A survey of named entity recognition and classification

Dados Bibliográficos

ID5841208
AutoresDavid R Nadeau, David Nadeau (National Research Council Canada), Satoshi Sekine (New York University)
Ano2007
Volume30
Fascículo1
Páginas3-26
Data de publicação2007-08-10
Peer ReviewedSim
Open AccessNão
TipoARTICLE
PeriódicoLingvisticae investigationes. Supplementa (JOURNAL)
Identificadores do periódicoISSN: 0165-7569 • E-ISSN: 1569-9927
EditoraJohn Benjamins Publishing Company (PUBLISHER • NL)
DOI10.1075/li.30.1.03nad
OpenAlexW2020278455
IdiomaEN
Citações recebidas48
Referências citadas1

This survey covers fifteen years of research in the Named Entity Recognition and Classification (NERC) field, from 1991 to 2006. We report observations about languages, named entity types, domains and textual genres studied in the literature. From the start, NERC systems have been developed using hand-made rules, but now machine learning techniques are widely used. These techniques are surveyed along with other critical aspects of NERC such as features and evaluation methods. Features are word-level, dictionary-level and corpus-level representations of words in a document. Evaluation techniques, ranging from intuitive exact match to very complex matching techniques with adjustable cost of errors, are an indisputable key to progress

Named entity · Authorship Attribution and Profiling · Natural Language Processing Techniques · Topic Modeling