By Yorick Wilks (auth.), Maria Teresa Pazienza (eds.)
Information extraction (IE) is a brand new know-how permitting appropriate content material to be extracted from textual details to be had electronically. IE primarily builds on ordinary language processing and computational linguistics, however it can be heavily on the topic of the good proven sector of data retrieval and comprises studying. In live performance with different promising and rising info engineering applied sciences like information mining, clever information research, and textual content summarization, IE will play an important function for scientists and execs in addition to different end-users who've to accommodate colossal quantities of data, for instance from the web. because the first publication exclusively dedicated to IE, it really is of relevance to anyone drawn to new and rising tendencies in info processing technology.
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Additional info for Information Extraction A Multidisciplinary Approach to an Emerging Information Technology: International Summer School, SCIE-97 Frascati, Italy, July 14–18, 1997
However, if, for a particular multilinguat IE task, such a concept is included in a domain model, languagespecific rules can be used to map phrases to single concepts where necessary. It is tempting to presume that texical gaps will be rare, since the domain model will generally define an area of interest which has much in common across all the languages involved, but they will occur. In the AVENTINUS drug domain, for example, the domain model may include a drug concept node for the English word c r a c k , but pointers to the appropriate entries in the lexicons of non-English languages may not exist.
G. full analysis of quantifier scope and word sense disambiguation. , Gianluigi Ferrero a assistd i~ la rgunion annuelle de Vereom Corp fi Londres/ Gianluigi Ferrero attended the annual meeting of Vercom Corp in London: assister(el), tense(el,past), voice(el,active), isubj(el,e2), lobj(el,e3), name(e2,'Gianluigi Ferrero'), reunion(e3), number(e3,sing), name(e5,'Vercom Corp'), organisation(eS), compagnie(e5), name(e6,'Londres'), lieu(e6), ville(e6) attend(el), tense(el,past), voice(el,active), lsub3(el,e2), lobj(el,e3), name(e2,'Gianluigi Ferrero' meeting(e3), number(e3,sing), name(e5,'Vercom Corp'), organisation(e5), company(e5), name(e6,'Londres'), location(e6), city(e6) Syntactically, QLF expressions are simply conjunctions of first order logical terms.
Domain specific patterns like < company_name > joint venture < company_name > (1) are widely used to capture coarse relationships in the text, able to trigger the scenario recognition. This source of information is clearly lexical according to . Besides a variety of considerations related to design, reusability and acquisition of such lexical information, it should be stressed here that a considerable improvement in performances (6 point increase of the MUC F's metrics) is reported, despite the relative coarse grain of the representation.