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Word-sense disambiguation

Word-sense disambiguation (WSD) is the process of identifying which sense of a word is meant in a sentence or other segment of context. In human language processing and cognition, it is usually subconscious/automatic but can often come to conscious attention when ambiguity impairs clarity of communication, given the pervasive polysemy in natural language. In computational linguistics, it is an open problem that affects other computer-related writing, such as discourse, improving relevance of search engines, anaphora resolution, coherence, and inference.

Given that natural language requires reflection of neurological reality, as shaped by the abilities provided by the brain's neural networks, computer science has had a long-term challenge in developing the ability in computers to do natural language processing and machine learning.

Many techniques have been researched, including dictionary-based methods that use the knowledge encoded in lexical resources, supervised machine learning methods in which a classifier is trained for each distinct word on a corpus of manually sense-annotated examples, and completely unsupervised methods that cluster occurrences of words, thereby inducing word senses. Among these, supervised learning approaches have been the most successful algorithms to date.

Accuracy of current algorithms is difficult to state without a host of caveats. In English, accuracy at the coarse-grained (homograph) level is routinely above 90% (as of 2009), with some methods on particular homographs achieving over 96%. On finer-grained sense distinctions, top accuracies from 59.1% to 69.0% have been reported in evaluation exercises (SemEval-2007, Senseval-2), where the baseline accuracy of the simplest possible algorithm of always choosing the most frequent sense was 51.4% and 57%, respectively.

Variants

Disambiguation requires two strict inputs: a dictionary to specify the senses which are to be disambiguated and a corpus of language data to be disambiguated (in some methods, a training corpus of language examples is also required). WSD task has two variants: "lexical sample" (disambiguating the occurrences of a small sample of target words which were previously selected) and "all words" task (disambiguation of all the words in a running text). "All words" task is generally considered a more realistic form of evaluation, but the corpus is more expensive to produce because human annotators have to read the definitions for each word in the sequence every time they need to make a tagging judgement, rather than once for a block of instances for the same target word.

History

WSD was first formulated as a distinct computational task during the early days of machine translation in the 1940s, making it one of the oldest problems in computational linguistics. Warren Weaver first introduced the problem in a computational context in his 1949 memorandum on translation.[1] Later, Bar-Hillel (1960) argued[2] that WSD could not be solved by "electronic computer" because of the need in general to model all world knowledge.

In the 1970s, WSD was a subtask of semantic interpretation systems developed within the field of artificial intelligence, starting with Wilks' preference semantics. However, since WSD systems were at the time largely rule-based and hand-coded they were prone to a knowledge acquisition bottleneck.

By the 1980s large-scale lexical resources, such as the Oxford Advanced Learner's Dictionary of Current English (OALD), became available: hand-coding was replaced with knowledge automatically extracted from these resources, but disambiguation was still knowledge-based or dictionary-based.

In the 1990s, the statistical revolution advanced computational linguistics, and WSD became a paradigm problem on which to apply supervised machine learning techniques.

The 2000s saw supervised techniques reach a plateau in accuracy, and so attention has shifted to coarser-grained senses, domain adaptation, semi-supervised and unsupervised corpus-based systems, combinations of different methods, and the return of knowledge-based systems via graph-based methods. Still, supervised systems continue to perform best.

Difficulties

Differences between dictionaries

One problem with word sense disambiguation is deciding what the senses are, as different dictionaries and thesauruses will provide different divisions of words into senses. Some researchers have suggested choosing a particular dictionary, and using its set of senses to deal with this issue. Generally, however, research results using broad distinctions in senses have been much better than those using narrow ones.[3][4] Most researchers continue to work on fine-grained WSD.

Most research in the field of WSD is performed by using WordNet as a reference sense inventory for English. WordNet is a computational lexicon that encodes concepts as synonym sets (e.g. the concept of car is encoded as { car, auto, automobile, machine, motorcar }). Other resources used for disambiguation purposes include Roget's Thesaurus[5] and Wikipedia.[6] More recently, BabelNet, a multilingual encyclopedic dictionary, has been used for multilingual WSD.[7]

Part-of-speech tagging

In any real test, part-of-speech tagging and sense tagging have proven to be very closely related, with each potentially imposing constraints upon the other. The question whether these tasks should be kept together or decoupled is still not unanimously resolved, but recently scientists incline to test these things separately (e.g. in the Senseval/SemEval competitions parts of speech are provided as input for the text to disambiguate).

Both WSD and part-of-speech tagging involve disambiguating or tagging with words. However, algorithms used for one do not tend to work well for the other, mainly because the part of speech of a word is primarily determined by the immediately adjacent one to three words, whereas the sense of a word may be determined by words further away. The success rate for part-of-speech tagging algorithms is at present much higher than that for WSD, state-of-the art being around 96%[8] accuracy or better, as compared to less than 75%[citation needed] accuracy in word sense disambiguation with supervised learning. These figures are typical for English, and may be very different from those for other languages.

Inter-judge variance

Another problem is inter-judge variance. WSD systems are normally tested by having their results on a task compared against those of a human. However, while it is relatively easy to assign parts of speech to text, training people to tag senses has been proven to be far more difficult.[9] While users can memorize all of the possible parts of speech a word can take, it is often impossible for individuals to memorize all of the senses a word can take. Moreover, humans do not agree on the task at hand – give a list of senses and sentences, and humans will not always agree on which word belongs in which sense.[10]

As human performance serves as the standard, it is an upper bound for computer performance. Human performance, however, is much better on coarse-grained than fine-grained distinctions, so this again is why research on coarse-grained distinctions[11][12] has been put to test in recent WSD evaluation exercises.[3][4]

Pragmatics

Some AI researchers like Douglas Lenat argue that one cannot parse meanings from words without some form of common sense ontology. This linguistic issue is called pragmatics. As agreed by researchers, to properly identify senses of words one must know common sense facts.[13] Moreover, sometimes the common sense is needed to disambiguate such words like pronouns in case of having anaphoras or cataphoras in the text.

Sense inventory and algorithms' task-dependency

A task-independent sense inventory is not a coherent concept:[14] each task requires its own division of word meaning into senses relevant to the task. Additionally, completely different algorithms might be required by different applications. In machine translation, the problem takes the form of target word selection. The "senses" are words in the target language, which often correspond to significant meaning distinctions in the source language ("bank" could translate to the French "banque"—that is, 'financial bank' or "rive"—that is, 'edge of river'). In information retrieval, a sense inventory is not necessarily required, because it is enough to know that a word is used in the same sense in the query and a retrieved document; what sense that is, is unimportant.

Discreteness of senses

Finally, the very notion of "word sense" is slippery and controversial. Most people can agree in distinctions at the coarse-grained homograph level (e.g., pen as writing instrument or enclosure), but go down one level to fine-grained polysemy, and disagreements arise. For example, in Senseval-2, which used fine-grained sense distinctions, human annotators agreed in only 85% of word occurrences.[15] Word meaning is in principle infinitely variable and context-sensitive. It does not divide up easily into distinct or discrete sub-meanings.[16] Lexicographers frequently discover in corpora loose and overlapping word meanings, and standard or conventional meanings extended, modulated, and exploited in a bewildering variety of ways. The art of lexicography is to generalize from the corpus to definitions that evoke and explain the full range of meaning of a word, making it seem like words are well-behaved semantically. However, it is not at all clear if these same meaning distinctions are applicable in computational applications, as the decisions of lexicographers are usually driven by other considerations. In 2009, a task – named lexical substitution – was proposed as a possible solution to the sense discreteness problem.[17] The task consists of providing a substitute for a word in context that preserves the meaning of the original word (potentially, substitutes can be chosen from the full lexicon of the target language, thus overcoming discreteness).

Approaches and methods

There are two main approaches to WSD – deep approaches and shallow approaches.

Deep approaches presume access to a comprehensive body of world knowledge. These approaches are generally not considered to be very successful in practice, mainly because such a body of knowledge does not exist in a computer-readable format, outside very limited domains.[18] Additionally due to the long tradition in computational linguistics, of trying such approaches in terms of coded knowledge and in some cases, it can be hard to distinguish between knowledge involved in linguistic or world knowledge. The first attempt was that by Margaret Masterman and her colleagues, at the Cambridge Language Research Unit in England, in the 1950s. This attempt used as data a punched-card version of Roget's Thesaurus and its numbered "heads", as an indicator of topics and looked for repetitions in text, using a set intersection algorithm. It was not very successful,[19] but had strong relationships to later work, especially Yarowsky's machine learning optimisation of a thesaurus method in the 1990s.

