Applying various distance functions and feature extraction schemes to ambiguity resolution

Abstract

. Word Sense Disambiguation, which is one of the most challenging problems in the process of machine translation, can be considered as a classification problem. In this paper, we use K-Nearest-Neighbor, as one of the most popular classification methods, as well as some knowledge based resources in order to design a WSD scheme. The success of K-Nearest-Neighbor is tightly dependent on two factors; the features used to represent the context in which an ambiguous word occurs and the distance/similarity measure used for comparison of text vectors. Hence, in the present study, we focus on these two matters. For the first purpose, we extract three sets of features; syntactic features, lexical features and semantic features. In order to produce enriched and useful corpora, we apply preprocessed steps. In this work, we carry out a feature selection process as well as a feature weighting policy in order to fine-tune the classifier. For the second purpose, we try several distance/similarity metrics (rather than one metric) in order to find the most proper one. We also assign and use feature weights and propose a weighted formula for every metric. Moreover, to show that the proposed schemes are not language-dependent, we apply the suggested schemes to two sets of data; English and Persian corpora. The evaluation results, with regards to the feature selection and feature weighting strategies, show that the semantic and syntactic features have a significant effect on the classification ability of the system. The results are also encouraging compared to state of the art.

Keywords

Machine translation word sense disambiguation K-Nearest-Neighbor similarity metric distance metric feature weighting feature selection

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References

Navigli

, Word sense disambiguation: A survey, ACM Computing Surveys (CSUR) 41(2) (2009), 10.

Mihalcea

, Multilingual word sense disambiguation using wikipedia (Doctoral dissertation, University of North Texas), 2013.

Kumar

, Word Sense Disambiguation (Doctoral dissertation, The Pennsylvania State University), 2015.

Yarowsky

, Decision lists for lexical ambiguity resolution: Application to accent restoration in Spanish and French. In Proceedings of the 32nd annual meeting on Association for Computational Linguistics, Association for Computational Linguistics, 1994 June, pp. 88–95.

Parameswarappa

and Narayana

V.M.

, Kannada Word Sense Disambiguation Using Decision List, Volume, 2, 2013, pp. 272–278.

Gale

W.A.

Church

K.W.

and Yarowsky

, A method for disambiguating word senses in a large corpus, Computers and the Humanities 26(5–6), pp. 415–439.

Goutte

, Note on free lunches and cross-validation, Neural Computation 9(6) (1997), 1211–15.

Aung

N.T.T.

Soe

K.M.

and Thein

N.L.

, A Word Sense Disambiguation System Using Naïve Bayesian Algorithm for Myanmar Language, International Journal of Scientific and Engineering Research 2(9) (2011).

H.T.

and Lee

H.B.

, Integrating multiple knowledge sources to disambiguate word sense: An exemplar-based approach. In Proceedings of the 34th annual meeting on Association for Computational Linguistics, Association for Computational Linguistics, 1996 June, pp. 40–47.

10.

Escudero

Màrquez

and Rigau

, Boosting applied to word sense disambiguation, In European Conference on Machine Learning, Springer Berlin Heidelberg, 2000 May, pp. 129–141.

11.

Golding

A.R.

and Roth

, A winnow-based approach to context-sensitive spelling correction, Machine Learning 34(1–3) (1999), 107–130.

12.

Mangu

and Brill

, Automatic rule acquisition for spelling correction, In ICM 97 (1997 July), 187–194.

13.

Pedersen

, A simple approach to building ensembles of Naive Bayesian classifiers for word sense disambiguation, In Proceedings of the 1st North American chapter of the Association for Computational Linguistics conference, Association for Computational Linguistics, 2000 April, pp. 63–69.

14.

Zhong

and Ng

H.T.

,It makes sense: A wide-coverage word sense disambiguation system for free text. In Proceedings of the ACL 2010 System Demonstrations, Association for Computational Linguistics, 2010 July, pp. 78–83.

15.

Dandala

Mihalcea

and Bunescu

,Word sense disambiguation using Wikipedia, In The People’s Web Meets NLP, Springer Berlin Heidelberg, 2013, pp. 241–262.

16.

Nigam

and McCallum

A.K.

,Text Classification from Labeled and Unlabeled Documents using EM, Machine Learning 39 (2000), 103–34.

17.

Shinnou

and Sasaki

,Unsupervised learning of word sense disambiguation rules by estimating an optimum iteration number in the EM algorithm. In Proceedings of the seventh conference on Natural language learning at HLT-NAACL 2003-Volume 4, Association for Computational Linguistics, 2003 May, pp. 41–48.

18.

Agirre

Martínez

de Lacalle

O.L.

and Soroa

,Two graph-based algorithms for state-of-the-art WSD. In Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing, Association for Computational Linguistics, 2006, July, pp. 585–593.

19.

Brody

and Lapata

,March. Bayesian word sense induction. In Proceedings of the 12th Conference of the European Chapter of the Association for Computational Linguistics, Association for Computational Linguistics, 2009, pp. 103–111.

20.

Manandhar

Klapaftis

I.P.

Dligach

and Pradhan

S.S.

,SemEval-2010 task 14: Word sense induction & disambiguation, In Proceedings of the 5th international workshop on semantic evaluation, Association for Computational Linguistics, 2010 July, pp. 63–68.

21.

Di Marco

and Navigli

,Clustering and diversifying web search results with graph-based word sense induction, Computational Linguistics 39(3) (2013), 709–754.

22.

