniser 502E identifies known proper nouns such as “Albert Einstein” or “International Business Machines Incorporated” and other multi-word proper nouns. Named-entity tagger 502E collects tokens that form a named entity into groups and classifies the group into categories. Such categories include: a person, location, artefact, as will be familiar to those skilled in the art. Named-entity categories are determined by a Hidden Markov Model (HMM) that is trained on parts of the sense-tagged corpus 404 in which the named entities have been marked. For example in the text fragment “Today Coca-Cola announced . . . ”, the HMM will categorize “Coca-Cola” as a company (instead of an artefact) because of analysis of the surrounding words. Many techniques exist for named entity recognition as will be familiar to those skilled in the art.

Part-of-speech tagger 502F assigns functional roles such as “noun” and “verb” to the words in the input text. Part of speech tagger 502F identifies a part of speech, which can be mapped to the broad parts of speech (noun, verb, adverb, adjective) relevant to disambiguating between word senses. Part-of-speech tagger 502F utilizes several a trigram-based Hidden Markov Model (HMM) trained on a portion of sense-tagged corpus 404 which has been annotated with part of speech information. Many techniques exist for part of speech tagging, as will be familiar to those skilled in the art.

Compound finder 502H finds possible compounds in the input text. An example of a compound is “coffee table” or “fire truck”, which although sometimes written as two words need to be treated as a single word for the purposes of word sense disambiguation. Knowledge base 400 contains a list of compounds, which can be identified in the text. Each identified compound is given a probability which marks the likelihood that the compound was correctly formed. The probability is calculated from the sense-tagged corpus 404.

Turning now to ICS 500, ICS 500 controls the sequence in which linguistic components 502 and WSD components 504 are operated on text, to continually reduce the amount of ambiguity in a text being processed. It has several specific functions:

1. It coordinates extraction of required elements from text utilizing selected linguistic components 502 and provides such elements to WSD components 504. through a common interface.

2. It seeds an initial set of sense possible for each word using seeder 500A, which associates an initial set of possible senses from the knowledge base 400 to each word in the text to identify to the WSD components 504 which senses they must disambiguate between, thus providing an initial maximum level of ambiguity.

3. It invokes WSD components 504 according to an algorithm mix identified by control file 516. Activations of the selected WSD components 504 then attempt to disambiguate the text, providing probabilities and confidence scores associated with possible senses of the words in the text. Preferably, WSD components are invoked in multiple iterations.

4. It merges and integrates output from multiple components using merging module 500B and ambiguity eliminator 500C. Merger module 500B combines the outputs of all of the WSD components 504 into a single merged probability distribution and confidence score. Ambiguity eliminator 500C which determines which sense ambiguity can be removed from the text based upon the output of merger module 500B.

More detailed description of the function and design of ICS 500 is provided in subsequent sections describing the operation of the process of word sense disambiguation.

The control file optimizer 514 optionally performs a training procedure which outputs a “recipe” in the form of control file 516, which contains optimal sequence and parameters for the WSD components 504 in each iteration, and is used by ICS 500 during word sense disambiguation. More detailed description of the function and design of control file optimizer 514 is provided in subsequent section describing the generation of an optimized control file.

Further detail is now provided on steps performed by the embodiment to process text. Referring to FIG. 6, a process to perform disambiguation of text generally by reference 600. The process may be divided into four steps. The first step is to generate an optimized control file 602. This step creates a control file which is used in the step disambiguate text 606. The second step read text 604 comprises reading in the text to be disambiguated from a file. The third step disambiguate text 606 consists of disambiguating the text, and is the main step in the process. The fourth step output disambiguated text 608 consists of writing the sense-tagged text to a file.

Referring to FIG. 7, further detail is now provided on the main processing step, disambiguate text 606.

Upon receiving a text to disambiguate, ICS 500 processes the text in the following manner:

1. ICS 500 passes the text through tokenizer 502A to identify the boundaries of the words and separate these from punctuation symbols that may be present in the text.

2. ICS 500 causes the syntactic features in the text to be identified by passing the text through linguistic components 502. Such features include: lemma (including compounds), part of speech, named entities and semantic dependencies. Each feature is generated with a confidence score and with a probability distribution.