Shallow approaches don't try to understand the text, but instead consider the surrounding words. These rules can be automatically derived by the computer, using a training corpus of words tagged with their word senses. This approach, while theoretically not as powerful as deep approaches, gives superior results in practice, due to the computer's limited world knowledge.

There are four conventional approaches to WSD:

Almost all these approaches work by defining a window of n content words around each word to be disambiguated in the corpus, and statistically analyzing those n surrounding words. Two shallow approaches used to train and then disambiguate are Naïve Bayes classifiers and decision trees. In recent research, kernel-based methods such as support vector machines have shown superior performance in supervised learning. Graph-based approaches have also gained much attention from the research community, and currently achieve performance close to the state of the art.

Dictionary- and knowledge-based methods

The Lesk algorithm[20] is the seminal dictionary-based method. It is based on the hypothesis that words used together in text are related to each other and that the relation can be observed in the definitions of the words and their senses. Two (or more) words are disambiguated by finding the pair of dictionary senses with the greatest word overlap in their dictionary definitions. For example, when disambiguating the words in "pine cone", the definitions of the appropriate senses both include the words evergreen and tree (at least in one dictionary). A similar approach[21] searches for the shortest path between two words: the second word is iteratively searched among the definitions of every semantic variant of the first word, then among the definitions of every semantic variant of each word in the previous definitions and so on. Finally, the first word is disambiguated by selecting the semantic variant which minimizes the distance from the first to the second word.

An alternative to the use of the definitions is to consider general word-sense relatedness and to compute the semantic similarity of each pair of word senses based on a given lexical knowledge base such as WordNet. Graph-based methods reminiscent of spreading activation research of the early days of AI research have been applied with some success. More complex graph-based approaches have been shown to perform almost as well as supervised methods[22] or even outperforming them on specific domains.[3][23] Recently, it has been reported that simple graph connectivity measures, such as degree, perform state-of-the-art WSD in the presence of a sufficiently rich lexical knowledge base.[24] Also, automatically transferring knowledge in the form of semantic relations from Wikipedia to WordNet has been shown to boost simple knowledge-based methods, enabling them to rival the best supervised systems and even outperform them in a domain-specific setting.[25]

The use of selectional preferences (or selectional restrictions) is also useful, for example, knowing that one typically cooks food, one can disambiguate the word bass in "I am cooking basses" (i.e., it's not a musical instrument).

Supervised methods

Supervised methods are based on the assumption that the context can provide enough evidence on its own to disambiguate words (hence, common sense and reasoning are deemed unnecessary). Probably every machine learning algorithm going has been applied to WSD, including associated techniques such as feature selection, parameter optimization, and ensemble learning. Support Vector Machines and memory-based learning have been shown to be the most successful approaches, to date, probably because they can cope with the high-dimensionality of the feature space. However, these supervised methods are subject to a new knowledge acquisition bottleneck since they rely on substantial amounts of manually sense-tagged corpora for training, which are laborious and expensive to create.

Semi-supervised methods

Because of the lack of training data, many word sense disambiguation algorithms use semi-supervised learning, which allows both labeled and unlabeled data. The Yarowsky algorithm was an early example of such an algorithm.[26] It uses the ‘One sense per collocation’ and the ‘One sense per discourse’ properties of human languages for word sense disambiguation. From observation, words tend to exhibit only one sense in most given discourse and in a given collocation.[27]

The bootstrapping approach starts from a small amount of seed data for each word: either manually tagged training examples or a small number of surefire decision rules (e.g., 'play' in the context of 'bass' almost always indicates the musical instrument). The seeds are used to train an initial classifier, using any supervised method. This classifier is then used on the untagged portion of the corpus to extract a larger training set, in which only the most confident classifications are included. The process repeats, each new classifier being trained on a successively larger training corpus, until the whole corpus is consumed, or until a given maximum number of iterations is reached.

Other semi-supervised techniques use large quantities of untagged corpora to provide co-occurrence information that supplements the tagged corpora. These techniques have the potential to help in the adaptation of supervised models to different domains.

Also, an ambiguous word in one language is often translated into different words in a second language depending on the sense of the word. Word-aligned bilingual corpora have been used to infer cross-lingual sense distinctions, a kind of semi-supervised system.[citation needed]

Unsupervised methods

Unsupervised learning is the greatest challenge for WSD researchers. The underlying assumption is that similar senses occur in similar contexts, and thus senses can be induced from text by clustering word occurrences using some measure of similarity of context,[28] a task referred to as word sense induction or discrimination. Then, new occurrences of the word can be classified into the closest induced clusters/senses. Performance has been lower than for the other methods described above, but comparisons are difficult since senses induced must be mapped to a known dictionary of word senses. If a mapping to a set of dictionary senses is not desired, cluster-based evaluations (including measures of entropy and purity) can be performed. Alternatively, word sense induction methods can be tested and compared within an application. For instance, it has been shown that word sense induction improves Web search result clustering by increasing the quality of result clusters and the degree diversification of result lists.[29][30] It is hoped that unsupervised learning will overcome the knowledge acquisition bottleneck because they are not dependent on manual effort.

Representing words considering their context through fixed size dense vectors (word embeddings) has become one of the most fundamental blocks in several NLP systems.[31][32][33] Even though most of traditional word embedding techniques conflate words with multiple meanings into a single vector representation, they still can be used to improve WSD.[34] In addition to word embeddings techniques, lexical databases (e.g., WordNet, ConceptNet, BabelNet) can also assist unsupervised systems in mapping words and their senses as dictionaries. Some techniques that combine lexical databases and word embeddings are presented in AutoExtend[35][36] and Most Suitable Sense Annotation (MSSA).[37] In AutoExtend,[36] they present a method that decouples an object input representation into its properties, such as words and their word senses. AutoExtend uses a graph structure to map words (e.g. text) and non-word (e.g. synsets in WordNet) objects as nodes and the relationship between nodes as edges. The relations (edges) in AutoExtend can either express the addition or similarity between its nodes. The former captures the intuition behind the offset calculus,[31] while the latter defines the similarity between two nodes. In MSSA,[37] an unsupervised disambiguation system uses the similarity between word senses in a fixed context window to select the most suitable word sense using a pre-trained word embedding model and WordNet. For each context window, MSSA calculates the centroid of each word sense definition by averaging the word vectors of its words in WordNet's glosses (i.e., short defining gloss and one or more usage example) using a pre-trained word embeddings model. These centroids are later used to select the word sense with the highest similarity of a target word to its immediately adjacent neighbors (i.e., predecessor and successor words). After all words are annotated and disambiguated, they can be used as a training corpus in any standard word embedding technique. In its improved version, MSSA can make use of word sense embeddings to repeat its disambiguation process iteratively.

Other approaches

Other approaches may vary differently in their methods:

Other languages

  • Hindi : Lack of lexical resources in Hindi have hindered the performance of supervised models of WSD, while the unsupervised models suffer due to extensive morphology. A possible solution to this problem is the design of a WSD model by means of parallel corpora.[46][47] The creation of the Hindi WordNet has paved way for several Supervised methods which have been proven to produce a higher accuracy in disambiguating nouns.[48]

Local impediments and summary

The knowledge acquisition bottleneck is perhaps the major impediment to solving the WSD problem. Unsupervised methods rely on knowledge about word senses, which is only sparsely formulated in dictionaries and lexical databases. Supervised methods depend crucially on the existence of manually annotated examples for every word sense, a requisite that can so far[when?] be met only for a handful of words for testing purposes, as it is done in the Senseval exercises.

One of the most promising trends in WSD research is using the largest corpus ever accessible, the World Wide Web, to acquire lexical information automatically.[49] WSD has been traditionally understood as an intermediate language engineering technology which could improve applications such as information retrieval (IR). In this case, however, the reverse is also true: web search engines implement simple and robust IR techniques that can successfully mine the Web for information to use in WSD. The historic lack of training data has provoked the appearance of some new algorithms and techniques, as described in Automatic acquisition of sense-tagged corpora.

External knowledge sources

Knowledge is a fundamental component of WSD. Knowledge sources provide data which are essential to associate senses with words. They can vary from corpora of texts, either unlabeled or annotated with word senses, to machine-readable dictionaries, thesauri, glossaries, ontologies, etc. They can be[50][51] classified as follows:

Structured:

  1. Machine-readable dictionaries (MRDs)
  2. Ontologies
  3. Thesauri

Unstructured:

  1. Collocation resources
  2. Other resources (such as word frequency lists, stoplists, domain labels,[52] etc.)
  3. Corpora: raw corpora and sense-annotated corpora

Evaluation

Comparing and evaluating different WSD systems is extremely difficult, because of the different test sets, sense inventories, and knowledge resources adopted. Before the organization of specific evaluation campaigns most systems were assessed on in-house, often small-scale, data sets. In order to test one's algorithm, developers should spend their time to annotate all word occurrences. And comparing methods even on the same corpus is not eligible if there is different sense inventories.

