Chinese Named Entity Recognition with Graph-based Semi-supervised Learning Model

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Named entity recognition (NER) plays an important role in the NLP literature. The traditional methods tend to employ large annotated corpus to achieve a high performance. Different with many semi-supervised learning models for NER task, in this paper, we employ the graph-based semi-supervised learning (GBSSL) method to utilize the freely available unlabeled data. The experiment shows that the unlabeled corpus can enhance the state-of-theart conditional random field (CRF) learning model and has potential to improve the tagging accuracy even though the margin is a little weak and not satisfying in current experiments.
Original languageEnglish
Title of host publicationProceedings of the Eighth SIGHAN Workshop on Chinese Language Processing
EditorsLiang-Chih Yu, Zhifang Sui, Yue Zhang, Vincent Ng
PublisherAssociation for Computational Linguistics
Pages15-20
Number of pages6
Publication statusPublished - Jul 2015

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