3. Processed text is then provided to seeder 500A which uses lemma and part of speech generated by linguistic components 502 to identify a list of possible senses in the knowledge base 400 for each word in the text.

4. ICS 500 then applies a set of WSD components 504 independently to the input text, where specific WSD components 504 and a sequence of their execution are specified in control file 516. Each WSD component 504 disambiguates some or all of the words in the text. For senses that are disambiguated, a probability distribution and a confidence score are generated by each WSD component 504.

5. ICS 500 then performs a merging operation using merging module 500B. This module merges the results of all components for all words to generate a single probability distribution of senses and associated confidence score for each word. Prior to merging, if specified in the control file 516, ICS 500 may discard results with insufficiently high confidence, or for which the probability of the top result is insufficiently high. The merged probability distribution is the weighted sum of each remaining probability distribution, with the weight being provided by the confidence score. The merged confidence score is a weighted average of confidence values, with weights provided by the confidence score. For example, if a WSD component “A” had given “hot beverage” at 100% probability for the sense of the word “Java”, and WSD component “B” had given “programming language” at 100% probability for the same word, then the merged distribution would contain both “hot beverage” and “programming language” at 50% probability each. In order to merge the results of WSD components 504 that produce only coarse senses, the merger can optionally be run twice, once on the coarse senses and a second time over the group of fine senses associated with each coarse sense.

6. ICS 500 then performs ambiguity reduction using ambiguity eliminator 500C. The embodiment performs this process based upon the merged distribution and confidence output by merging module 500B. When a sense in the merged distribution has a deemed very high probability and high confidence, it is deemed to contain the correct sense and all other senses can be removed. For example, if a merged result indicated that the disambiguation for “java” was “coffee” with 98% probability and its confidence score was 90%, then all other senses would be excluded as being possible, and “coffee” would be the sole remaining sense. Control file 516 sets probability and confidence score thresholds for this decision point. Conversely, when one or more senses have a very low probability and high confidence score, such senses may be deemed to be improbable and are removed from the set of senses. Again control file 516 sets probability and confidence thresholds for this decision point. This process reduces ambiguity from the input text by utilizing information provided by WSD components 504, and accordingly influences which senses are provided to WSD components 504 during subsequent iterations of disambiguation.

7. At least one or more iterations of steps 4, 5 and 6 may optionally be performed. It will be appreciated that results of each subsequent iteration will likely be different than those of previous iteration(s), as WSD components 504 themselves do not predict senses which were eliminated after previous iterations. WSD components 504 make use of the reduced ambiguity as compared to the previous iteration to produce a result with a more accurate distribution and/or higher confidence score. Control file 516 identifies which set of WSD components 504 is applied on each iteration. It will be appreciated that several iterations may be performed until a sufficient number of words have been disambiguated or until the number of iterations specified in the control file 516 have been completed.

In the embodiment, the word sense disambiguation process may involve multiple iterations. Typically, in each iteration, only a portion of ambiguity can be removed without introducing a large number of disambiguation errors. Preferably, for each word that any selected WSD component 504 attempts to disambiguate, the selected WSD component 504 returns a full probability distribution over those senses which had not previously been removed. Generally, a WSD component 504 is not allowed to increase ambiguity of a text by re-submitting a sense for a word which has previously been discarded for that word. Also, each WSD component in an iteration operates independently from the others and interactions between WSD components 504 occur under the control of ICS 500 or via ambiguity removed in a previous iteration. In other embodiments, different degrees of interaction and knowledge of results between WSD components during an iteration and between iterations may be provided. It will be appreciated that due to the highly complex and unpredictable nature of such interactions, systems that include a high degree of interaction between WSD components 504 explicitly programmed into the WSD components 504 tend to be too complex to built practically. As such, the controlled interaction between WSD components 504 provided by the structure of the ICS and the independence of the WSD components 504 is a key advantage of the embodiment and invention.

