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Motivation
Thanks to both statistical approaches and
finite state methods,
natural language processing (NLP), particularly in the area of robust,
open-domain text processing, has made considerable progress in the
last couple of decades. It is probably fair to say that NLP tools have
reached satisfactory performance at the level of syntactic processing,
be the output structures chunks, phrase structures, or dependency
graphs. Therefore, the time seems ripe to extend the state-of-the-art
and consider deep semantic processing as a serious task in
wide-coverage NLP. This is a step that normally requires syntactic
parsing, as well as named entity recognition, anaphora resolution,
thematic role labelling and word sense disambiguation, as well as other
lower levels of processing for which reasonably good methods have
already been developed. Accurate automatic semantic interpretation of
text is expected to benefit newly emerging areas targetting semantic
and pragmatic issues, such as affectivity and sentiment analysis of
texts, textual entailment, and consistency checking.
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