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Learning Korean Named Entity by Bootstrapping with Web Resources
Gary Geunbae LEE
IEICE TRANSACTIONS on Information and Systems Vol.E87-D No.12 pp.2872-2882
Publication Date: 2004/12/01
Print ISSN: 0916-8532
Type of Manuscript: PAPER
Category: Natural Language Processing
Named Entity Recognition,
automatic generation of NE-tagged corpus,
bootstrapping with web resources,
Full Text: PDF
An important issue in applying machine learning algorithms to Natural Language Processing areas such as Named Entity Recognition tasks is to overcome the lack of tagged corpora. Several bootstrapping methods such as co-training have been proposed as a solution. In this paper, we present a different approach using the Web resources. A Named Entity (NE) tagged corpus is generated from the Web using about 3,000 names as seeds. The generated corpus may have a lower quality than the manually tagged corpus but its size can be increased sufficiently. Several features are developed and the decision list is learned using the generated corpus. Our method is verified by comparing it to both the decision list learned on the manual corpus and the DL-CoTrain method. We also present a two-level classification by cascading highly precise lexical patterns and the decision list to improve the performance.