In order to define common evaluation datasets and procedures, public evaluation campaigns have been organized. Senseval (now renamed SemEval) is an international word sense disambiguation competition, held every three years since 1998: Senseval-1 (1998), (2001), Senseval-3 (2004), and its successor, (2007). The objective of the competition is to organize different lectures, preparing and hand-annotating corpus for testing systems, perform a comparative evaluation of WSD systems in several kinds of tasks, including all-words and lexical sample WSD for different languages, and, more recently, new tasks such as semantic role labeling, gloss WSD, lexical substitution, etc. The systems submitted for evaluation to these competitions usually integrate different techniques and often combine supervised and knowledge-based methods (especially for avoiding bad performance in lack of training examples).

In recent years 2007-2012, the WSD evaluation task choices had grown and the criterion for evaluating WSD has changed drastically depending on the variant of the WSD evaluation task. Below enumerates the variety of WSD tasks:

Task design choices

As technology evolves, the Word Sense Disambiguation (WSD) tasks grows in different flavors towards various research directions and for more languages:

  • Classic monolingual WSD evaluation tasks use WordNet as the sense inventory and are largely based on supervised/semi-supervised classification with the manually sense annotated corpora:[53]
    • Classic English WSD uses the Princeton WordNet as it sense inventory and the primary classification input is normally based on the SemCor corpus.
    • Classical WSD for other languages uses their respective WordNet as sense inventories and sense annotated corpora tagged in their respective languages. Often researchers will also tapped on the SemCor corpus and aligned bitexts with English as its source language
  • Cross-lingual WSD evaluation task is also focused on WSD across 2 or more languages simultaneously. Unlike the Multilingual WSD tasks, instead of providing manually sense-annotated examples for each sense of a polysemous noun, the sense inventory is built up on the basis of parallel corpora, e.g. Europarl corpus.[54]
  • Multilingual WSD evaluation tasks focused on WSD across 2 or more languages simultaneously, using their respective WordNets as its sense inventories or BabelNet as multilingual sense inventory.[55] It evolved from the Translation WSD evaluation tasks that took place in Senseval-2. A popular approach is to carry out monolingual WSD and then map the source language senses into the corresponding target word translations.[56]
  • Word Sense Induction and Disambiguation task is a combined task evaluation where the sense inventory is first induced from a fixed training set data, consisting of polysemous words and the sentence that they occurred in, then WSD is performed on a different testing data set.[57]

Software

  • Babelfy,[58] a unified state-of-the-art system for multilingual Word Sense Disambiguation and Entity Linking
  • BabelNet API,[59] a Java API for knowledge-based multilingual Word Sense Disambiguation in 6 different languages using the BabelNet semantic network
  • WordNet::SenseRelate,[60] a project that includes free, open source systems for word sense disambiguation and lexical sample sense disambiguation
  • UKB: Graph Base WSD,[61] a collection of programs for performing graph-based Word Sense Disambiguation and lexical similarity/relatedness using a pre-existing Lexical Knowledge Base[62]
  • pyWSD,[63] python implementations of Word Sense Disambiguation (WSD) technologies

See also

References

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Works cited

  • Agirre, E.; Lopez de Lacalle, A.; Soroa, A. (2009). "Knowledge-based WSD on Specific Domains: Performing better than Generic Supervised WSD" (PDF). Proc. of IJCAI.
  • Agirre, E.; M. Stevenson. 2006. Knowledge sources for WSD. In Word Sense Disambiguation: Algorithms and Applications, E. Agirre and P. Edmonds, Eds. Springer, New York, NY.
  • Bar-Hillel, Y. (1964). Language and information. Reading, MA: Addison-Wesley.
  • Buitelaar, P.; B. Magnini, C. Strapparava and P. Vossen. 2006. Domain-specific WSD. In Word Sense Disambiguation: Algorithms and Applications, E. Agirre and P. Edmonds, Eds. Springer, New York, NY.
  • Chan, Y. S.; H. T. Ng. 2005. Scaling up word sense disambiguation via parallel texts. In Proceedings of the 20th National Conference on Artificial Intelligence (AAAI, Pittsburgh, PA).
  • Edmonds, P. 2000. Designing a task for SENSEVAL-2. Tech. note. University of Brighton, Brighton. U.K.
  • Fellbaum, Christiane (1997). "Analysis of a handwriting task". Proc. of ANLP-97 Workshop on Tagging Text with Lexical Semantics: Why, What, and How? Washington D.C., USA.
  • Gliozzo, A.; B. Magnini and C. Strapparava. 2004. Unsupervised domain relevance estimation for word sense disambiguation. In Proceedings of the 2004 Conference on Empirical Methods in Natural Language Processing (EMNLP, Barcelona, Spain).
  • Ide, N.; T. Erjavec, D. Tufis. 2002. Sense discrimination with parallel corpora. In Proceedings of ACL Workshop on Word Sense Disambiguation: Recent Successes and Future Directions (Philadelphia, PA).
  • Kilgarriff, A. 1997. I don't believe in word senses. Comput. Human. 31(2), pp. 91–113.
  • Kilgarriff, A.; G. Grefenstette. 2003. Introduction to the special issue on the Web as corpus. Computational Linguistics 29(3), pp. 333–347
  • Kilgarriff, Adam; Joseph Rosenzweig, English Senseval: Report and Results May–June, 2000, University of Brighton
  • Lapata, M.; and F. Keller. 2007. An information retrieval approach to sense ranking. In Proceedings of the Human Language Technology Conference of the North American Chapter of the Association for Computational Linguistics (HLT-NAACL, Rochester, NY).
  • Lenat, D. Archived at Ghostarchive and the : "Computers versus Common Sense". YouTube. Retrieved 2008-12-10. (GoogleTachTalks on YouTube)
  • Lenat, D.; R. V. Guha. 1989. Building Large Knowledge-Based Systems, Addison-Wesley
  • Lesk; M. 1986. Automatic sense disambiguation using machine readable dictionaries: How to tell a pine cone from an ice cream cone. In Proc. of SIGDOC-86: 5th International Conference on Systems Documentation, Toronto, Canada.
  • Litkowski, K. C. 2005. Computational lexicons and dictionaries. In Encyclopaedia of Language and Linguistics (2nd ed.), K. R. Brown, Ed. Elsevier Publishers, Oxford, U.K.
  • Magnini, B; G. Cavaglià. 2000. Integrating subject field codes into WordNet. In Proceedings of the 2nd Conference on Language Resources and Evaluation (LREC, Athens, Greece).
  • McCarthy, D.; R. Koeling, J. Weeds, J. Carroll. 2007. Unsupervised acquisition of predominant word senses. Computational Linguistics 33(4): 553–590.
  • McCarthy, D.; R. Navigli. 2009. The English Lexical Substitution Task, Language Resources and Evaluation, 43(2), Springer.
  • Mihalcea, R. 2007. . In Proc. of the North American Chapter of the Association for Computational Linguistics (NAACL 2007), Rochester, April 2007.
  • Mohammad, S; G. Hirst. 2006. Determining word sense dominance using a thesaurus. In Proceedings of the 11th Conference on European chapter of the Association for Computational Linguistics (EACL, Trento, Italy).
  • Navigli, R. 2006. . Proc. of the 44th Annual Meeting of the Association for Computational Linguistics joint with the 21st International Conference on Computational Linguistics (COLING-ACL 2006), Sydney, Australia.
  • Navigli, R.; A. Di Marco. Clustering and Diversifying Web Search Results with Graph-Based Word Sense Induction. Computational Linguistics, 39(3), MIT Press, 2013, pp. 709–754.
  • Navigli, R.; G. Crisafulli. Inducing Word Senses to Improve Web Search Result Clustering. Proc. of the 2010 Conference on Empirical Methods in Natural Language Processing (EMNLP 2010), MIT Stata Center, Massachusetts, USA.
  • Navigli, R.; M. Lapata. An Experimental Study of Graph Connectivity for Unsupervised Word Sense Disambiguation. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 32(4), IEEE Press, 2010.
  • Navigli, R.; K. Litkowski, O. Hargraves. 2007. SemEval-2007 Task 07: Coarse-Grained English All-Words Task. Proc. of Semeval-2007 Workshop (SemEval), in the 45th Annual Meeting of the Association for Computational Linguistics (ACL 2007), Prague, Czech Republic.
  • Navigli, R.;P. Velardi. 2005. Structural Semantic Interconnections: a Knowledge-Based Approach to Word Sense Disambiguation. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 27(7).
  • Palmer, M.; O. Babko-Malaya and H. T. Dang. 2004. Different sense granularities for different applications. In Proceedings of the 2nd Workshop on Scalable Natural Language Understanding Systems in HLT/NAACL (Boston, MA).
  • Ponzetto, S. P.; R. Navigli. . In Proc. of the 48th Annual Meeting of the Association for Computational Linguistics (ACL), 2010.
  • Pradhan, S.; E. Loper, D. Dligach, M. Palmer. 2007. SemEval-2007 Task 17: English lexical sample, SRL and all words. Proc. of Semeval-2007 Workshop (SEMEVAL), in the 45th Annual Meeting of the Association for Computational Linguistics (ACL 2007), Prague, Czech Republic.
  • Schütze, H. 1998. Automatic word sense discrimination. Computational Linguistics, 24(1): 97–123.
  • Snow, R.; S. Prakash, D. Jurafsky, A. Y. Ng. 2007. Learning to Merge Word Senses, Proceedings of the 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning (EMNLP-CoNLL).
  • Snyder, B.; M. Palmer. 2004. . In Proc. of the 3rd International Workshop on the Evaluation of Systems for the Semantic Analysis of Text (Senseval-3), Barcelona, Spain.
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  • Yarowsky, D. Word-sense disambiguation using statistical models of Roget's categories trained on large corpora. In Proc. of the 14th conference on Computational linguistics (COLING), 1992.
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External links and suggested reading

