Amita Jain
Skip Abstract Section
Abstract
In this article, we propose Fuzzy Hindi WordNet, which is an extended version of Hindi WordNet. The proposed idea of fuzzy relations and their role in modeling Fuzzy Hindi WordNet is explained. We mathematically define fuzzy relations and the composition of these fuzzy relations for this extended version. We show that the concept of composition of fuzzy relations can be used to infer a relation between two words that otherwise are not directly related in Hindi WordNet. Then we propose fuzzy graph connectivity measures that include both local and global measures. These measures are used in determining the significance of a concept (which is represented as a vertex in the fuzzy graph) in a specific context. Finally, we show how these extended measures solve the problem of word sense disambiguation (WSD) effectively, which is useful in many natural language processing applications to improve their performance. Experiments on standard sense tagged corpus for WSD show better results when Fuzzy Hindi WordNet is used in place of Hindi WordNet.
- N. Alon and A. Orlitsky. 1996. Source coding and graph entropies. IEEE Transactions on Information Theory 42, 5, 1329--1339. Google Scholar
Digital Library
- K. Avneet. 2010. Development of an approach for disambiguating ambiguous Hindi post position. International Journal on Computer Applications 5, 9, 25--32.Google ScholarCross Ref
- S. Banerjee and T. Pedersen. 2002. An adapted Lesk algorithm for word sense disambiguation using WordNet. In Computational Linguistics and Intelligent Text Processing. Lecture Notes in Computer Science, Vol. 2276. Springer, 136--145. Google ScholarDigital Library
- J. C. Bezdek, G. Biswas, and L. Huang. 1986. Transitive closures of fuzzy thesauri for information-retrieval systems. International Journal of Man-Machine Studies 25, 343--356. Google ScholarDigital Library
- P. Bhattacharya. 1987. Some remarks on fuzzy graphs. Pattern Recognition Letters 6, 5, 297--302. Google ScholarDigital Library
- P. Bhattacharyya. 2010. IndoWordNet. In Proceedings of the Lexical Resources Engineering Conference.Google Scholar
- P. P. Bonissone and S. D. Keith. 1986. Selecting uncertainty calculi and granularity: An experiment in trading-off precision and complexity. Machine Intelligence Pattern Recognition 4, 17--247.Google Scholar
- S. P. Borgatti. 2006. Identifying set of key players in a social network. In Computational and Mathematical Organization Theory. Springer, 21--34. Google ScholarDigital Library
- R. A. Botafogo, E. Rivlin, and B. Shneiderman. 1992. Structural analysis of hypertexts: Identifying hierarchies and useful metrics. ACM Transactions on Information Systems 10, 2, 142--180. Google ScholarDigital Library
- U. Brandes. 2001. A faster algorithm for betweenness centrality. Journal of Mathematical Sociology 25, 2, 163--177.Google ScholarCross Ref
- Center for Indian Language Technology. 2010. Hindi Corpus. Retrieved October 20, 2015, from http://www.cfilt.iitb.ac.in/Downloads.html.Google Scholar
- S. M. Chen, Y. J. Horng, and C. H. Lee. 2001. Document retrieval using fuzzy valued concept network. IEEE Transactions on Systems, Man and Cybernetics 31, 1, 111--118. Google ScholarDigital Library
- S. M. Chen, Y. J. Horng, and C. H. Lee. 2003. Fuzzy information retrieval based on multi relationship fuzzy concept network. Fuzzy Sets and Systems 140, 1, 183--205.Google ScholarCross Ref
- M. D. Cock, S. Guadarrama, and M. Nikravesh. 2005. Fuzzy thesauri for and from the WWW. In Soft Computing for Information Processing and Analysis. Studies in Fuzziness and Soft Computing, Vol. 164. Springer, 1--10.Google Scholar