The combined action of merger module 500B and ambiguity eliminator 500C is to post-process the results of several WSD algorithms 504 to reduce ambiguity in the text. The combined action of these modules is referred to as the post processing module 512. It will be appreciated that the use of a merging module 500B and an ambiguity reducer 500C as described in the embodiment is an exemplary technique in this particular embodiment only and that alternative techniques could be devised. For example, post processing module 512 may utilize a machine learning technique, such as a neural network, to merge and prune results. In this algorithm, the probability distributions and confidence scores of each algorithm are fed into a learning system, which generates a combined probability and confidence score for each sense.

In relation to the merger module 500B, other algorithms, such as voting algorithms and merging of rankings algorithms may be used.

Referring to FIG. 8, further details are now provided on control file optimizer process 514 used to generate an optimized control file 516 providing maximum disambiguation accuracy. The process begins with a sense tagged corpus 802. In the embodiment, this sense tagged corpus is a portion of the sense tagged corpus 404 that has been set aside for the purpose of performing control file optimizer process 514. Control file optimizer 514 uses the WSD module 606 to generate a control file 516 that optimizes accuracy of the WSD module over the sense tagged corpus.

Control file optimizer 514 requires that optimization criteria are specified. Thresholds are specified separately for either the percentage of ambiguity to be removed, or the percentage accuracy of disambiguation; the control file optimizer then optimizes the control file to maximize the performance of word sense disambiguator on one measure given the threshold for the other. It is also possible to specify a maximum number of iterations. The number of correct results or the amount of ambiguity removed given are then maximized for each iteration. After the optimal combination of algorithms and thresholds for a given accuracy have been determined, the training proceeds to the next iteration. The target accuracy is lowered at each iteration, which allows the standard of results to drop gradually as the number of iterations increases. Multiple sequences of target accuracy are tested and the sequence producing the best results over the sense tagged corpus 802 is selected. Preferentially, accuracy or remaining ambiguity is progressively reduced on each subsequent iteration. Example iteration accuracy sequences that are tested are:

1. 95%−>90%−>85%−>80%

2. 90%−>80%

For a given iteration and target disambiguation accuracy, the optimal list of algorithms to invoke and the associated probability and confidence thresholds of results to keep is identified by executing the following steps:

1. Invoke each WSD component 504 individually on sense-tagged corpus 802 to obtain a set of results for each component.

2. For a set of results of a WSD component 504, search space of probability and confidence threshold to identify thresholds which maximize performance against the optimization criteria. This is done through a search of all combinations of probability and confidence thresholds in the range of 0% to 100% in fixed step increments, such as 5%.

3. Once optimal thresholds for each WSD component 504 are identified, results of all WSD components 504 are pruned according to those thresholds and are merged using the merging module 500B as described earlier.

4. Consolidated merged results are then searched to identify probability and confidence thresholds of merged results that optimize a number of correct answers with an accuracy equal to or above the target accuracy for the iteration. This is preferably performed using the method of step 2.

5. Step 4 is repeated for WSD component 504 that was merged but the results of the WSD component 504 of interest are excluded. The probability and confidence thresholds to maximize the number of correct results of this result set are them identified. The difference between the maximum number of correct results of this set compared to the number obtained in step 4 indicates a contribution of correct unique answers of the algorithm of interest. If the contribution of a WSD component 504 is negative, it identifies that this WSD component 504 as having a detrimental impact on the results. If the contribution is zero, then it identifies that the WSD component 504 is not contributing new correct results in the iteration. In either case, the WSD component 504 having the lowest negative contribution is removed from the list of WSD components 504 to be invoked in subsequent iterations.

6. Step 5 is repeated until a set number WSD components 504 that have a negative or zero contribution are identified and removed. The number may be all WSD components 504.

7. Steps 2 through 6 are repeated but with the target accuracy for of step 2 modified by a small increment, e.g. 2.5% both above and then below the target accuracy of the iteration.

8. The combination of WSD components 504 and the associated probability and confidence thresholds that resulted in the largest number of correct answers are retained as the solution to a given iteration. The thresholds for probability and confidence for each WSD algorithm 504 and the ambiguity reducer 500C are written to the control file, and the training proceeds to the next iteration and target disambiguation accuracy.

The control file optimizer 514, can be set to optimize accuracy given that each word is assigned one and only one sense, the above description implies. It will be recognized that for certain applications or in certain specific instances, it may not make sense to attempt to assign only one sense to each word, or to disambiguate all the words.

