Sense Disambiguation of English Simple Prepositions in the Context of English–Hindi Machine Translation System

  • D. Jyothi Ratnam
  • M. Anand Kumar
  • B. Premjith
  • K. P. Soman
  • S. Rajendran
Chapter

Abstract

In the context of developing a Machine Translation System, the identification of the correct sense of each and every word in the document to be translated is extremely important. Adpositons play a vital role in the determination of the sense of a particular word in a sentence as they link NPs with the VPs. In the context of developing English to Hindi Machine Translation system, the transfer of the senses of each Preposition into the target langue needs done with much attention. The linguistic and grammatical role of a preposition is to express a variety of syntactic and semantic relationships between nouns, verbs, adjectives, and adverbs. Here we have selected the most important and most frequently used English simple prepositions such as ‘at’, ‘by’, ‘from’, ‘for’, ‘in’, ‘of’, ‘on’, ‘to’ and ‘with’ for the sake of contrast. A supervised machine learning approach called Support Vector Machine (SVM) is used for disambiguating the senses of the simple preposition ‘at’ in contrast with Hindi postpositions.

Keywords

Prepositions Postpositions Support Vector Machine Word embedding 

References

  1. 1.
    Alam Y (2004). Decision Trees for Sense Disambiguation of Preposition. Case over. In HLT-NAACL, Computational Lexical Semantics Workshop, Boston: MA, pp 52–59.Google Scholar
  2. 2.
    Anand Kumar, M., Rajendran, S., & Soman, K. P. (2015). Cross-lingual preposition disambiguation for machine translation. Procedia Computer Science, 54, 291–300.CrossRefGoogle Scholar
  3. 3.
    Aravind, A., & Anand Kumar, M. (2014). Machine Learning approach for correcting preposition errors using SVD features. 2014 International conference on Advances in Computing, Communications and informatics (ICACCI), New Delhi, India, 359–376.Google Scholar
  4. 4.
    Downing, A., & Locke, P. (2002). A university course in English grammar. New Fetter Lane, London: Routledge, 11, 590–601.Google Scholar
  5. 5.
    Baldwin, T. V., & Kordoni, A. Villavicencio. (2009). Prepositions in applications. A survey and introduction to the special issue. Computational Linguistics, 35(2), 119–149.CrossRefGoogle Scholar
  6. 6.
    Bannard, C., & Baldwin, T. (2003). Distributional Models of Preposition Semantics. ACL-SIGSEM, Workshop on the Linguistic Dimensions of Prepositions and Their Use in Computational Linguistics Formalism and Applications, Toulouse: France, pp 169–180.Google Scholar
  7. 7.
    Beth, L. (1993). English verb classes and alternations a preliminary investigation (p. 201). Chicago, IL: University of Chicago press.Google Scholar
  8. 8.
    Bojanowski, P., et al. (2016). Enriching word vectors with sub word information arXiv preprint arXiv: 1607.0460.Google Scholar
  9. 9.
    Dorr, B. (1992). The use of lexical semantics in interlingual machine translation. Machine Translation, 7(3), 135–193.CrossRefGoogle Scholar
  10. 10.
    Harabagiu, S. (1996). An application of word net to prepositional attachment (pp. 360–363). Santa Cruz: ACL.Google Scholar
  11. 11.
    Hovy, D., Tratz, S., & Hovy, E. (2010). What’s in a preposition? Dimensions of sense disambiguation for an interesting word class. In Coling 2010: Posters, (pp. 454–462). Beijing, China, August. Coling 2010 Organizing Committee.Google Scholar
  12. 12.
    Joachims, T. (1999). Transductive inference for text classification using support vector machines. In International Conference on Machine Learning (ICML).Google Scholar
  13. 13.
    Kamakshi, S., & Rajendrarn, S. (2008). Preliminaries to the Preparation of a machine aid to translate linguistic texts written in English into Tamil. DLA publications.Google Scholar