  • (1998)
  • Evaluation Exercises for Word Sense Disambiguation The de facto standard benchmarks for WSD systems.
  • Roberto Navigli. Word Sense Disambiguation: A Survey, ACM Computing Surveys, 41(2), 2009, pp. 1–69. An up-to-date state of the art of the field.
  • Word Sense Disambiguation as defined in Scholarpedia
  • (PDF) A comprehensive overview By Prof. Nancy Ide & Jean Véronis (1998).
  • Word Sense Disambiguation Tutorial, by Rada Mihalcea and Ted Pedersen (2005).
  • Well, well, well ... Word Sense Disambiguation with Google n-Grams, by Craig Trim (2013).
  • Word Sense Disambiguation: Algorithms and Applications, edited by Eneko Agirre and Philip Edmonds (2006), Springer. Covers the entire field with chapters contributed by leading researchers. www.wsdbook.org site of the book
  • Bar-Hillel, Yehoshua. 1964. Language and Information. New York: Addison-Wesley.
  • Edmonds, Philip & Adam Kilgarriff. 2002. Introduction to the special issue on evaluating word sense disambiguation systems. Journal of Natural Language Engineering, 8(4):279-291.
  • Edmonds, Philip. 2005. Lexical disambiguation. The Elsevier Encyclopedia of Language and Linguistics, 2nd Ed., ed. by Keith Brown, 607–23. Oxford: Elsevier.
  • Ide, Nancy & Jean Véronis. 1998. Word sense disambiguation: The state of the art. Computational Linguistics, 24(1):1-40.
  • Jurafsky, Daniel & James H. Martin. 2000. Speech and Language Processing. New Jersey, USA: Prentice Hall.
  • Litkowski, K. C. 2005. Computational lexicons and dictionaries. In Encyclopaedia of Language and Linguistics (2nd ed.), K. R. Brown, Ed. Elsevier Publishers, Oxford, U.K., 753–761.
  • Manning, Christopher D. & Hinrich Schütze. 1999. Foundations of Statistical Natural Language Processing. Cambridge, MA: MIT Press. Foundations of Statistical Natural Language Processing
  • Mihalcea, Rada. 2007. Word sense disambiguation. Encyclopedia of Machine Learning. Springer-Verlag.
  • Resnik, Philip and David Yarowsky. 2000. Distinguishing systems and distinguishing senses: New evaluation methods for word sense disambiguation, Natural Language Engineering, 5(2):113-133. [1]
  • Yarowsky, David. 2001. Word sense disambiguation. Handbook of Natural Language Processing, ed. by Dale et al., 629–654. New York: Marcel Dekker.

