'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 as 'Lingvisticae
Investigationes' 30:1 (2007).