Natural Language Information Retrieval (text, Speech And Language Technology)
by T. Strzalkowski /
2010 / English / DjVu
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The last decade has been one of dramatic progress in the field of
Natural Language Processing (NLP). This hitherto largely academic
discipline has found itself at the center of an information
revolution ushered in by the Internet age, as demand for
human-computer communication and informa tion access has exploded.
Emerging applications in computer-assisted infor mation production
and dissemination, automated understanding of news, understanding
of spoken language, and processing of foreign languages have given
impetus to research that resulted in a new generation of robust
tools, systems, and commercial products. Well-positioned government
research funding, particularly in the U. S. , has helped to advance
the state-of-the art at an unprecedented pace, in no small measure
thanks to the rigorous 1 evaluations. This volume focuses on the
use of Natural Language Processing in In formation Retrieval (IR),
an area of science and technology that deals with cataloging,
categorization, classification, and search of large amounts of
information, particularly in textual form. An outcome of an
information retrieval process is usually a set of documents
containing information on a given topic, and may consist of
newspaper-like articles, memos, reports of any kind, entire books,
as well as annotated image and sound files. Since we assume that
the information is primarily encoded as text, IR is also a natural
language processing problem: in order to decide if a document is
relevant to a given information need, one needs to be able to
understand its content.
The last decade has been one of dramatic progress in the field of
Natural Language Processing (NLP). This hitherto largely academic
discipline has found itself at the center of an information
revolution ushered in by the Internet age, as demand for
human-computer communication and informa tion access has exploded.
Emerging applications in computer-assisted infor mation production
and dissemination, automated understanding of news, understanding
of spoken language, and processing of foreign languages have given
impetus to research that resulted in a new generation of robust
tools, systems, and commercial products. Well-positioned government
research funding, particularly in the U. S. , has helped to advance
the state-of-the art at an unprecedented pace, in no small measure
thanks to the rigorous 1 evaluations. This volume focuses on the
use of Natural Language Processing in In formation Retrieval (IR),
an area of science and technology that deals with cataloging,
categorization, classification, and search of large amounts of
information, particularly in textual form. An outcome of an
information retrieval process is usually a set of documents
containing information on a given topic, and may consist of
newspaper-like articles, memos, reports of any kind, entire books,
as well as annotated image and sound files. Since we assume that
the information is primarily encoded as text, IR is also a natural
language processing problem: in order to decide if a document is
relevant to a given information need, one needs to be able to
understand its content.