word, sense, disambiguation, disambiguation, redirects, here, other, uses, disambiguation, disambiguation, information, disambiguation, topic, names, wikipedia, wikipedia, disambiguation, process, identifying, which, sense, word, meant, sentence, other, segmen. Disambiguation redirects here For other uses see Disambiguation disambiguation For information on disambiguation of topic names in Wikipedia see Wikipedia Disambiguation Word sense disambiguation WSD is the process of identifying which sense of a word is meant in a sentence or other segment of context In human language processing and cognition it is usually subconscious automatic but can often come to conscious attention when ambiguity impairs clarity of communication given the pervasive polysemy in natural language In computational linguistics it is an open problem that affects other computer related writing such as discourse improving relevance of search engines anaphora resolution coherence and inference Given that natural language requires reflection of neurological reality as shaped by the abilities provided by the brain s neural networks computer science has had a long term challenge in developing the ability in computers to do natural language processing and machine learning Many techniques have been researched including dictionary based methods that use the knowledge encoded in lexical resources supervised machine learning methods in which a classifier is trained for each distinct word on a corpus of manually sense annotated examples and completely unsupervised methods that cluster occurrences of words thereby inducing word senses Among these supervised learning approaches have been the most successful algorithms to date Accuracy of current algorithms is difficult to state without a host of caveats In English accuracy at the coarse grained homograph level is routinely above 90 as of 2009 with some methods on particular homographs achieving over 96 On finer grained sense distinctions top accuracies from 59 1 to 69 0 have been reported in evaluation exercises SemEval 2007 Senseval 2 where the baseline accuracy of the simplest possible algorithm of always choosing the most frequent sense was 51 4 and 57 respectively Contents 1 Variants 2 History 3 Difficulties 3 1 Differences between dictionaries 3 2 Part of speech tagging 3 3 Inter judge variance 3 4 Pragmatics 3 5 Sense inventory and algorithms task dependency 3 6 Discreteness of senses 4 Approaches and methods 4 1 Dictionary and knowledge based methods 4 2 Supervised methods 4 3 Semi supervised methods 4 4 Unsupervised methods 4 5 Other approaches 4 6 Other languages 4 7 Local impediments and summary 5 External knowledge sources 6 Evaluation 6 1 Task design choices 7 Software 8 See also 9 References 10 Works cited 11 External links and suggested readingVariants EditDisambiguation requires two strict inputs a dictionary to specify the senses which are to be disambiguated and a corpus of language data to be disambiguated in some methods a training corpus of language examples is also required WSD task has two variants lexical sample disambiguating the occurrences of a small sample of target words which were previously selected and all words task disambiguation of all the words in a running text All words task is generally considered a more realistic form of evaluation but the corpus is more expensive to produce because human annotators have to read the definitions for each word in the sequence every time they need to make a tagging judgement rather than once for a block of instances for the same target word History EditWSD was first formulated as a distinct computational task during the early days of machine translation in the 1940s making it one of the oldest problems in computational linguistics Warren Weaver first introduced the problem in a computational context in his 1949 memorandum on translation 1 Later Bar Hillel 1960 argued 2 that WSD could not be solved by electronic computer because of the need in general to model all world knowledge In the 1970s WSD was a subtask of semantic interpretation systems developed within the field of artificial intelligence starting with Wilks preference semantics However since WSD systems were at the time largely rule based and hand coded they were prone to a knowledge acquisition bottleneck By the 1980s large scale lexical resources such as the Oxford Advanced Learner s Dictionary of Current English OALD became available hand coding was replaced with knowledge automatically extracted from these resources but disambiguation was still knowledge based or dictionary based In the 1990s the statistical revolution advanced computational linguistics and WSD became a paradigm problem on which to apply supervised machine learning techniques The 2000s saw supervised techniques reach a plateau in accuracy and so attention has shifted to coarser grained senses domain adaptation semi supervised and unsupervised corpus based systems combinations of different methods and the return of knowledge based systems via graph based methods Still supervised systems continue to perform best Difficulties EditDifferences between dictionaries Edit One problem with word sense disambiguation is deciding what the senses are as different dictionaries and thesauruses will provide different divisions of words into senses Some researchers have suggested choosing a particular dictionary and using its set of senses to deal with this issue Generally however research results using broad distinctions in senses have been much better than those using narrow ones 3 4 Most researchers continue to work on fine grained WSD Most research in the field of WSD is performed by using WordNet as a reference sense inventory for English WordNet is a computational lexicon that encodes concepts as synonym sets e g the concept of car is encoded as car auto automobile machine motorcar Other resources used for disambiguation purposes include Roget s Thesaurus 5 and Wikipedia 6 More recently BabelNet a multilingual encyclopedic dictionary has been used for multilingual WSD 7 Part of speech tagging Edit In any real test part of speech tagging and sense tagging have proven to be very closely related with each potentially imposing constraints upon the other The question whether these tasks should be kept together or decoupled is still not unanimously resolved but recently scientists incline to test these things separately e g in the Senseval SemEval competitions parts of speech are provided as input for the text to disambiguate Both WSD and part of speech tagging involve disambiguating or tagging with words However algorithms used for one do not tend to work well for the other mainly because the part of speech of a word is primarily determined by the immediately adjacent one to three words whereas the sense of a word may be determined by words further away The success rate for part of speech tagging algorithms is at present much higher than that for WSD state of the art being around 96 8 accuracy or better as compared to less than 75 citation needed accuracy in word sense disambiguation with supervised learning These figures are typical for English and may be very different from those for other languages Inter judge variance Edit Another problem is inter judge variance WSD systems are normally tested by having their results on a task compared against those of a human However while it is relatively easy to assign parts of speech to text training people to tag senses has been proven to be far more difficult 9 While users can memorize all of the possible parts of speech a word can take it is often impossible for individuals to memorize all of the senses a word can take Moreover humans do not agree on the task at hand give a list of senses and sentences and humans will not always agree on which word belongs in which sense 10 As human performance serves as the standard it is an upper bound for computer performance Human performance however is much better on coarse grained than fine grained distinctions so this again is why research on coarse grained distinctions 11 12 has been put to test in recent WSD evaluation exercises 3 4 Pragmatics Edit Some AI researchers like Douglas Lenat argue that one cannot parse meanings from words without some form of common sense ontology This linguistic issue is called pragmatics As agreed by researchers to properly identify senses of words one must know common sense facts 13 Moreover sometimes the common sense is needed to disambiguate such words like pronouns in case of having anaphoras or cataphoras in the text Sense inventory and algorithms task dependency Edit A task independent sense inventory is not a coherent concept 14 each task requires its own division of word meaning into senses relevant to the task Additionally completely different algorithms might be required by different applications In machine translation the problem takes the form of target word selection The senses are words in the target language which often correspond to significant meaning distinctions in the source language bank could translate to the French banque that is financial bank or rive that is edge of river In information retrieval a sense inventory is not necessarily required because it is enough to know that a word is used in the same sense in the query and a retrieved document what sense that is is unimportant Discreteness of senses Edit Finally the very notion of word sense is slippery and controversial Most people can agree in distinctions at the coarse grained homograph level e g pen as writing instrument or enclosure but go down one level to fine grained polysemy and disagreements arise For example in Senseval 2 which used fine grained sense distinctions human annotators agreed in only 85 of word occurrences 15 Word meaning is in principle infinitely variable and context sensitive It does not divide up easily into distinct or discrete sub meanings 16 Lexicographers frequently discover in corpora loose and overlapping word meanings and standard or conventional meanings extended modulated and exploited in a bewildering variety of ways The art of lexicography is to generalize from the corpus to definitions that evoke and explain the full range of meaning of a word making it seem like words are well behaved semantically However it is not at all clear if these same meaning distinctions are applicable in computational applications as the decisions of lexicographers are usually driven by other considerations In 2009 a task named lexical substitution was proposed as a possible solution to the sense discreteness problem 17 The task consists of providing a substitute for a word in context that preserves the meaning of the original word potentially substitutes can be chosen from the full lexicon of the target language thus overcoming discreteness Approaches and methods EditThere are two main approaches to WSD deep approaches and shallow approaches Deep approaches presume access to a comprehensive body of world knowledge These approaches are generally not considered to be very successful in practice mainly because such a body of knowledge does not exist in a computer readable format outside very limited domains 18 Additionally due to the