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 know-how permitting suitable content material to be extracted from textual info on hand electronically. IE primarily builds on traditional language processing and computational linguistics, however it is usually heavily concerning the good verified region of data retrieval and consists of 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 a very important position for scientists and pros in addition to different end-users who've to house mammoth quantities of knowledge, for instance from the net. because the first booklet exclusively dedicated to IE, it's 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

Sample text

So both the sentences Fr: Gianluigi Ferrero a assistd fi la rdunion annuelle de Vercom Corp & Londres. En: Gianluigi Ferrero attended the annual meeting of Vercom Corp in London. should give rise to the same template fill: := ORGANISATION: 'Vercom Corp' LOCATION: 'London' TYPE: 'annual meeting' PRESENT: := NAME: ORGANISATION: 'Gianluig± Ferrero' UNCLEAR We see three main ways of addressing the problem: 31 Language I I Syntactic/Semantic Analysis Language 2 Syntactic/Semantic Analysis , .

Some instances may be mentioned in the QLF representation without an explicit class, but a class may be inferrable from the instance's attributes° For instance, an attribute of the t i t l e attribute node in the domain model may specify that all instances with this attribute must be instances of the p e r s o n class, so that, for example, p e r s o n ( e 2 ) can be inferred from t i t l e (e2, Mr. ). Further, in our example, e2 is the l s u b j of e l , and the l s u b j _ t y p e attribute of e l ' s class ( a t t e n d ) indicates that the subject must be of the class n030 (person).

However, as the examples in [16] pertaining to Japanese and English name translation demonstrate, this simple solution will not always be acceptable. Once the QLF representation has been added to the discourse model, the further stages of discourse processing take place within the same languageindependent representation: presuppositions concerning role players in events are processed to allow missing verbal or prepositional complements to be identified; coreference is carried out to merge instances; inferences are performed to 37 a t t e m p t to derive template-specific information.

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