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Semantics in Text Processing - STEP 2008 Conference Proceedings (Paperback)
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Semantics in Text Processing - STEP 2008 Conference Proceedings (Paperback)
Series: Research in Computational Semantics, v. 1
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Research in robust open-domain text processing has seen
considerable progress in the last couple of decades. It is probably
fair to say that language technology tools have reached
satisfactory performance at the level of syntactic processing.
Therefore, it is timelier than ever to consider deep semantic
processing as a serious task in wide-coverage natural language
processing. This is a step that requires the integration of
syntactic parsing, named entity recognition, anaphora resolution,
thematic role labelling, word sense disambiguation with
fine-grained semantic analysis. Accurate automatic semantic
interpretation of text will benefit newly emerging sub-areas such
as affectivity and sentiment analysis of texts, textual entailment,
and consistency checking, and applications such as automated
question answering, summarisation, and machine translation. This
volume addresses these ambitions by presenting a collection of
papers presented at the first workshop on the Semantics in Text
Processing (STEP 2008), held in Venice from 22 to 24 September
2008. It is divided into three parts: (1) regular papers describing
new results and completed research; (2) reports and descriptions of
state-of-the-art systems that participated in the shared task on
comparing semantic representations; and (3) short papers addressing
ongoing work, novel techniques, or project descriptions. This is
the first volume in \textit{Research in Computational Semantics}
series launched by College Publications. Computational semantics is
a relatively new interdisciplinary area in natural language
processing, focusing on developing techniques to automate the
interpretation of spoken and written natural language. It is an
exciting area combining linguistic insight, logical reasoning, and
knowledge engineering using both symbolic and statistical
techniques to achieve robust and scalable methods for processing
human languages.
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