  14. 14.
    Kamata Prasad Guru (1992). Hindi Vyakaran. Nagaripracharinisabha, Varanasi, India, 359-376.Google Scholar
  15. 15.
    Litkowski, K. (2002). Digraph analysis of dictionary preposition definition. In ACL-SIGLEX, SENSEVAL Workshop on Word Sense Disambiguation: Recent Success and Future Directions, Philadelphia: PA, pp 9–16.Google Scholar
  16. 16.
    Litkowski, K., & Hargraves, O. (2007). SemEval-2007 task 06: word-sense disambiguation of prepositions. In Proceedings of the 4th International Workshop on Semantic Evaluations (SemEval-2007), Prague, Czech Republic.Google Scholar
  17. 17.
    O’Hara, T. J. Wiebe. (2002). Classifying preposition semantic roles using class-based Lexical Associations Technical Report NMSU-CS-2002- 013. Computer Science Department: New Mexico State University.Google Scholar
  18. 18.
    O’Hara, T., & Wiebe, J. (2003). Preposition semantic classification via Penn Treebank and FrameNet. In Proceedings of CoNLL (pp. 79–86).Google Scholar
  19. 19.
    O’Hara, T. J. Wiebe. (2009). Exploiting semantic role resources for preposition disambiguation. Computational Linguistics, 35(2), 151–184.CrossRefGoogle Scholar
  20. 20.
    Mamidi, R. (2004). Disambiguating prepositions for machine translation using lexical semantical resources. In National Seminar on Theoretical and Applied Aspect of Lexical Semantics. Center of Advanced Study in Linguistics, Osmania University, Hyderabad.Google Scholar
  21. 21.
    Rahimi, A., & Recht, B. (2007). Random features for large-scale kernel machines. In NIPS (Vol. 4).Google Scholar
  22. 22.
    Rudin, Walter (2011). Fourier Analysis On Groups. John Wiley& Sons.Google Scholar
  23. 23.
    Rudzicz, F., & Mokhov, S. A. (2003). Towards a heuristic categorization of prepositional phrases in English with word net. Technical report, Cornell University.Google Scholar
  24. 24.
    Saint-Dizier, P., & Vazquez, G. (2001). A compositional framework for prepositions. In ACLSIGSEM, International Workshop on Computational Semantic. Tilburg: Netherlands.Google Scholar
  25. 25.
    Sablayrolles, P. (1995). The semantics of motion. EACL (pp. 281–283). France: Toulouse.Google Scholar
  26. 26.
    Husain, S., Sharma, D. M., Reddy, M. (2007). Simple Preposition Correspondence: A problem in English to Indian language machine Translation. Language Technologies research Centre, IIIT, Hydrabed, India.Google Scholar
  27. 27.
    Schölkopf, B., & Smola, A. J. (2002). Learning with kernels: support vector machines, regularization, optimization, and beyond. Cambridge, MA: MIT press.Google Scholar
  28. 28.
    Stephen, T., & Dirk, H. (2009). Disambiguation of preposition sense using linguistically motivated features. In Proceedings of the NAACL HLT Student Research Workshop and Doctroal Consortium (pp. 96–100). Boulder, Colarado.Google Scholar
  29. 29.
    Soman, K. P., Loganathan, R., & Ajay, V. (2009). Machine learning with SVM and other kernel methods. New Delhi: PHI Learning Pvt. Ltd.Google Scholar
  30. 30.
    Sopena, J. A. L., & Loberas, J. Moliner. (1998). A connectionist approach to prepositional phrase attachment for real world text. ACL, Montreal (pp. 1233–1237). Canada: Quebec.Google Scholar
  31. 31.
    Tratz, S., & Hovy, D. (2009). Disambiguation of preposition sense using linguistically motivated features. In Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics (pp. 96–100).Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  • D. Jyothi Ratnam
    • 1
    • 2
  • M. Anand Kumar
    • 1
    • 2
  • B. Premjith
    • 1
    • 2
  • K. P. Soman
    • 1
    • 2
  • S. Rajendran
    • 1
    • 2
  1. 1.Center for Computational Engineering and Networking (CEN), Amrita School of EngineeringCoimbatoreIndia
  2. 2.Amrita Vishwa VidyapeethamCoimbatoreIndia

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