The amount of ambiguity present in text prior to any disambiguation may be considered to be the maximum ambiguity. The amount of ambiguity present in fully sense-tagged text, for which each word has been assigned one and only one word sense can be considered to be the minimum ambiguity. It will be recognized that for some applications or in certain cases it will be useful to remove only part of the ambiguity present in the text. This can be accomplished by allowing a word to have more than one possible sense, or by not disambiguating certain words, or both of these. In the embodiment, the percentage of ambiguity removed is defined as the (number of senses discarded), divided by the (total number of possible senses minus one). It will further be recognized that, in general, removing a smaller percentage of ambiguity permits word sense disambiguator 32 to return a more accurate results, given that word sense disambiguator 32 can specify more than one possible sense for a word, and where a word is considered correctly disambiguated if senses specified for the word include the correct sense of the word.

Optionally, the control file optimizer 514 can be provided with separate optimization criteria and thresholds for the percentage of ambiguity to be removed by the word sense disambiguator 32 and the accuracy of the disambiguation results of word sense disambiguator 32. The control file optimizer 514 can be asked to either a) maximize the amount of ambiguity removed subject to a minimum threshold of accuracy (for example, remove as much ambiguity as possible, ensuring that the remaining possible senses for the words are 95% likely to contain the correct sense), or b) to maximize disambiguation accuracy subject to a minimum percentage of ambiguity to remove (for example, maximize accuracy subject to removing at least 70% of additional senses for each word). This capability is useful in applications a) because it allows word sense disambiguator 32 to better fit the real world of natural language texts, in which words may be truly ambiguous (i.e. ambiguous to a human) as expressed in a text, and therefore not possible to fully disambiguate, and b) because it allows applications making use of word sense disambiguator 32 to opt for more or less conservative implementations of word sense disambiguator 32, wherein the precision of the disambiguation is lower, but fewer correct senses are discarded. This is particularly valuable, for example in information retrieval applications for which it is critical that correct information is never discarded (e.g. due to incorrect disambiguation), even at the expense of including extraneous information (e.g. due to additional incorrect senses being present in the disambiguated text).

Optionally, the control file optimizer 514 can be provided with a maximum number of iterations.

It will be appreciated that creating accurate confidence functions is important. A component with a poor confidence function, even a component with high accuracy, will not contribute or will contribute less than optimally to the system accuracy. This occurs in one of two ways:

  • 1. If the confidence function tends to frequently give a low confidence value to a correct result, then merger 500A will effectively ignore this result, due to the arithmetic of the merger whereby results are weighted by the confidence score, with the net effect being as if the component had not given a result at all for that word. Thus, these correct results will be excluded from contributing to the system due to the poor confidence function.
  • 2. On the other hand, if the confidence function gives a high confidence value to incorrect results, then the automatic training procedure will recognize that the algorithm contributes many incorrect results, and exclude it from being run.

It will be appreciated that adding an algorithm with a poor confidence function to the system (for example, one which is overly optimistic and often produces incorrect results with 100% confidence) does not severely detrimentally affect the accuracy of the system, as the control file optimization procedure 514 described above will discounts such results and it will not execute that algorithm in further iterations of disambiguation. This provides a level of robustness to the system against the inclusion of poor WSD components.

It will be apparent to those skilled in the art that the accuracy of most WSD systems increases with the size of the training corpus but decreases with an inaccurately tagged training corpus. The addition of accurately sense-tagged text to the training corpus will usually increase the effectiveness of WSD components. In addition, most WSD components 504 require a portion of the sense-tagged corpus 404 to be set aside for the training of their confidence function. It will be appreciated that the effectiveness of the confidence function increases as the amount of sense-tagged text in the portion of the sense-tagged corpus 404 set aside for confidence function training increases.

Sense-tagged corpus 404 can be created manually by human lexicographers. It will be appreciated that this is a time consuming and expensive process, and that finding a way to generate or augment sense-tagged corpus 404 automatically would be of substantial value.

