Abstract
This works presents an application of semantic compression where domain frequency dictionaries are used to augment comprehension of documents. This is achieved by incorporating user’s feedback into proposed solution. Experiments and examples of actual output are given. Moreover, a measure that allows for evaluation of changes in a structure of available groups is defined and presented.
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Ceglarek, D., Haniewicz, K., Rutkowski, W.: Semantically Enchanced Intellectual Property Protection System - SEIPro2S. In: Nguyen, N.T., Kowalczyk, R., Chen, S.-M. (eds.) ICCCI 2009. LNCS, vol. 5796, pp. 449–459. Springer, Heidelberg (2009)
Ceglarek, D., Haniewicz, K., Rutkowski, W.: Semantic compression for specialised Information Retrieval systems. In: Nguyen, N.T., Katarzyniak, R., Chen, S.-M. (eds.) Advances in Intelligent Information and Database Systems. SCI, vol. 283, pp. 111–121. Springer, Heidelberg (2010)
Ceglarek, D., Haniewicz, K., Rutkowski, W.: Quality of semantic compression in classification. In: Pan, J.-S., Chen, S.-M., Nguyen, N.T. (eds.) ICCCI 2010. LNCS, vol. 6421, pp. 162–171. Springer, Heidelberg (2010)
Fellbaum, C.: WordNet - An Electronic Lexical Database. The MIT Press, USA (1998) ISBN: 978-0-262-06197-1
Percova, N.N.: On the types of semantic compression of text. In: Proc. of the 9th Conference on Computational Linguistics (COLING 1982), vol. 2, pp. 229–231. Academia Praha, Czechoslovakia (1982)
Krovetz, R., Croft, W.B.: Lexical ambiguity and information retrieval. ACM Transactions on Information Systems 10(2), 115–141 (1992)
Mihalcea, R., Csomai, A.: Senselearner: Word sense disambiguation for all words in unrestricted text. In: Proc. of the 43rd ACL, pp. 53–56 (2005)
Resnik, P.: Using information content to evaluate semantic similarity. In: Proc. of the 14th IJCAI, Canada, pp. 448–453 (1995)
Sinha, R., Mihalcea, R.: Unsupervised graphbased word sense disambiguation using measures of word semantic similarity. In: Proc. of the IEEE International Conference on Semantic Computing (2007)
Erk, K., Pado, S.: A structured vector space model for word meaning in context. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP 2008), pp. 897–906. Association for Computational Linguistics, Stroudsburg (2008)
Milkowski, M.: Automated Building of Error Corpora of Polish. In: Lewandowska-Tomaszczyk, B. (ed.) Corpus Linguistics, Computer Tools, and Applications - State of the Art. PALC 2007, Peter Lang. Internationaler Verlag der Wissenschaften, pp. 631–639 (2008)
Baeza-Yates, R.A., Ribeiro-Neto, B.: Modern Information Retrieval. Addison-Wesley Longman Publishing Co., Inc., Boston (1999)
Gordon, A.D.: Classification, 2nd edn. Chapman and Hall/CRC, USA (1999) ISBN: 978-1-58488-013-4
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Ceglarek, D., Haniewicz, K., Rutkowski, W. (2011). Domain Based Semantic Compression for Automatic Text Comprehension Augmentation and Recommendation. In: Jędrzejowicz, P., Nguyen, N.T., Hoang, K. (eds) Computational Collective Intelligence. Technologies and Applications. ICCCI 2011. Lecture Notes in Computer Science(), vol 6923. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23938-0_5
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DOI: https://doi.org/10.1007/978-3-642-23938-0_5
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