Abstract
Keyword is the important item in the document that provides efficient access to the content of a document. It can be used to search for information or to decide whether to read a document. This paper mainly focuses on extracting hidden topics from meeting transcripts. Existing system is handled with web documents, but this proposed framework focuses on solving Synonym, Homonym, Hyponymy and Polysemy problems in meeting transcripts. Synonym problem means different words having similar meaning are grouped and single keyword is extracted. Hyponymy problem means one word denoting subclass is considered and super class keyword is extracted. Homonym means a word can have two or more different meanings. For example, Left might appear in two different contexts: Car left (past tense of leave) and Left side (Opposite of right). A polysemy means word with different, but related senses. For example, count has different related meanings: to say number in right order, to calculate. Hidden topics from meeting transcripts can be found using LDA model. Finally MaxEnt classifier is used for extracting keywords and topics which will be used for information retrieval.
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References
Liu, F., Pennell, D., Liu, F.: Unsupervised Approaches for Automatic keyword extraction, Boulder, Colorado. ACM (June 2009)
Phan, X.-H., Nguyen, C.-T., Le, D.-T., Nguyen, L.-M.: A Hidden Topic-Based Framework toward Building Applications with Short Web Documents. IEEE Transactions on Knowledge and Data Engineering 23 (2011)
Manning, C.D., Raghavan, P., Schutze, H.: Introduction to Information Retrieval. Cambridge Univ. Press, Springer (2008)
Deerwester, S., Furnas, G., Landauer, T.: Indexing by Latent Semantic Analysis. J. Am. Soc. for Information Science 41(6), 391–407 (1990)
Letsche, T.A., Berry, M.W.: Large-Scale Information Retrieval with Latent Semantic Indexing. Information Science 100(1-4), 105–137 (1997)
Baker, L., McCallum, A.: Distributional Clustering of Words for Text Classification. In: Proc. ACM SIGIR (1998)
Bekkerman, R., El-Yaniv, R., Tishby, N., Winter, Y.: Distributional Word Clusters vs. Words for Text Categorization. Machine Learning Research 3, 1183–1208 (2003)
Dhillon, I., Modha, D.: Concept Decompositions for Large Sparse Text Data Using Clustering. Machine Learning 42(1/2), 143–175 (2001)
Metzler, D., Dumais, S., Meek, C.: Similarity Measures for Short Segments of Text. In: Proc. 29th European Conference IR Research, ECIR 2007. ACM (2007)
Yih, W., Meek, C.: Improving Similarity Measures for Short Segments of Text. In: Proc. 22nd National Conference on Artificial Intelligence, AAAI (2007)
Sahami, M., Heilman, T.: A Web-Based Kernel Function for Measuring the Similarity of Short Text Snippets. In: Proc. 15th International Conference on World Wide Web. ACM (2006)
Gabrilovich, E., Markovitch, S.: Computing Semantic Relatedness Using Wikipedia-Based Explicit Semantic Analysis. In: Proc. 20th Int’l Joint Conference, Artificial Intelligence (2007)
Cai, L., Hofmann, T.: Text Categorization by Boosting Automatically Extracted Concepts. In: Proc. ACM SIGIR (2003)
Cai, J., Lee, W., The, Y.: Improving WSD Using Topic Features. In: Proc. Joint Conf. Empirical Methods in Natural Language Processing and Computational Natural Language Learning, EMNLPCoNLL, Prague, pp. 1015–1023 (June 2007)
Term frequency-inverse document frequency, http://www.wikipedia.com/
http://www.buzzle.com/articles/lexical-relations-hyponymy-and-homonymy.html
http://umass.academia.edu/AndrewMcCallum/Papers/49541/Using_Maximum_Entropy_for_Text_Classification
Gibb Sampling Algorithm, http://www.wikipedia.com/
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Sheeba, J.I., Vivekanandan, K., Sabitha, G., Padmavathi, P. (2013). Unsupervised Hidden Topic Framework for Extracting Keywords (Synonym, Homonym, Hyponymy and Polysemy) and Topics in Meeting Transcripts. In: Meghanathan, N., Nagamalai, D., Chaki, N. (eds) Advances in Computing and Information Technology. Advances in Intelligent Systems and Computing, vol 177. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31552-7_32
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DOI: https://doi.org/10.1007/978-3-642-31552-7_32
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