Referring to FIG. 9, the embodiment also provides a system and method for automatically providing a sense-tagged corpus 404 or for automatically increasing the size of sense-tagged corpus 404 for the training of WSD components 504. There are two processes illustrated in FIG. 9. The first is the component training process 960. This process uses sense tagged text 404 or untagged text 900 as an input to the WSD component training module 906 in order to generate improved component resources for the WSD components 504. The second process is the corpus generation process 950. This process processes untagged text 900 or partially tagged text 902 through the WSD module 32. Using the confidence function and probability distributions output by the WSD process 32, senses which are likely to be incorrectly tagged are then filtered out by the filter module 904. This partially sense tagged text can then be added to the partially tagged text 902 or the sense tagged corpus 404. When these two processes component training process 960 and corpus generation process 950 are run alternatively, the effect is to improve the accuracy of the WSD module 32 and to increase the size of the sense-tagged corpus 404.

As described above, it will recognized that most conceivable WSD components 504 require a training process to be performed over a sense tagged corpus 404 before they can be used to disambiguate text. For example, priors component 504A requires that the frequencies of senses be recorded from a sense tagged corpus 404. These frequencies are stored in the WSD component resources 402. As described above, the more sense tagged text 404 is available to the training process, the more accurate each WSD algorithm 504 will be. The collection of the training processes of all WSD components 504 is collectively referred to in FIG. 9 as the WSD component training process 960.

As described above, results of several WSD components 504 are combined to disambiguate previously unseen text. This is a process known as “bootstrapping”.

With the embodiment, only results with sufficiently high confidence are added to the training data, utilizing the following algorithm:

1. Train each model of each word sense disambiguation using the component training process 960 using available training data from the sense tagged corpus 404.

2. Disambiguate a large quantity of untagged documents 900 using the WSD module 32; preferably a very large quantity of documents are used from various domains.

3. In the filter module 904, discard all results where the result is ambiguous or where the confidence is below a threshold, which may be adjusted.

4. Add the non-discarded senses to the sense tagged data 404.

5. Re-train the set of word sense disambiguation components using the component training process 960.

6. Restart the training over the same documents which are now in the sense tagged corpus 404 or over a new body of untagged text 900.

A key to this process is the use of a probability distribution and confidence score. In prior art systems, a confidence score is not available and inaccurate results cannot be discarded. As a result, the WSD components 504 are less accurate after retraining on the enlarged sense tagged corpus 404 than they were before, and such a process is not practically useful. By setting a high confidence threshold that rejects most incorrect senses from being added to the sense tagged corpus 404, the embodiment eliminates this deficiency in the prior art system and allows the training data to be enlarged with high quality tagged text. It will be appreciated that this process can run multiple times, and may create a self-reinforcing loop that increases both the size of the sense tagged corpus 404 and the accuracy of the WSD system 32. The quality of the training data extracted (due to the use of a probability distribution and a confidence score) and the potentially self-reinforcing nature of the bootstrapping process are features of the embodiment.

The embodiment also provides a variant of the above bootstrapping process to train the system for a specific domain (e.g., law, health, etc.), utilizing the following variation on the algorithm:

1. A number of documents are disambiguated by a highly accurate method, such as manually by a skilled human. Use of these documents provides “seeding resources” to the system, which are added to the sense tagged corpus 404.

2. The word sense disambiguation components are trained using the WSD component training process 960.

3. A large quantity of documents from the domain are automatically disambiguated and added to the sense tagged corpus 404 using the corpus tagging process 950.

It will be apparent that the embodiment has several advantages over the prior art. Some include:

1. Multiple independent algorithms. The embodiment allows more components to be incorporated utilizing a simplified interface through ICS 500. As such, several disambiguation techniques (for example between 10 and 20) without the system becoming too complex to manipulate.