- S. Das, A. Seetha, M. Kumar, and J. L. Rana. 2010. Post translation query expansion using Hindi Word-Net for English-Hindi CLIR system. In Proceedings of the Forum for Information Retrieval Evaluation (FIRE’10).Google Scholar
- C. Diou, G. Katsikatsos, and A. Delopoulos. 2006. Constructing fuzzy relations from WordNet for word sense disambiguation. In Proceedings of the 1st Workshop on Semantic Media Adaptation and Personalization. 135--140. Google ScholarDigital Library
- S. K. Dwivedi and P. Rastogi. 2008. An entropy based method for removing Web query ambiguity in Hindi language. Journal of Computer Science 4, 9, 762--767.Google ScholarCross Ref
- K. Erk, D. McCarthy, and N. Gaylord. 2013. Measuring word meaning in context. Computational Linguistics 39, 3, 511--554.Google ScholarCross Ref
- A. Esuli and S. Fabrizio. 2006. SENTIWORDNET: A publically available lexical resource for opinion mining. In Proceedings of the 5th Conference on Language Resources and Evaluation (LREC’06). 417--422.Google Scholar
- C. Fellbaum. 1998. WordNet: An Electronic Lexical Database. MIT Press, Cambridge, MA.Google Scholar
- L. C. Freeman. 1979. Centrality in social networks conceptual clarification. Social Networks 1, 3, 215--239.Google ScholarCross Ref
- L. C. Freeman, S. P. Boragatti, and D. R. White. 1991. Centrality in valued graph: A measure of betweenness based on network flow. Social Networks 13, 141--154.Google ScholarCross Ref
- S. Fujita and A. Fujino. 2013. Word sense disambiguation by combining labeled data expansion and semisupervised learning method. ACM Transactions on Asian Language Information Processing 12, 2, Article No. 7. Google ScholarDigital Library
- G. K. Gupta. 2006. Introduction to Data Mining with Case Studies. Prentice Hall of India Pvt. Ltd., 238--240.Google Scholar
- A. Jain, K. Mittal, and D. K. Tayal. 2014a. Automatically incorporating context meaning for query expansion using graph connectivity measures. Progress in Artificial Intelligence 2, 2--3, 129--139.Google ScholarCross Ref
- A. Jain, D. K. Tayal, and S. Rai. 2014b. Shrinking digital gap through automatic generation of WordNet for Indian languages. Open Forum 30, 2, 215--222. DOI:10.1007/s00146-014-0548-5 Google ScholarDigital Library
- A. Jain, D. K. Tayal, and S. Yadav. 2014c. Retrieving Web search results for a Hindi query using max-max soft clustering. International Journal of System Assurance Engineering and Management, 1--12.Google Scholar
- A. Jain, S. Yadav, and D. Tayal. 2013. Measuring context meaning for open class words in Hindi language. In Proceedings of the 6th International Conference on Contemporary Computing (IC3). 118--123.Google Scholar
- D. B. Johnson. 1977. Efficient algorithms for shortest paths in sparse networks. Journal of the ACM 24, 1--13. Google ScholarDigital Library
- J. M. Kleinberg. 1999. Authoritative sources in a hyperlinked environment. Journal of the ACM 46, 604--632. Google ScholarDigital Library
- G. J. Klir and T. A. Folger. 1988. Fuzzy Sets, Uncertainty and Information. Prentice Hall. Google ScholarDigital Library
- G. K. Kotle and S. C. Bhairal. 2009a. Exploring links in WordNet hierarchy for word sense disambiguation of nouns. In Proceedings of the International Conference on Advances in Computing, Communication, and Control. 20--25. Google ScholarDigital Library
- G. K. Kotle and S. C. Bhairal. 2009b. WordNet: A knowledge source for word sense disambiguation. International Journal of Recent Trends in Engineering 2, 4, 213--217.Google Scholar
- R. Kumar and G. N. Jha. 2010. Translating politeness across cultures: Case of Hindi and English. In Proceedings of the 3rd International Conference on Intercultural Collaboration. ACM, New York, NY, 175--178. Google ScholarDigital Library
