1999 UnsupervisedNamedEntityClassification

• (Collins & Singer, 1999) ⇒ Michael Collins, Yoram Singer. (1999). “Unsupervised Models for Named Entity Classification.” In: Proceedings of EMNLP 1999.

Subject Headings: Semi-Supervised Named Entity Recognition Algorithm, Bootstrapping, Unsupervised Learning Algorithm.

Cited By

~578 http://scholar.google.com/scholar?cites=13260482622036226672

Quotes

Abstract

• This paper discusses the use of unlabeled examples for the problem of named entity classification. A large number of rules is needed for coverage of the domain, suggesting that a fairly large number of labeled examples should be required to train a classifier. However, we show that the use of unlabeled data can reduce the requirements for supervision to just 7 simple “seed” rules. The approach gains leverage from natural redundancy in the data: for many named-entity instances both the spelling of the name and the context in which it appears are sufficient to determine its type. We present two algorithms. The first method uses a similar algorithm to that of (Yarowsky 95), with modifications motivated by (Blum and Mitchell 98). The second algorithm extends ideas from boosting algorithms, designed for supervised learning tasks, to the framework suggested by (Blum and Mitchell 98).

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
1999 UnsupervisedNamedEntityClassification	Michael Collins Yoram Singer			Unsupervised Models for Named Entity Classification			http://acl.ldc.upenn.edu/W/W99/W99-0613.pdf