2. Confidence functions. In prior art systems, a confidence score is not available. The confidence score provides several critical advantages in prior art systems:

  • a) Merging together of results of multiple components. The confidence function allows results from different probabilistic algorithms to be combined with different weights reflecting the expected accuracy of the algorithm in a particular situation. Using the confidence function invention above, the system can merge together decisions of many components to obtain a more likely sense.
  • b) Discarding poor results or word senses for truly ambiguous words. It allows potentially inaccurate results to be discarded, such embodiment can opt not to provide senses for words for which it has little confidence in its answer. This reflects better the real world of natural language expression, wherein some expressions remain ambiguous even when analyzed by a human.
  • c) Bootstrapping. The confidence function provides a likelihood that each answer is correct. This allows only highly accurate results to be kept and reused as training text for components and the overall system. Additional training text in turn further improves the accuracy of the components and the overall system. This is a highly accurate form of bootstrapping, and offers a comparable gain in performance to sense-tagging additional training text using human lexicographers, at a tiny fraction of the cost. The amount of sense-tagged text that can be generated from untagged text (for example, the Internet) with this technique is limited only by available computer capacity Prior art systems have performed bootstrapping without a confidence score, but the sense tags in the text fed to the system are far less accurate than those provided by a human lexicographer or a confidence-score enabled system, and the overall performance of the system quickly stagnates or degrades.

3. Iterative disambiguation. The system allows a component to have multiple passes over the text being disambiguated, which allows it to use high-accuracy disambiguations (or reductions in ambiguity) provided by any of the other components, to improve its accuracy in disambiguating the remaining words. For example, when faced with the words “cup” and “green” in one sentence, a particular WSD component 504 may not be able to distinguish between a “cup” sense for “golf” and the more mundane “drinking vessel”. If another WSD component 504 is able to disambiguate the word “green” into its “golf green” sense, then the first WSD component 504 may now be able to correctly disambiguate “golf” into “golf cup”. In this sense, WSD components 504 interact with each other to arrive at more likely senses.

4. Method for automatically tuning WSD module 32. WSD module 32 includes a method for merging an optimal “recipe” of components and parameter values. This merged set is optimal in the sense that it provides the parameters which utilise multiple iterations of multiple components to obtain the maximum possible accuracy.

5. Multiple levels of ambiguity. By operating simultaneously on coarse and fine senses, the embodiment can integrate different components effectively. For example, several classes of linguistic components operate by attempting to discern a topical content of text. These types of components tend to have poor accuracy over fine senses, since these often respect grammatical rather than semantic distinctions, but do very well over coarse senses. The WSD module 32 is capable of merging results between components that give fine and coarse senses, allowing each component to operate over the sense granularity most appropriate for that component. Furthermore, an application that requires only coarse senses can obtain these from WSD module 32. Due to their coarseness, these coarse senses will have higher accuracy than the fine senses.

6. Use of domain-specific data. If information about the problem domain is known, the embodiment can be biased to favour senses which match the problem domain. For example, if it is known that a particular document falls within the domain of Law, then WSD module 32 can provide sense distributions to the components which favour those terms in the legal domain.

7. Gradual reduction in ambiguity. It will be appreciated that prior art systems perform disambiguation by attempting to choose one single sense for each word in a single iteration, which amounts to removing all ambiguity at once. This decreases the accuracy of the disambiguation. The embodiment instead performs this process gradually, removing some of the ambiguity at each iteration.

Optionally, the embodiment uses metadata. For example, the title of the document can be used to aid in the disambiguation of the document's text, by allowing the words in the title to carry disproportionate weight towards the disambiguation.

Although the invention has been described with reference to certain specific embodiments, various modifications thereof will be apparent to those skilled in the art without departing from the scope of the invention as outlined in the claims appended hereto. A person skilled in the art would have sufficient knowledge of at least one or more of the following disciplines: computer programming, machine learning and computational linguistics.

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Classifications
U.S. Classification704/9
International ClassificationG06F7/00, G06F17/30, G06F17/27
Cooperative ClassificationY10S707/99933, Y10S707/99935, Y10S707/99934, G06F17/2795, G06F17/30864, G06F17/2785
European ClassificationG06F17/27S, G06F17/30W1, G06F17/27T
Legal Events
DateCodeEventDescription
May 11, 2006ASAssignment
Owner name: IDILIA INC., CANADA
Free format text: ASSIGNMENT OF ASSIGNORS INTEREST;ASSIGNORS:COLLEDGE, MR. MATTHEW;BELZILE, MR. PIERRE;BARNES, MR. JEREMY;REEL/FRAME:017604/0949
Effective date: 20041209