- S. Kumar and V. Mansotra. 2012. Query optimization: A solution for low recall problem in Hindi language information retrieval. International Journal of Computer Applications 55, 17, 6--17.Google ScholarCross Ref
- K. H. Lee. 2005. First Course on Fuzzy Theory and Applications. Springer-Verlag, Berlin. Google ScholarDigital Library
- B. Luca and C. Carlos. 2006. The distribution of pagerank follows a power-law only for particular values of the damping factor. In Proceedings of the 15th International Conference on World Wide Web. ACM, New York, NY, 941--942. Google ScholarDigital Library
- K. Martin. 1992. A fuzzy concept network model and its application. In Proceedings of 1st IEEE International Conference on Fuzzy Systems.Google Scholar
- S. Mathew and M. S. Sunitha. 2009. Types of arcs in a fuzzy graph. Information Sciences 179, 1760--1768. Google ScholarDigital Library
- R. Mihalcea. 2005. Unsupervised large vocabulary word sense disambiguation with graph based algorithm for sequence data labelling. In Proceedings of the Human Language Technology Conference and the Conference on Empirical Methods in Natural Language Processing (HLT/EMNLP’09). 411--418. Google ScholarDigital Library
- R. Mihalcea and R. Dragmoir. 2011. Graph-Based Natural Language Processing. Cambridge University Press. Google ScholarDigital Library
- G. A. Miller and C. Fellbaum. 2007. WordNet then and now. Language Resources and Evaluation 41, 2, 209--214.Google ScholarCross Ref
- N. Mishra, S. Yadav, and T. J. Siddiqui. 2009. An unsupervised approach to Hindi word sense disambiguation. In Proceedings of the 1st International Conference on Intelligent Human Computer Interaction (IHCI’09). 327--335.Google Scholar
- D. Narayan, D. Chakrabarty, P. Pande, and P. Bhattacharyya. 2002. An experience in building the Indo WordNet: A WordNet for Hindi. In Proceedings of the 1st International Conference on Global WordNet.Google Scholar
- R. Navigli. 2005. Semiautomatic extension of large scale linguistic knowledge bases. In Proceedings of the 18th Florida Artificial Intelligence Research Society Conference. 548--553.Google Scholar
- R. Navigli. 2009. Word sense disambiguation: A survey. ACM Computing Surveys 41, 2, 1--69. Google ScholarDigital Library
- R. Navigli and M. Lapata. 2010. An experimental study of graph connectivity for unsupervised word sense disambiguation. IEEE Transactions on Pattern Analysis and Machine Intelligence 32, 4, 678--692. Google ScholarDigital Library
- M. E. J. Newman. 2005. A measure of betweenness centrality based on random walks. arXiv:cond-mat/0309045.Google Scholar
- T. Opsahl, F. Agneessens, and J. Skvoretz. 2010. Node centrality in weighted network generalising degree and shortest path. Social Networks 32, 3, 245--251.Google ScholarCross Ref
- S. P. Ponzetto and R. Navigli. 2010. Knowledge-rich word sense disambiguation rivalling supervised systems. In Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics. 1522--1531. Google ScholarDigital Library
- F. P. Romero, P. Julian-Iranzo, A. Soto, M. Ferreira-Satler, and J. Gallardo-Casero. 2012. Classifying unlabeled short texts using a fuzzy declarative approach. Language Resources and Evaluation 47, 1, 151--178. Google ScholarDigital Library
- A. Rosenfeld, L. A. Zadeh, K. S. Fu, K. Tanaka, and M. Shimura. 1975. Fuzzy Sets and Their Applications to Cognitive and Decision Processes. Academic, New York, NY, 77--97.Google Scholar
- K. V. Sandeep and K. V. Chanchal. 2012. A graph based word sense disambiguation for Hindi language. International Journal of Scientific Research Engineering and Technology 1, 5, 313--318.Google Scholar
