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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.

  2008 · Laboratorio LC · EmailWebmaster