Unsupervised Models for Named Entity Classification

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Unsupervised Models for Named Entity Classification (1999)

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by Michael Collins , Yoram Singer

Venue:	In Proceedings of the Joint SIGDAT Conference on Empirical Methods in Natural Language Processing and Very Large Corpora
Citations:	542 - 4 self

BibTeX

@INPROCEEDINGS{Collins99unsupervisedmodels,
    author = {Michael Collins and Yoram Singer},
    title = {Unsupervised Models for Named Entity Classification},
    booktitle = {In Proceedings of the Joint SIGDAT Conference on Empirical Methods in Natural Language Processing and Very Large Corpora},
    year = {1999},
    pages = {100--110}
}

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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). 1

Keyphrases

named entity classification large number approach gain unlabeled example entity classification unlabeled data first method second algorithm extends idea natural redundancy similar algorithm simple seed rule many named-entity instance supervised learning task labeled example

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