Navigli

Jurgens

and Vannella

,Semeval-2013 task 12: Multilingual word sense disambiguation. In Second Joint Conference on Lexical and Computational Semantics (* SEM), (Vol. 2), 2013 June, pp. 222–231.

23.

Chaplot

D.S.

Bhattacharyya

and Paranjape

,Unsupervised Word Sense Disambiguation Using Markov Random Field and Dependency Parser, In AAAI. 2015 January, pp. 2217–2223.

24.

Agirre

Rigau

Padro

and Atserias

,Combining supervised and unsupervised lexical knowledge methods for word sense disambiguation, Computers and the Humanities 4(1-2) (2000), 103–108.

25.

Yarowsky

,Unsupervised word sense disambiguation rivaling supervised methods. In Proceedings of the 33rd annual meeting on Association for Computational Linguistics, Association for Computational Linguistics, 1995 June, pp. 189–196.

26.

Diab

and Resnik

,An unsupervised method for word sense tagging using parallel corpora, In Proceedings of the 40th Annual Meeting on Association for Computational Linguistics, Association for Computational Linguistics, 2002 July, pp. 255–262.

27.

Mihltz

and Pohl

,Exploiting parallel corpora for supervised word-sense disambiguation in english-hungarian machine translation. In Proceedings of the 5th Conference on Language Resources and Evaluation, 2006, pp. 1294–1297.

28.

Tufiş

and Koeva

,Ontology-supported text classification based on cross-lingual word sense disambiguation, In International Workshop on Fuzzy Logic and Applications, Springer Berlin Heidelberg, 2007 July, pp. 447–455.

29.

Sarrafzadeh

Yakovets

Cercone

and An

,Cross-lingual word sense disambiguation for languages with scarce resources, In Canadian Conference on Artificial Intelligence, Springer Berlin Heidelberg, 2011 May, pp. 347–358.

30.

Banea

and Mihalcea

, Word sense disambiguation with multilingual features, In Proceedings of the Ninth International Conference on Computational Semantics, Association for Computational Linguistics, 2011 January, pp. 25–34.

31.

Lefever

, ParaSense: parallel corpora for word sense disambiguation (Doctoral dissertation, Ghent University), 2012.

32.

Fernandez-Ordonez

Mihalcea

and Hassan

,Unsupervised Word Sense Disambiguation with Multilingual Representations, In LREC, 2012 May, pp. 847–851.

33.

Dagan

and Itai

,Word sense disambiguation using a second language monolingual corpus, Computational linguistics 20(4) (1994), 563–596.

34.

Schwab

and Guillaume

,A global ant colony algorithm for word sense disambiguation based on semantic relatedness, In Highlights in Practical Applications of Agents and Multiagent Systems, Springer Berlin Heidelberg, 2011, pp. 257–264.

35.

Abualhaija

and Zimmermann

K.H.

,D-Bees: A novel method inspired by bee colony optimization for solving word sense disambiguation, Swarm and Evolutionary Computation 27 (2016), 188–195.

36.

Rezapour

Fakhrahmad

S.M.

Sadreddini

M.H.

and Jahromi

M.Z.

,An accurate word sense disambiguation system based on weighted lexical features, Literary and Linguistic Computing 29(1) (2014), 74–88.

37.

Mihalcea

and Yang

,TWA Sense Tagged Data, University of North Texas. W WW. cse. unt. edu/

\sim

rada/downloads. html, 2003.

38.

Agirre

and Edmonds

, eds. ,Word sense disambiguation: Algorithms and applications, Vol. 33, Springer Science & Business Media, 2007.

39.

Ahmed

and Nürnberger

,September. Arabic/english word translation disambiguation using parallel corpora and matching schemes, In Proceedings of EAMT (Vol. 8), 2008, p. 28.

40.

Brown

P.F.

Pietra

S.A.D.

Pietra

V.J.D.

and Mercer

R.L.

,Word-sense disambiguation using statistical methods, In Proceedings of the 29th annual meeting on Association for Computational Linguistics, Association for Computational Linguistics, 1991 June, pp. 264–270.

41.

Clough

and Stevenson

,Cross-language information retrieval using eurowordnet and word sense disambiguation, In European Conference on Information Retrieval, Springer Berlin Heidelberg, 2004 April, pp. 327–337.

42.

Diou

Katsikatsos

and Delopoulos

,December. Constructing Fuzzy Relations fromWordNet forWord Sense Disambiguation. In 2006 First International Workshop on Semantic Media Adaptation and Personalization (SMAP’06), IEEE, 2006, pp. 135–140.

43.

Fakhrahmad

S.M.

Rezapour

A.R.

Jahromi

M.Z.

and Sadreddini

M.H.

,A novel approach to machine translation: A proposed language-independent system based on deductive schemes, Iranian Journal of Science and Technology, Transactions of Electrical Engineering 38(E1) (2014), 59.

44.

Justeson

J.S.

and Katz

S.M.

,Principled disambiguation: Discriminating adjective senses with modified nouns, Computational Linguistics 21(1) (1995), 1–27.

45.

Kilgarriff

Rychly

Smrz

and Tugwell

,Itri-04-08 the sketch engine, Information Technology 105 (2004), 116.

46.

Kohonen

Barna

and Chrisley

,Statistical pattern recognition with neural networks: Benchmarking studies. In Neural Networks, IEEE International Conference on, 1988 July, pp. 61–68, IEEE.

47.

Lagarda

A.L.

Alabau

Casacuberta

Silva

and Diaz-de-Liano

, Statistical post-editing of a rule-based machine translation system, In Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics, Companion Volume: Short Papers, Association for Computational Linguistics, 2009 May, pp. 217–220.