Tense, aspect and mood based event extraction for situation analysis and crisis management

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2011

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Hürriyetoğlu, Ali

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Nowadays event extraction systems mainly deal with a relatively small amount of information about temporal and modal qualifications of situations, primarily processing assertive sentences in the past tense. However, systems with a wider coverage of tense, aspect and mood can provide better analyses and can be used in a wider range of text analysis applications. This thesis develops such a system for Turkish language. This is accomplished by extending Open Source Information Mining and Analysis (OPTIMA) research group's event extraction software, by implementing appropriate extensions in the semantic representation format, by adding a partial grammar which improves the TAM (Tense, Aspect and Mood) marker, adverb analysis and matching functions of ExPRESS, and by constructing an appropriate lexicon in the standard of CORLEONE. These extensions are based on the theory of anchoring relations (Temürcü, 2007, 2011) which is a cross-linguistically applicable semantic framework for analyzing tense, aspect and mood related categories. The result is a system which can, in addition to extracting basic event structures, classify sentences given in news reports according to their temporal, modal and volitional/illocutionary values. Although the focus is on news reports of natural disasters, disease outbreaks and man-made disasters in Turkish language, the approach can be adapted to other languages, domains and genres. This event extraction and classification system, with further developments, can provide a basis for automated browsing systems for preventing environmental and humanitarian risk.

Subject Keywords

Crisis management., Language and languages.

URI

http://etd.lib.metu.edu.tr/upload/12614296/index.pdf
https://hdl.handle.net/11511/21510

Collections

Graduate School of Informatics, Thesis

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Citation Formats

A. Hürriyetoğlu, “Tense, aspect and mood based event extraction for situation analysis and crisis management,” M.S. - Master of Science, Middle East Technical University, 2011.