long tradition in computational linguistics of trying such approaches in terms of coded knowledge and in some cases it can be hard to distinguish between knowledge involved in linguistic or world knowledge The first attempt was that by Margaret Masterman and her colleagues at the Cambridge Language Research Unit in England in the 1950s This attempt used as data a punched card version of Roget s Thesaurus and its numbered heads as an indicator of topics and looked for repetitions in text using a set intersection algorithm It was not very successful 19 but had strong relationships to later work especially Yarowsky s machine learning optimisation of a thesaurus method in the 1990s Shallow approaches don t try to understand the text but instead consider the surrounding words These rules can be automatically derived by the computer using a training corpus of words tagged with their word senses This approach while theoretically not as powerful as deep approaches gives superior results in practice due to the computer s limited world knowledge There are four conventional approaches to WSD Dictionary and knowledge based methods These rely primarily on dictionaries thesauri and lexical knowledge bases without using any corpus evidence Semi supervised or minimally supervised methods These make use of a secondary source of knowledge such as a small annotated corpus as seed data in a bootstrapping process or a word aligned bilingual corpus Supervised methods These make use of sense annotated corpora to train from Unsupervised methods These eschew almost completely external information and work directly from raw unannotated corpora These methods are also known under the name of word sense discrimination Almost all these approaches work by defining a window of n content words around each word to be disambiguated in the corpus and statistically analyzing those n surrounding words Two shallow approaches used to train and then disambiguate are Naive Bayes classifiers and decision trees In recent research kernel based methods such as support vector machines have shown superior performance in supervised learning Graph based approaches have also gained much attention from the research community and currently achieve performance close to the state of the art Dictionary and knowledge based methods Edit The Lesk algorithm 20 is the seminal dictionary based method It is based on the hypothesis that words used together in text are related to each other and that the relation can be observed in the definitions of the words and their senses Two or more words are disambiguated by finding the pair of dictionary senses with the greatest word overlap in their dictionary definitions For example when disambiguating the words in pine cone the definitions of the appropriate senses both include the words evergreen and tree at least in one dictionary A similar approach 21 searches for the shortest path between two words the second word is iteratively searched among the definitions of every semantic variant of the first word then among the definitions of every semantic variant of each word in the previous definitions and so on Finally the first word is disambiguated by selecting the semantic variant which minimizes the distance from the first to the second word An alternative to the use of the definitions is to consider general word sense relatedness and to compute the semantic similarity of each pair of word senses based on a given lexical knowledge base such as WordNet Graph based methods reminiscent of spreading activation research of the early days of AI research have been applied with some success More complex graph based approaches have been shown to perform almost as well as supervised methods 22 or even outperforming them on specific domains 3 23 Recently it has been reported that simple graph connectivity measures such as degree perform state of the art WSD in the presence of a sufficiently rich lexical knowledge base 24 Also automatically transferring knowledge in the form of semantic relations from Wikipedia to WordNet has been shown to boost simple knowledge based methods enabling them to rival the best supervised systems and even outperform them in a domain specific setting 25 The use of selectional preferences or selectional restrictions is also useful for example knowing that one typically cooks food one can disambiguate the word bass in I am cooking basses i e it s not a musical instrument Supervised methods Edit Supervised methods are based on the assumption that the context can provide enough evidence on its own to disambiguate words hence common sense and reasoning are deemed unnecessary Probably every machine learning algorithm going has been applied to WSD including associated techniques such as feature selection parameter optimization and ensemble learning Support Vector Machines and memory based learning have been shown to be the most successful approaches to date probably because they can cope with the high dimensionality of the feature space However these supervised methods are subject to a new knowledge acquisition bottleneck since they rely on substantial amounts of manually sense tagged corpora for training which are laborious and expensive to create Semi supervised methods Edit Because of the lack of training data many word sense disambiguation algorithms use semi supervised learning which allows both labeled and unlabeled data The Yarowsky algorithm was an early example of such an algorithm 26 It uses the One sense per collocation and the One sense per discourse properties of human languages for word sense disambiguation From observation words tend to exhibit only one sense in most given discourse and in a given collocation 27 The bootstrapping approach starts from a small amount of seed data for each word either manually tagged training examples or a small number of surefire decision rules e g play in the context of bass almost always indicates the musical instrument The seeds are used to train an initial classifier using any supervised method This classifier is then used on the untagged portion of the corpus to extract a larger training set in which only the most confident classifications are included The process repeats each new classifier being trained on a successively larger training corpus until the whole corpus is consumed or until a given maximum number of iterations is reached Other semi supervised techniques use large quantities of untagged corpora to provide co occurrence information that supplements the tagged corpora These techniques have the potential to help in the adaptation of supervised models to different domains Also an ambiguous word in one language is often translated into different words in a second language depending on the sense of the word Word aligned bilingual corpora have been used to infer cross lingual sense distinctions a kind of semi supervised system citation needed Unsupervised methods Edit Main article Word sense induction Unsupervised learning is the greatest challenge for WSD researchers The underlying assumption is that similar senses occur in similar contexts and thus senses can be induced from text by clustering word occurrences using some measure of similarity of context 28 a task referred to as word sense induction or discrimination Then new occurrences of the word can be classified into the closest induced clusters senses Performance has been lower than for the other methods described above but comparisons are difficult since senses induced must be mapped to a known dictionary of word senses If a mapping to a set of dictionary senses is not desired cluster based evaluations including measures of entropy and purity can be performed Alternatively word sense induction methods can be tested and compared within an application For instance it has been shown that word sense induction improves Web search result clustering by increasing the quality of result clusters and the degree diversification of result lists 29 30 It is hoped that unsupervised learning will overcome the knowledge acquisition bottleneck because they are not dependent on manual effort Representing words considering their context through fixed size dense vectors word embeddings has become one of the most fundamental blocks in several NLP systems 31 32 33 Even though most of traditional word embedding techniques conflate words with multiple meanings into a single vector representation they still can be used to improve WSD 34 In addition to word embeddings techniques lexical databases e g WordNet ConceptNet BabelNet can also assist unsupervised systems in mapping words and their senses as dictionaries Some techniques that combine lexical databases and word embeddings are presented in AutoExtend 35 36 and Most Suitable Sense Annotation MSSA 37 In AutoExtend 36 they present a method that decouples an object input representation into its properties such as words and their word senses AutoExtend uses a graph structure to map words e g text and non word e g synsets in WordNet objects as nodes and the relationship between nodes as edges The relations edges in AutoExtend can either express the addition or similarity between its nodes The former captures the intuition behind the offset calculus 31 while the latter defines the similarity between two nodes In MSSA 37 an unsupervised disambiguation system uses the similarity between word senses in a fixed context window to select the most suitable word sense using a pre trained word embedding model and WordNet For each context window MSSA calculates the centroid of each word sense definition by averaging the word vectors of its words in WordNet s glosses i e short defining gloss and one or more usage example using a pre trained word embeddings model These centroids are later used to select the word sense with the highest similarity of a target word to its immediately adjacent neighbors i e predecessor and successor words After all words are annotated and disambiguated they can be used as a training corpus in any standard word embedding technique In its improved version MSSA can make use of word sense embeddings to repeat its disambiguation process iteratively Other approaches Edit Other approaches may vary differently in their methods Domain driven disambiguation 38 39 Identification of dominant word senses 40 41 42 WSD using Cross Lingual Evidence 43 44 WSD solution in John Ball s language independent NLU combining Patom Theory and RRG Role and Reference Grammar Type inference in constraint based grammars 45 Other languages Edit Hindi Lack of lexical resources in Hindi have hindered the performance of supervised models of WSD while the unsupervised models suffer due to extensive morphology A possible solution to this problem is the design of a WSD model by means of parallel corpora 46 47 The creation of the Hindi WordNet has paved way for several Supervised methods which have been proven to produce a higher accuracy in disambiguating nouns 48 Local impediments and summary Edit The knowledge acquisition bottleneck is perhaps the major impediment to solving the WSD problem Unsupervised methods rely on knowledge about word senses which is only sparsely formulated in dictionaries and lexical databases