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In regards to computing lexical similarity, the two fundamental problems are respectively concerned with how to explore concept relationships predefined and enumerated in lexical knowledge bases and how to statistically induce and learn context relationships from word co-occurrences. To address these problems, this book focuses on approaching both taxonomic similarity through the semantic networks in WordNet and distributional similarity through syntactically constrained context. The taxonomic similarity model we proposed outperforms most popular similarity methods with respect to simulating…mehr

Produktbeschreibung
In regards to computing lexical similarity, the two
fundamental problems are respectively concerned with
how to explore concept relationships predefined and
enumerated in lexical knowledge bases and how to
statistically induce and learn context relationships
from word co-occurrences. To address these problems,
this book focuses on approaching both taxonomic
similarity through the semantic networks in WordNet
and distributional similarity through syntactically
constrained context. The taxonomic similarity model
we proposed outperforms most popular similarity
methods with respect to simulating human similarity
judgments. In relation to distributional similarity,
we thoroughly investigated the semantic properties of
grammatical relationships in regulating word
meanings, whereby over 80% precision can be reached
in extracting synonyms or near-synonyms. This book
provides a systematic guidance on computing taxonomic
similarity and distributional similarity. It is
appropriate for system developers and researchers
working in language technology.
Autorenporträt
PhD, major research fields include Lexical Semantics and Natural
Language Processing. Research Fellow at Flinders University,
Australia.