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APPLIED TEXTUAL ENTAILMENT. Edition No. 1
VDM Publishing House, May 2009, Pages: 132
This book introduces the applied notion of textual
entailment as a generic empirical task that captures
major semantic inferences across many applications.
Textual Entailment addresses semantic inference as a
direct mapping between language expressions and
abstracts the common semantic inferences as needed
for text based Natural Language Processing
applications. The book defines the task and describes
the creation of a benchmark dataset for textual
entailment along with proposed evaluation measures.
It further describes how textual entailment can be
approximated and modeled at the lexical level and
proposes a lexical reference subtask and a
correspondingly derived dataset. The book further
proposes a general probabilistic setting that casts
the applied notion of textual entailment in
probabilistic terms. This proposed setting may
provide a unifying framework for modeling uncertain
semantic inferences from texts. Finally, the book
presents a novel acquisition algorithm to identify
lexical entailment relations from a single corpus
focusing on the extraction of verb paraphrases.
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