Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
Skip to main content

Trends in word sense disambiguation

  • Published:
Artificial Intelligence Review Aims and scope Submit manuscript

Abstract

The problem and process of identifying the meaning of a word as per its usage context is called word sense disambiguation (WSD). Although research in this field has been ongoing for the past forty years, a distinct change of techniques adopted can be observed over time. Two important parameters govern the direction in which WSD research progresses during any period. These are the underlying requirement of the kind of sense disambiguation, or the domain, and the robustness of available knowledge in the form of corpora or dictionaries. This paper surveys the progress of WSD over time and the important linguistic achievements that enabled this progress.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

Abbreviations

WSD:

Word sense disambiguation

AI:

Artificial intelligence

References

  • Agirre E, Martinez D, L’opez de Lacealle O, Soroa A (2006) Two graph-based algorithms for state-of-the-art WSD. In: Proceedings of the 2006 conference on empirical methods in natural language processing, Stroudsburg, PA, USA, pp 585–593

  • Bhattacharya I, Getoor L, Bengio Y (2004) Unsupervised sense disambiguation using bilingual probabilistic models. In: ACL 04 Proceedings of the 42nd annual meeting on association for computational linguistics, Stroudsburg, PA, USA, p 287 (2004)

  • Brody S, Lapata M (2009) Bayesian word sense induction. In: Proceedings of the 12th conference of the European chapter of the association for computational linguistics (EACL’09). Association for computational linguistics, Stroudsburg, PA, USA, pp 103–111

  • Bruce RF, Wiebe JM (1999) Decomposable modeling in natural language processing. Comput Linguist 25(2): 195–207

    Google Scholar 

  • Chen P, Ding W, Choly M, Bowes C (2010) Word sense disambiguation with automatically acquired knowledge. IEEE Intell Syst PP(99), 1–1

    Google Scholar 

  • Chodorow M, Leacock C, Miller G (2000) A topical/local classifier for word sense identification. Comput Humanit 34(1): 115–120

    Article  Google Scholar 

  • Chodorow M, Miller G, Landes S, Leacock C, Thomas R (1994) Using a semantic concordance for sense identification. In: Proceedings of the workshop on human language technology HLT 94, Plainsboro, NJ, pp 240–243

  • Collins A, Loftus E (1975) A spreading-activation theory of semantic processing. Pyschol Rev 82(6): 407–428

    Article  Google Scholar 

  • Dang H, Palmer M (2002) Combining contextual features for word sense disambiguation. In: Proceedings of the ACL-02 workshop on word sense disambiguation: recent successes and future directions— WSD ’02, Stroudsburg, PA, USA, vol 8, pp 88–94

  • Escudero G, Lluís M, German R (2000) Boosting applied to word sense disambiguation. In: Proceedings of the 11th European conference on machine learning (ECML: ’00), machine learning: ECML 2000, vol 1820, 129–141

  • Fellbaum C, Palmer M, Dang HT, Delfs L, Wolf S (2001) Manual and automated semantic annotation with WordNet. In: Proceedings of the NAACL workshop on WordNet and other lexical resources applications customizations, Pittsburg, PA, Invited Talk, pp 3–10

  • Hutchins J (1999) Warren Weaver memorandum: 50th anniversary of machine translation. MT News International (22):5–6

  • Hutchins WJ (2000) Early years in machine translation: memoirs and biographies of pioneers. John Benjamins, Europe, 400 (2000)

  • Hwang M, Choi C, Kim P (2011) Automatic enrichment of semantic relation network and its application to word sense disambiguation. IEEE Trans Knowl Data Eng 23(6): 845–858

    Article  Google Scholar 

  • Ide N, Véronis J (1998) Introduction to the special issue on word sense disambiguation: the state of the art. Comput Linguist 24(1): 2–40

    Google Scholar 

  • Kilgarriff A (1997) I don’t believe in word senses. Comput Humanit 31(2): 25

    Article  Google Scholar 

  • Kilgarriff A (1998) SENSEVAL : an exercise in evaluating word sense disambiguation programs. In: Proceedings of the international conference on language resources and evaluation LREC, Granada, pp 581–588

  • KotlerMan L, Dagan I, Szpektor I, Zhitomirsky-Geffet M (2010) Directional distributional similarity for lexical inference. Nat Lang Eng 16(4): 359–389

