Anzeige
Erschienen in:
2013 | OriginalPaper | Buchkapitel
Topic-Oriented Words as Features for Named Entity Recognition
verfasst von : Ziqi Zhang, Trevor Cohn, Fabio Ciravegna
Erschienen in: Computational Linguistics and Intelligent Text Processing
Verlag: Springer Berlin Heidelberg
Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.
Wählen Sie Textabschnitte aus um mit Künstlicher Intelligenz passenden Patente zu finden. powered by
Markieren Sie Textabschnitte, um KI-gestützt weitere passende Inhalte zu finden. powered by
Research has shown that topic-oriented words are often related to named entities and can be used for Named Entity Recognition. Many have proposed to measure topicality of words in terms of ‘informativeness’ based on global distributional characteristics of words in a corpus. However, this study shows that there can be large discrepancy between informativeness and topicality; empirically, informativeness based features can damage learning accuracy of NER. This paper proposes to measure words’ topicality based on local distributional features specific to individual documents, and proposes methods to transform topicality into gazetteer-like features for NER by binning. Evaluated using five datasets from three domains, the methods have shown consistent improvement over a baseline by between 0.9 and 4.0 in F-measure, and always outperformed methods that use informativeness measures.