2006 IntegratingLinguisticKnowledgeI

• (Tsai et al., 2006) ⇒ Tzong-han Tsai, Wen-Chi Chou, Shih-Hung Wu, Ting-Yi Sung, Jieh Hsiang, and Wen-Lian Hsu. (2006). “Integrating Linguistic Knowledge Into a Conditional Random Fieldframework to Identify Biomedical Named Entities.” In: Expert Systems with Applications: An International Journal, 30(1). doi:10.1016/j.eswa.2005.09.072

Subject Headings: Biomedical NER, NER Predictor Feature, CRF-based NER Algorithm, CRF-based Supervised NER Algorithm.

Cited By

• http://scholar.google.com/scholar?q=%222006%22+Integrating+Linguistic+Knowledge+Into+a+Conditional+Random+Fieldframework+to+Identify+Biomedical+Named+Entities
• http://dl.acm.org/citation.cfm?id=1707617.1707634&preflayout=flat#citedby

Quotes

Author Keywords

Biomedical named entity recognition; Conditional random fields; Literature mining; Linguistic features.

Abstract

As new high-throughput technologies have created an explosion of biomedical literature, there arises a pressing need for automatic information extraction from the literature bank. To this end, biomedical named entity recognition (NER) from natural language text is indispensable. Current NER approaches include: dictionary based, rule based, or machine learning based. Since, there is no consolidated nomenclature for most biomedical NEs, any NER system relying on limited dictionaries or rules does not seem to perform satisfactorily. In this paper, we consider a machine learning model, CRF, for the construction of our NER framework. CRF is a well-known model for solving other sequence tagging problems. In our framework, we do our best to utilize available resources including dictionaries, web corpora, and lexical analyzers, and represent them as linguistic features in the CRF model. In the experiment on the JNLPBA 2004 data, with minimal post-processing, our system achieves an F-score of 70.2%, which is better than most state-of-the-art systems. On the GENIA 3.02 corpus, our system achieves an F-score of 78.4% for protein names, which is 2.8% higher than the next-best system. In addition, we also examine the usefulness of each feature in our CRF model. Our experience could be valuable to other researchers working on machine learning based NER.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2006 IntegratingLinguisticKnowledgeI	Tzong-han Tsai Wen-Chi Chou Shih-Hung Wu Ting-Yi Sung Jieh Hsiang Wen-Lian Hsu			Integrating Linguistic Knowledge Into a Conditional Random Fieldframework to Identify Biomedical Named Entities				10.1016/j.eswa.2005.09.072		2006