Information Extraction A Multidisciplinary Approach to an by Yorick Wilks (auth.), Maria Teresa Pazienza (eds.)

By Yorick Wilks (auth.), Maria Teresa Pazienza (eds.)

Information extraction (IE) is a brand new expertise allowing suitable content material to be extracted from textual details to be had electronically. IE basically builds on common language processing and computational linguistics, however it can also be heavily with regards to the good demonstrated quarter of knowledge retrieval and includes studying. In live performance with different promising and rising details engineering applied sciences like information mining, clever information research, and textual content summarization, IE will play an important position for scientists and execs in addition to different end-users who've to accommodate big quantities of data, for instance from the web. because the first e-book exclusively dedicated to IE, it really is of relevance to anyone drawn to new and rising developments in details processing technology.

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Additional resources for Information Extraction A Multidisciplinary Approach to an Emerging Information Technology: International Summer School, SCIE-97 Frascati, Italy, July 14–18, 1997

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18. G. A. ). WordNet: An on-line lexical database. International Journal of Lexicography, 3(4):235-312, 1990. 19. SPARKLE: Shallow parsing and knowledge extraction for language engineering. html. Site visited 10/06/97. 20. TREE: Trans European Employment. html. Site visited 29/05/97. 21. Y. Wilks and M. Stevenson. Sense tagging: Semantic tagging with a lexicon. In Proceedings of the ANLP97 Workshop on Tagging Text with Lexical Semantics, 1997. 22. D. Yarowsky. Word-sense disambiguation using statistical models of Roget's categories trained on large corpora.

Of course this is a significant problem for natural language generation systems in general ([14]), usually requiring reference to features of the discourse context and user model to resolve. For example, in the domain model for the m a n a g e m e n t succession task, the English lexical entries for 'chief executive officer' and ' C E O ' both have pointers to the same node. The current system just selects the first pointer found, and for the simple summaries produced at present this is usually adequate.

Many problems depend on the crude fact that heterogeneous formats of dictionaries (even of well established ones) pose hard problems to the induction process (see for example, Byrd et al in [27]). Complex extraction processes are required to map the dictionary content to specific lexical data structures (better suited for computational tasks). Such database oriented approaches aim to optimize the interface between a NLP system and the dictionary as it is (or as it has been filled in by humans).

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