  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.
