Semantic Relations in Bioscience Text
Extracting Semantics with Natural Language Processing and Machine Learning
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Semantic analysis is one of the most important areas in the field of natural language processing. Semantic analysis is devoted to the study of the meaning of linguistic utterances.This book describes algorithms that extract semantics from bioscience text using statistical machine learning techniques. In particular this book is concerned with the identification of concepts of interest ("entities") and the identification of the relationships that hold between them.Novel machine learning algorithms are proposed for the problems of classifying the semantic relations between nouns in noun compounds...
Semantic analysis is one of the most important areas
in the field of natural language processing.
Semantic analysis is devoted to the study of the
meaning of linguistic utterances.
This book describes algorithms that extract
semantics from bioscience text using statistical
machine learning techniques. In particular this book
is concerned with the identification of concepts of
interest ("entities") and the identification of the
relationships that hold between them.
Novel machine learning algorithms are proposed for
the problems of classifying the semantic relations
between nouns in noun compounds, of distinguishing
among several relation types that can occur between
the entities "treatment" and "disease" and the
problem of identifying the interactions between
proteins.
The results described in this book represent first
steps on the way to a comprehensive strategy of
exploiting machine learning algorithms for the
analysis of bioscience text.
in the field of natural language processing.
Semantic analysis is devoted to the study of the
meaning of linguistic utterances.
This book describes algorithms that extract
semantics from bioscience text using statistical
machine learning techniques. In particular this book
is concerned with the identification of concepts of
interest ("entities") and the identification of the
relationships that hold between them.
Novel machine learning algorithms are proposed for
the problems of classifying the semantic relations
between nouns in noun compounds, of distinguishing
among several relation types that can occur between
the entities "treatment" and "disease" and the
problem of identifying the interactions between
proteins.
The results described in this book represent first
steps on the way to a comprehensive strategy of
exploiting machine learning algorithms for the
analysis of bioscience text.
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