Supervised methods depend crucially on the existence of manually annotated examples for every word sense a requisite that can so far when be met only for a handful of words for testing purposes as it is done in the Senseval exercises One of the most promising trends in WSD research is using the largest corpus ever accessible the World Wide Web to acquire lexical information automatically 49 WSD has been traditionally understood as an intermediate language engineering technology which could improve applications such as information retrieval IR In this case however the reverse is also true web search engines implement simple and robust IR techniques that can successfully mine the Web for information to use in WSD The historic lack of training data has provoked the appearance of some new algorithms and techniques as described in Automatic acquisition of sense tagged corpora External knowledge sources EditKnowledge is a fundamental component of WSD Knowledge sources provide data which are essential to associate senses with words They can vary from corpora of texts either unlabeled or annotated with word senses to machine readable dictionaries thesauri glossaries ontologies etc They can be 50 51 classified as follows Structured Machine readable dictionaries MRDs Ontologies ThesauriUnstructured Collocation resources Other resources such as word frequency lists stoplists domain labels 52 etc Corpora raw corpora and sense annotated corporaEvaluation EditComparing and evaluating different WSD systems is extremely difficult because of the different test sets sense inventories and knowledge resources adopted Before the organization of specific evaluation campaigns most systems were assessed on in house often small scale data sets In order to test one s algorithm developers should spend their time to annotate all word occurrences And comparing methods even on the same corpus is not eligible if there is different sense inventories In order to define common evaluation datasets and procedures public evaluation campaigns have been organized Senseval now renamed SemEval is an international word sense disambiguation competition held every three years since 1998 Senseval 1 1998 Senseval 2 2001 Senseval 3 2004 and its successor SemEval 2007 The objective of the competition is to organize different lectures preparing and hand annotating corpus for testing systems perform a comparative evaluation of WSD systems in several kinds of tasks including all words and lexical sample WSD for different languages and more recently new tasks such as semantic role labeling gloss WSD lexical substitution etc The systems submitted for evaluation to these competitions usually integrate different techniques and often combine supervised and knowledge based methods especially for avoiding bad performance in lack of training examples In recent years 2007 2012 the WSD evaluation task choices had grown and the criterion for evaluating WSD has changed drastically depending on the variant of the WSD evaluation task Below enumerates the variety of WSD tasks Task design choices Edit As technology evolves the Word Sense Disambiguation WSD tasks grows in different flavors towards various research directions and for more languages Classic monolingual WSD evaluation tasks use WordNet as the sense inventory and are largely based on supervised semi supervised classification with the manually sense annotated corpora 53 Classic English WSD uses the Princeton WordNet as it sense inventory and the primary classification input is normally based on the SemCor corpus Classical WSD for other languages uses their respective WordNet as sense inventories and sense annotated corpora tagged in their respective languages Often researchers will also tapped on the SemCor corpus and aligned bitexts with English as its source language Cross lingual WSD evaluation task is also focused on WSD across 2 or more languages simultaneously Unlike the Multilingual WSD tasks instead of providing manually sense annotated examples for each sense of a polysemous noun the sense inventory is built up on the basis of parallel corpora e g Europarl corpus 54 Multilingual WSD evaluation tasks focused on WSD across 2 or more languages simultaneously using their respective WordNets as its sense inventories or BabelNet as multilingual sense inventory 55 It evolved from the Translation WSD evaluation tasks that took place in Senseval 2 A popular approach is to carry out monolingual WSD and then map the source language senses into the corresponding target word translations 56 Word Sense Induction and Disambiguation task is a combined task evaluation where the sense inventory is first induced from a fixed training set data consisting of polysemous words and the sentence that they occurred in then WSD is performed on a different testing data set 57 Software EditBabelfy 58 a unified state of the art system for multilingual Word Sense Disambiguation and Entity Linking BabelNet API 59 a Java API for knowledge based multilingual Word Sense Disambiguation in 6 different languages using the BabelNet semantic network WordNet SenseRelate 60 a project that includes free open source systems for word sense disambiguation and lexical sample sense disambiguation UKB Graph Base WSD 61 a collection of programs for performing graph based Word Sense Disambiguation and lexical similarity relatedness using a pre existing Lexical Knowledge Base 62 pyWSD 63 python implementations of Word Sense Disambiguation WSD technologiesSee also Edit Linguistics portalAmbiguity Controlled natural language Entity linking Lesk algorithm Lexical substitution Part of speech tagging Polysemy Semeval Semantic unification Judicial interpretation Sentence boundary disambiguation Syntactic ambiguity Word sense Word sense inductionReferences Edit Weaver 1949 Bar Hillel 1964 pp 174 179 a b c Navigli Litkowski amp Hargraves 2007 pp 30 35 sfn error no target CITEREFNavigliLitkowskiHargraves2007 help a b Pradhan et al 2007 pp 87 92 sfn error no target CITEREFPradhanLoperDligachPalmer2007 help Yarowsky 1992 pp 454 460 sfn error no target CITEREFYarowsky1992 help Mihalcea 2007 sfn error no target CITEREFMihalcea2007 help A Moro A Raganato R Navigli Entity Linking meets Word Sense Disambiguation a Unified Approach Archived 2014 08 08 at the Wayback Machine Transactions of the Association for Computational Linguistics TACL 2 pp 231 244 2014 Martinez Angel R January 2012 Part of speech tagging Part of speech tagging Wiley Interdisciplinary Reviews Computational Statistics 4 1 107 113 doi 10 1002 wics 195 S2CID 62672734 Fellbaum 1997 Snyder amp Palmer 2004 pp 41 43 sfn error no target CITEREFSnyderPalmer2004 help Navigli 2006 pp 105 112 sfn error no target CITEREFNavigli2006 help Snow et al 2007 pp 1005 1014 sfn error no target CITEREFSnowPrakashJurafskyNg2007 help Lenat sfn error no target CITEREFLenat help Palmer Babko Malaya amp Dang 2004 pp 49 56 sfn error no target CITEREFPalmerBabko MalayaDang2004 help Edmonds 2000 sfn error no target CITEREFEdmonds2000 help Kilgarrif 1997 pp 91 113 sfn error no target CITEREFKilgarrif1997 help McCarthy amp Navigli 2009 pp 139 159 sfn error no target CITEREFMcCarthyNavigli2009 help Lenat amp Guha 1989 sfn error no target CITEREFLenatGuha1989 help Wilks Slator amp Guthrie 1996 sfn error no target CITEREFWilksSlatorGuthrie1996 help Lesk 1986 pp 24 26 sfn error no target CITEREFLesk1986 help Diamantini C Mircoli A Potena D Storti E 2015 06 01 Semantic disambiguation in a social information discovery system 2015 International Conference on Collaboration Technologies and Systems CTS 326 333 doi 10 1109 CTS 2015 7210442 ISBN 978 1 4673 7647 1 S2CID 13260353 Navigli amp Velardi 2005 pp 1063 1074 sfn error no target CITEREFNavigliVelardi2005 help Agirre Lopez de Lacalle amp Soroa 2009 pp 1501 1506 Navigli amp Lapata 2010 pp 678 692 sfn error no target CITEREFNavigliLapata2010 help Ponzetto amp Navigli 2010 pp 1522 1531 sfn error no target CITEREFPonzettoNavigli2010 help Yarowsky 1995 pp 189 196 sfn error no target CITEREFYarowsky1995 help Mitkov Ruslan 2004 13 5 3 Two claims about senses The Oxford Handbook of Computational Linguistics OUP p 257 ISBN 978 0 19 927634 9 Schutze 1998 pp 97 123 sfn error no target CITEREFSchutze1998 help Navigli amp Crisafulli 2010 sfn error no target CITEREFNavigliCrisafulli2010 help DiMarco amp Navigli 2013 sfn error no target CITEREFDiMarcoNavigli2013 help a b Mikolov Tomas Chen Kai Corrado Greg Dean Jeffrey 2013 01 16 Efficient Estimation of Word Representations in Vector Space arXiv 1301 3781 cs CL Pennington Jeffrey Socher Richard Manning Christopher 2014 Glove Global Vectors for Word Representation Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing EMNLP Stroudsburg PA USA Association for Computational Linguistics 1532 1543 doi 10 3115 v1 d14 1162 S2CID 1957433 Bojanowski Piotr Grave Edouard Joulin Armand Mikolov Tomas December 2017 Enriching Word Vectors with Subword Information Transactions of the Association for Computational Linguistics 5 135 146 doi 10 1162 tacl a 00051 ISSN 2307 387X Iacobacci Ignacio Pilehvar Mohammad Taher Navigli Roberto 2016 Embeddings for Word Sense Disambiguation An Evaluation Study Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics Volume 1 Long Papers Berlin Germany Association for Computational Linguistics 897 907 doi 10 18653 v1 P16 1085 Rothe Sascha Schutze Hinrich 2015 AutoExtend Extending Word Embeddings to Embeddings for Synsets and Lexemes Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing Volume 1 Long Papers Stroudsburg PA USA Association for Computational Linguistics 1793 1803 arXiv 1507 01127 Bibcode 2015arXiv150701127R doi 10 3115 v1 p15 1173 S2CID 15687295 a b Rothe Sascha Schutze Hinrich September 2017 AutoExtend Combining Word Embeddings with Semantic Resources Computational Linguistics 43 3 593 617 doi 10 1162 coli a 00294 ISSN 0891 2017 a b Ruas Terry Grosky William Aizawa Akiko December 2019 Multi sense embeddings through a word sense disambiguation process Expert Systems with Applications 136 288 303 arXiv 2101 08700 doi 10 1016 j eswa 2019 06 026 hdl 2027 42 145475 S2CID 52225306 Gliozzo Magnini amp Strapparava 2004 pp 380 387 sfn error no target CITEREFGliozzoMagniniStrapparava2004 help Buitelaar et al 2006 pp 275 298 sfn error no target CITEREFBuitelaarMagniniStrapparavaVossen2006 help McCarthy et al 2007 pp 553 590 sfn error no target CITEREFMcCarthyKoelingWeedsCarroll2007 help Mohammad amp Hirst 2006 pp 121 128 sfn error no target CITEREFMohammadHirst2006 help Lapata amp Keller 2007 pp 348 355 sfn error no target CITEREFLapataKeller2007 help Ide Erjavec amp Tufis 2002 pp 54 60 sfn error no target CITEREFIdeErjavecTufis2002 help Chan amp Ng 2005 pp 1037 1042 sfn error no target CITEREFChanNg2005 help Stuart M Shieber 1992 Constraint based Grammar Formalisms Parsing and Type Inference for Natural and Computer Languages MIT Press ISBN 978 0 262 19324 5 Bhattacharya Indrajit Lise Getoor and Yoshua Bengio Unsupervised sense disambiguation using bilingual probabilistic models Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics Association for Computational Linguistics 2004 Diab Mona and Philip Resnik An unsupervised method for word sense tagging