    Article  Google Scholar 

  • Lee Y, Ng H, Chia T (2004) Supervised word sense disambiguation with support vector machines and multiple knowledge sources. In: Mihalcea R, Edmonds P, (eds), Senseval-3: third international workshop on the evaluation of systems for the semantic analysis of text, Barcelona, Spain, pp 137–140 (July 2004)

  • Li C, Sun A, Datta A (2011) A generalized method for word sense disambiguation based on wikipedia. In: Proceedings of the 33rd European conference on advances in information retrieval, Dublin, Ireland, vol 6611/2011, pp 653–664

  • Mihalcea R (2007) Using Wikipedia for automatic word sense disambiguation. In : Proceedings of the conference of the North American chapter of the association for computational linguistics (NAACL), Rochester, NY, vol 2007, pp 196–203

  • Navigli R (2009) Word sense disambiguation: a survey. ACM Comput Surv 41(2): 1–69

    Article  Google Scholar 

  • Navigli R (2010) Simone Paolo Ponzetto: knowledge-rich word sense disambiguation rivaling supervised systems. In: ACL ’10 Proceedings of the 48th annual meeting of the association for computational linguistics, Uppsala, Sweden, pp 1522–1531 (2010)

  • Navigli R, Lapata M (2010) An experimental study of graph connectivity for unsupervised word sense disambiguation. IEEE Trans Pattern Anal Mach Intell 32(4): 678–692

    Article  Google Scholar 

  • Navigli R, Velardi P (2005) Structural semantic interconnections: a knowledge-based approach to word sense disambiguation. IEEE Trans Pattern Anal Mach Intell 27(7): 1075–1086

    Article  Google Scholar 

  • Purandare A, Pedersen T (2004) Word sense discrimination by clustering contexts in vector and similarity spaces. In: Proceedings of the conference on computational natural language learning, Boston, MA, pp 41–48

  • Schwartz H, Gomez F (2009) Using web selectors for disambiguation of all words. In: Proceedings of the workshop on semantic evaluations recent achievements and future directions, Boulder, Colorado, pp 28–36

  • Schwartz H, Gomez F (2010) UCF-WS: domain word sense disambiguation using web selectors. In: SemEval ’10 Proceedings of the 5th international workshop on semantic evaluation, Strousburg, PA, USA, pp 392–395

  • Sinha R, Mihalcea R (2007) Unsupervised graph-based word sense disambiguation using measures of word semantic similarity. In: Semantic Computing, 2007. ICSC 2007, Irvine, CA, pp 363–369 (2007)

  • Stanfill C, Waltz D (1986) Toward memory-based reasoning. Commun ACM 29(12): 1213–1228

    Article  Google Scholar 

  • Stevenson M (2001) The interaction of knowledge sources in word sense disambiguation. Comput Linguistics 27(3): 321–349

    Article  Google Scholar 

  • Towell G, Voorhees E (1998) Disambiguating highly ambiguous words. Computat Linguist Special Issue Word Sense Disambiguation 24(1): 125–145

    Google Scholar 

  • Turdakov D (2010) Word sense disambiguation methods. Program Comput Softw 36(6): 309–326

    Article  Google Scholar 

  • U.S. National Library of Medicine. In: Unified Medical Language System® (UMLS®). (Accessed September 19, 2011) http://www.nlm.nih.gov/research/umls/

  • Veronis J (2004) HyperLex: lexical cartography for information retrieval. Comput Speech Lang 18(3): 223–252

    Article  Google Scholar 

  • Weeds J, Weir D, McCarthy D (2004) Characterising measures of lexical distributional similarity. In: 20th international conference on computational linguistics COLING 04, Geneva, Switzerland, pp 1015-es

  • Yuret D, Yatbaz M (2010) The noisy channel model for unsupervised word sense disambiguation. J Comput Linguist 36(1): 111–127

    Article  Google Scholar 

  • Zhou X, Han H (2005) Survey of word sense disambiguation approaches. In: Proceedings of the 18th International Florida AI Research Society Conference, 2005, pp 307–313, Clearwater Beach, Florida

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to R. V. Vidhu Bhala.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Vidhu Bhala, R.V., Abirami, S. Trends in word sense disambiguation. Artif Intell Rev 42, 159–171 (2014). https://doi.org/10.1007/s10462-012-9331-5

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10462-012-9331-5

Keywords