Named Entities

Recognition, classification and use

| New York University
| University of Lisbon
ISBN 9789027222497 | EUR 90.00 | USD 120.00
ISBN 9789027289223 | EUR 90.00 | USD 120.00
Named Entities provides critical information for many NLP applications. Named Entity recognition and classification (NERC) in text is recognized as one of the important sub-tasks of Information Extraction (IE). The seven papers in this volume cover various interesting and informative aspects of NERC research. Nadeau & Sekine provide an extensive survey of past NERC technologies, which should be a very useful resource for new researchers in this field. Smith & Osborne describe a machine learning model which tries to solve the over-fitting problem. Mazur & Dale tackle a common problem of NE and conjunction; as conjunctions are often a part of NEs or appear close to NEs, this is an important practical problem. A further three papers describe analyses and implementations of NERC for different languages: Spanish (Galicia-Haro & Gelbukh), Bengali (Ekbal, Naskar & Bandyopadhyay), and Serbian (Vitas, Krstev & Maurel). Finally, Steinberger & Pouliquen report on a real WEB application where multilingual NERC technology is used to identify occurrences of people, locations and organizations in newspapers in different languages.

The contributions to this volume were previously published in Lingvisticae Investigationes 30:1 (2007).

[Benjamins Current Topics, 19]  2009.  v, 168 pp.
Publishing status: Available
Table of Contents
Cited by

Cited by 23 other publications

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This list is based on CrossRef data as of 20 april 2022. Please note that it may not be complete. Sources presented here have been supplied by the respective publishers. Any errors therein should be reported to them.

Subjects & Metadata
BIC Subject: CFG – Semantics, Pragmatics, Discourse Analysis
BISAC Subject: LAN009000 – LANGUAGE ARTS & DISCIPLINES / Linguistics / General
ONIX Metadata
ONIX 2.1
ONIX 3.0
U.S. Library of Congress Control Number:  2009017541 | Marc record