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By Marie-Francine Moens
Info extraction regards the techniques of structuring and mixing content material that's explicitly said or implied in a single or a number of unstructured details assets. It includes a semantic type and linking of sure items of knowledge and is taken into account as a mild kind of content material knowing by way of the computer. at present, there's a significant curiosity in integrating the result of info extraction in retrieval structures, a result of growing to be call for for se's that go back unique solutions to versatile details queries. complicated retrieval versions fulfill that desire they usually depend upon instruments that instantly construct a probabilistic version of the content material of a (multi-media) document.The booklet specializes in content material popularity in textual content. It elaborates at the earlier and present so much profitable algorithms and their program in quite a few domain names (e.g., information filtering, mining of biomedical textual content, intelligence amassing, aggressive intelligence, felony info looking out, and processing of casual text). a massive half discusses present statistical and desktop studying algorithms for info detection and type, and integrates their leads to probabilistic retrieval versions. The publication additionally finds a few rules in the direction of a sophisticated figuring out and synthesis of text. The booklet is geared toward researchers and software program builders drawn to details extraction and retrieval, however the many illustrations and genuine global examples make it additionally compatible as a guide for college kids.
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Additional info for Information Extraction: Algorithms and Prospects in a Retrieval Context: Algorithms and Prospects in a Retrieval Context
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Amsterdam, The Netherlands: John Benjamins. Church, Kenneth (1988). A stochastic parts program and noun phrase parser for unrestricted texts. In Proceedings of the Second Conference on Applied Natural Language Processing. Austin, Texas. DeJong, Gerald (1977). Skimming newspaper stories by computer. In Proceedings of the 5th International Joint Conference on Artificial Intelligence (p. 16). Cambridge, MA: William Kaufmann. DeJong, Gerald (1982). An overview of the FRUMP system. In Wendy G. Lehnert and Martin H.
The linguistic contexts used in the classification and the eventual goals of the tasks differ. The linguistic context enlarges as the goal of the understanding grows in scope. The above extraction tasks are rather domain independent. , time, location). 1. Examples of information extraction tasks, their respective extraction units and linguistic contexts, and the eventual goal of the information extraction. Information Extraction task Named entity recognition Extraction unit Linguistic context Eventual goal Word/ Word group Sentence/text Entity understanding Noun phrase coreference resolution Word/ Word group Semantic role recognition Word/ Word group Entity relation recognition Words/ Word groups Sentence/text/ Timeline extraction Words/ Word groups Sentence/text/ Sentence/text/ Multiple texts Sentence multiple texts multiple texts Entity understanding Sentence understanding (Multi-text) discourse/story understanding (Multi-text) discourse/story understanding of a disease of a patient).
Charles J. Fillmore and John B. Lowe (1998). The Berkeley FrameNet project. In Proceedings of the COLING-ACL ’98 Joint Conference (pp. 86-90). San Francisco, CA: Morgan Kaufmann. Butler, Christopher S. (2003). Structure and Function: A Guide to Three Major Structural-Functional Theories. Amsterdam, The Netherlands: John Benjamins. Church, Kenneth (1988). A stochastic parts program and noun phrase parser for unrestricted texts. In Proceedings of the Second Conference on Applied Natural Language Processing.