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

TL;DR

This paper presents a system for Turkish language that extracts and classifies events based on tense, aspect, and mood, enhancing analysis of news reports for crisis management and situational awareness.

Contribution

It extends existing event extraction software with TAM analysis capabilities using a crosslinguistic semantic framework, adaptable to various languages and domains.

Findings

Extracts basic event structures from Turkish news reports.

Classifies sentences by temporal, modal, and volitional/illocutionary values.

Potential application in automated risk prevention systems.

Abstract

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 iv the theory of anchoring relations (Tem\"urc\"u, 2007, 2011) which is a crosslinguistically 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.