using parallel corpora Proceedings of the 40th Annual Meeting on Association for Computational Linguistics Association for Computational Linguistics 2002 Manish Sinha Mahesh Kumar Prabhakar Pande Laxmi Kashyap and Pushpak Bhattacharyya Hindi word sense disambiguation In International Symposium on Machine Translation Natural Language Processing and Translation Support Systems Delhi India 2004 Kilgarrif amp Grefenstette 2003 pp 333 347 sfn error no target CITEREFKilgarrifGrefenstette2003 help Litkowski 2005 pp 753 761 sfn error no target CITEREFLitkowski2005 help Agirre amp Stevenson 2006 pp 217 251 sfn error no target CITEREFAgirreStevenson2006 help Magnini amp Cavaglia 2000 pp 1413 1418 sfn error no target CITEREFMagniniCavaglia2000 help Lucia Specia Maria das Gracas Volpe Nunes Gabriela Castelo Branco Ribeiro and Mark Stevenson Multilingual versus monolingual WSD Archived 2012 04 10 at the Wayback Machine In EACL 2006 Workshop on Making Sense of Sense Bringing Psycholinguistics and Computational Linguistics Together pages 33 40 Trento Italy April 2006 Els Lefever and Veronique Hoste SemEval 2010 task 3 cross lingual word sense disambiguation Proceedings of the Workshop on Semantic Evaluations Recent Achievements and Future Directions June 04 04 2009 Boulder Colorado R Navigli D A Jurgens D Vannella SemEval 2013 Task 12 Multilingual Word Sense Disambiguation Proc of seventh International Workshop on Semantic Evaluation SemEval in the Second Joint Conference on Lexical and Computational Semantics SEM 2013 Atlanta USA June 14 15th 2013 pp 222 231 Lucia Specia Maria das Gracas Volpe Nunes Gabriela Castelo Branco Ribeiro and Mark Stevenson Multilingual versus monolingual WSD Archived 2012 04 10 at the Wayback Machine In EACL 2006 Workshop on Making Sense of Sense Bringing Psycholinguistics and Computational Linguistics Together pages 33 40 Trento Italy April 2006 Eneko Agirre and Aitor Soroa Semeval 2007 task 02 evaluating word sense induction and discrimination systems Proceedings of the 4th International Workshop on Semantic Evaluations p 7 12 June 23 24 2007 Prague Czech Republic Babelfy Babelfy Retrieved 2018 03 22 BabelNet API Babelnet org Retrieved 2018 03 22 WordNet SenseRelate Senserelate sourceforge net Retrieved 2018 03 22 UKB Graph Base WSD Ixa2 si ehu es Retrieved 2018 03 22 Lexical Knowledge Base LKB Moin delph in net 2018 02 05 Retrieved 2018 03 22 alvations pyWSD Github com Retrieved 2018 03 22 Works cited EditAgirre E Lopez de Lacalle A Soroa A 2009 Knowledge based WSD on Specific Domains Performing better than Generic Supervised WSD PDF Proc of IJCAI Agirre E M Stevenson 2006 Knowledge sources for WSD In Word Sense Disambiguation Algorithms and Applications E Agirre and P Edmonds Eds Springer New York NY Bar Hillel Y 1964 Language and information Reading MA Addison Wesley Buitelaar P B Magnini C Strapparava and P Vossen 2006 Domain specific WSD In Word Sense Disambiguation Algorithms and Applications E Agirre and P Edmonds Eds Springer New York NY Chan Y S H T Ng 2005 Scaling up word sense disambiguation via parallel texts In Proceedings of the 20th National Conference on Artificial Intelligence AAAI Pittsburgh PA Edmonds P 2000 Designing a task for SENSEVAL 2 Tech note University of Brighton Brighton U K Fellbaum Christiane 1997 Analysis of a handwriting task Proc of ANLP 97 Workshop on Tagging Text with Lexical Semantics Why What and How Washington D C USA Gliozzo A B Magnini and C Strapparava 2004 Unsupervised domain relevance estimation for word sense disambiguation In Proceedings of the 2004 Conference on Empirical Methods in Natural Language Processing EMNLP Barcelona Spain Ide N T Erjavec D Tufis 2002 Sense discrimination with parallel corpora In Proceedings of ACL Workshop on Word Sense Disambiguation Recent Successes and Future Directions Philadelphia PA Kilgarriff A 1997 I don t believe in word senses Comput Human 31 2 pp 91 113 Kilgarriff A G Grefenstette 2003 Introduction to the special issue on the Web as corpus Computational Linguistics 29 3 pp 333 347 Kilgarriff Adam Joseph Rosenzweig English Senseval Report and Results May June 2000 University of Brighton Lapata M and F Keller 2007 An information retrieval approach to sense ranking In Proceedings of the Human Language Technology Conference of the North American Chapter of the Association for Computational Linguistics HLT NAACL Rochester NY Lenat D Archived at Ghostarchive and the Wayback Machine Computers versus Common Sense YouTube Retrieved 2008 12 10 GoogleTachTalks on YouTube Lenat D R V Guha 1989 Building Large Knowledge Based Systems Addison Wesley Lesk M 1986 Automatic sense disambiguation using machine readable dictionaries How to tell a pine cone from an ice cream cone In Proc of SIGDOC 86 5th International Conference on Systems Documentation Toronto Canada Litkowski K C 2005 Computational lexicons and dictionaries In Encyclopaedia of Language and Linguistics 2nd ed K R Brown Ed Elsevier Publishers Oxford U K Magnini B G Cavaglia 2000 Integrating subject field codes into WordNet In Proceedings of the 2nd Conference on Language Resources and Evaluation LREC Athens Greece McCarthy D R Koeling J Weeds J Carroll 2007 Unsupervised acquisition of predominant word senses Computational Linguistics 33 4 553 590 McCarthy D R Navigli 2009 The English Lexical Substitution Task Language Resources and Evaluation 43 2 Springer Mihalcea R 2007 Using Wikipedia for Automatic Word Sense Disambiguation In Proc of the North American Chapter of the Association for Computational Linguistics NAACL 2007 Rochester April 2007 Mohammad S G Hirst 2006 Determining word sense dominance using a thesaurus In Proceedings of the 11th Conference on European chapter of the Association for Computational Linguistics EACL Trento Italy Navigli R 2006 Meaningful Clustering of Senses Helps Boost Word Sense Disambiguation Performance Proc of the 44th Annual Meeting of the Association for Computational Linguistics joint with the 21st International Conference on Computational Linguistics COLING ACL 2006 Sydney Australia Navigli R A Di Marco Clustering and Diversifying Web Search Results with Graph Based Word Sense Induction Computational Linguistics 39 3 MIT Press 2013 pp 709 754 Navigli R G Crisafulli Inducing Word Senses to Improve Web Search Result Clustering Proc of the 2010 Conference on Empirical Methods in Natural Language Processing EMNLP 2010 MIT Stata Center Massachusetts USA Navigli R M Lapata An Experimental Study of Graph Connectivity for Unsupervised Word Sense Disambiguation IEEE Transactions on Pattern Analysis and Machine Intelligence TPAMI 32 4 IEEE Press 2010 Navigli R K Litkowski O Hargraves 2007 SemEval 2007 Task 07 Coarse Grained English All Words Task Proc of Semeval 2007 Workshop SemEval in the 45th Annual Meeting of the Association for Computational Linguistics ACL 2007 Prague Czech Republic Navigli R P Velardi 2005 Structural Semantic Interconnections a Knowledge Based Approach to Word Sense Disambiguation IEEE Transactions on Pattern Analysis and Machine Intelligence TPAMI 27 7 Palmer M O Babko Malaya and H T Dang 2004 Different sense granularities for different applications In Proceedings of the 2nd Workshop on Scalable Natural Language Understanding Systems in HLT NAACL Boston MA Ponzetto S P R Navigli Knowledge rich Word Sense Disambiguation rivaling supervised systems In Proc of the 48th Annual Meeting of the Association for Computational Linguistics ACL 2010 Pradhan S E Loper D Dligach M Palmer 2007 SemEval 2007 Task 17 English lexical sample SRL and all words Proc of Semeval 2007 Workshop SEMEVAL in the 45th Annual Meeting of the Association for Computational Linguistics ACL 2007 Prague Czech Republic Schutze H 1998 Automatic word sense discrimination Computational Linguistics 24 1 97 123 Snow R S Prakash D Jurafsky A Y Ng 2007 Learning to Merge Word Senses Proceedings of the 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning EMNLP CoNLL Snyder B M Palmer 2004 The English all words task In Proc of the 3rd International Workshop on the Evaluation of Systems for the Semantic Analysis of Text Senseval 3 Barcelona Spain Weaver Warren 1949 Translation PDF In Locke W N Booth A D eds Machine Translation of Languages Fourteen Essays Cambridge MA MIT Press Wilks Y B Slator L Guthrie 1996 Electric Words dictionaries computers and meanings Cambridge MA MIT Press Yarowsky D Word sense disambiguation using statistical models of Roget s categories trained on large corpora In Proc of the 14th conference on Computational linguistics COLING 1992 Yarowsky D 1995 Unsupervised word sense disambiguation rivaling supervised methods In Proc of the 33rd Annual Meeting of the Association for Computational Linguistics External links and suggested reading Edit Look up disambiguation in Wiktionary the free dictionary Computational Linguistics Special Issue on Word Sense Disambiguation 1998 Evaluation Exercises for Word Sense Disambiguation The de facto standard benchmarks for WSD systems Roberto Navigli Word Sense Disambiguation A Survey ACM Computing Surveys 41 2 2009 pp 1 69 An up to date state of the art of the field Word Sense Disambiguation as defined in Scholarpedia Word Sense Disambiguation The State of the Art PDF A comprehensive overview By Prof Nancy Ide amp Jean Veronis 1998 Word Sense Disambiguation Tutorial by Rada Mihalcea and Ted Pedersen 2005 Well well well Word Sense Disambiguation with Google n Grams by Craig Trim 2013 Word Sense Disambiguation Algorithms and Applications edited by Eneko Agirre and Philip Edmonds 2006 Springer Covers the entire field with chapters contributed by leading researchers www wsdbook org site of the book Bar Hillel Yehoshua 1964 Language and Information New York Addison Wesley Edmonds Philip amp Adam Kilgarriff 2002 Introduction to the special issue on evaluating word sense disambiguation systems Journal of Natural Language Engineering 8 4 279 291 Edmonds Philip 2005 Lexical disambiguation The Elsevier Encyclopedia of Language and Linguistics 2nd Ed ed by Keith Brown 607 23 Oxford Elsevier Ide Nancy amp Jean Veronis 1998 Word sense disambiguation The state of the art Computational Linguistics 24 1 1 40 Jurafsky Daniel amp James H Martin 2000 Speech and Language Processing New Jersey USA Prentice Hall Litkowski K C 2005 Computational lexicons and dictionaries In Encyclopaedia of Language and Linguistics 2nd ed K R Brown Ed Elsevier Publishers Oxford U K 753 761 Manning Christopher D amp Hinrich Schutze 1999 Foundations of Statistical Natural Language Processing Cambridge MA MIT Press Foundations of Statistical Natural Language Processing Mihalcea Rada 2007 Word sense disambiguation Encyclopedia of Machine Learning Springer Verlag Resnik Philip and David Yarowsky 2000 Distinguishing systems and distinguishing senses New evaluation methods for word sense disambiguation Natural Language Engineering 5 2 113 133 1 Yarowsky David 2001 Word sense disambiguation Handbook of Natural Language Processing ed by Dale et al 629 654 New York Marcel Dekker Retrieved from https en wikipedia org w index php title Word sense disambiguation amp oldid 1131282842, wikipedia, wiki, book, books, library,

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