APPLIED TEXTUAL ENTAILMENT
A generic framework to capture shallow semantic inference
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This book introduces the applied notion of textualentailment as a generic empirical task that capturesmajor semantic inferences across many applications.Textual Entailment addresses semantic inference as adirect mapping between language expressions andabstracts the common semantic inferences as neededfor text based Natural Language Processingapplications. The book defines the task and describesthe creation of a benchmark dataset for textualentailment along with proposed evaluation measures.It further describes how textual entailment can beapproximated and modeled at the lexical level andpropos...
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.
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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