- A. Sharan, M. L. Joshi, and A. Pandey. 2011. Exploiting ontology for concept based information retrieval. In Information Systems for Indian Languages. Springer, Berlin, 157--164.Google Scholar
- V. Sheinman, C. Fellbaum, I. Julien, P. Schulam, and T. Tokunaga. 2013. Erratum to: Large, huge or gigantic? Identifying and encoding intensity relations among adjectives in WordNet. Language Resources and Evaluation 47, 3, 817--818. Google ScholarDigital Library
- M. Sinha, M. K. Reddy, R. P. Bhattacharya, P. Pandey, and L. Kashyap. 2004. Hindi word sense disambiguation. In Proceedings of the International Symposium on Machine Translation, Natural Language Processing, and Translation Support Systems.Google Scholar
- S. Sinha and T. J. Siddiqui. 2012. Evaluating effect of context window size, stemming and stop word removal on Hindi word sense disambiguation. In Proceedings of the Information Retrieval and Knowledge Management International Conference (CAMP’12). IEEE, Los Alamitos, CA, 1--5.Google Scholar
- M. S. Sunitha. 2001. Studies on Fuzzy Graph. Ph.D. Dissertation. Cochin University of Science and Technology, Cochin, India.Google Scholar
- D. K. Tayal, P. C. Saxena, A. Sharma, G. Khanna, and S. Guptz. 2014. New method for solving reviewer assignment problem using type-2 fuzzy sets and fuzzy functions. Applied Intelligence 40, 1, 54--73. Google ScholarDigital Library
- T. Upstill, N. Craswell, and D. Hawking. 2003. Predicting fame and fortune: PageRank or indegree? In Proceedings of the 8th Australasian Document Computing Symposium. 31--40.Google Scholar
- D. Widdow and B. Dorow. 2002. A graph model for unsupervised lexical acquisition. In Proceedings of the 19th International Conference on Computational Linguistics. 1--7. Google ScholarDigital Library
- R. R. Yager. 2010. Concept representation and database structure in fuzzy social relational networks. IEEE Transactions on Systems, Man and Cybernetics—Part A: Systems and Humans 40, 2, 413--419. Google ScholarDigital Library
- J. Yen and R. Langari. 2005. Fuzzy Logic Intelligence, Control and Information. Pearson Education. Google ScholarDigital Library
- L. A. Zadeh. 1965. Fuzzy sets. Information and Control 8, 3, 338--353.Google ScholarCross Ref
- L. A. Zedeh, K. S. Fu, and M. Shimura. 1975. Fuzzy relations, fuzzy graphs, and their applications to clustering analysis. In Fuzzy Sets and Their Application to Cognitive and Decision Processes. Academic Press, New York, NY, 125.Google Scholar
Index Terms
- Fuzzy Hindi WordNet and Word Sense Disambiguation Using Fuzzy Graph Connectivity Measures
Recommendations
Word Sense Disambiguation using Cooperative Game Theory and Fuzzy Hindi WordNet based on ConceptNet
Natural Language is fuzzy in nature. The fuzziness of Hindi language was captured in the Fuzzy Hindi WordNet (FHWN). FHWN assigned membership values to fuzzy relationships by consulting experts from various domains. However, these membership values need ...
Hindi Word Sense Disambiguation Using Lesk Approach on Bigram and Trigram Words
AICTC '16: Proceedings of the International Conference on Advances in Information Communication Technology & ComputingWord Sense Disambiguation (WSD) is a vital task which provides the definition of particular words according to their sense or according to given context. Lesk algorithm is originally based on the gloss overlap that can be observed as the measure, ...
Automatic Construction of Interval-Valued Fuzzy Hindi WordNet using Lexico-Syntactic Patterns and Word Embeddings
A computational lexicon is the backbone of any language processing system. It helps computers to understand the language complexity as a human does by inculcating words and their semantic associations. Manually constructed famous Hindi WordNet (HWN) ...
Comments