Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
A maximum entropy approach to named entity recognition
A maximum entropy approach to named entity recognition
Nymble: a high-performance learning name-finder
ANLC '97 Proceedings of the fifth conference on Applied natural language processing
Named Entity recognition without gazetteers
EACL '99 Proceedings of the ninth conference on European chapter of the Association for Computational Linguistics
Named entity recognition using an HMM-based chunk tagger
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
Two-phase biomedical NE recognition based on SVMs
BioMed '03 Proceedings of the ACL 2003 workshop on Natural language processing in biomedicine - Volume 13
Named entity recognition with character-level models
CONLL '03 Proceedings of the seventh conference on Natural language learning at HLT-NAACL 2003 - Volume 4
Named entity recognition using hundreds of thousands of features
CONLL '03 Proceedings of the seventh conference on Natural language learning at HLT-NAACL 2003 - Volume 4
CONLL '03 Proceedings of the seventh conference on Natural language learning at HLT-NAACL 2003 - Volume 4
Introduction to the bio-entity recognition task at JNLPBA
JNLPBA '04 Proceedings of the International Joint Workshop on Natural Language Processing in Biomedicine and its Applications
Biomedical named entity recognition using conditional random fields and rich feature sets
JNLPBA '04 Proceedings of the International Joint Workshop on Natural Language Processing in Biomedicine and its Applications
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Most research on biomedical named entity recognition has focused on English texts, e.g., MEDLINE abstracts. However, recent years have also seen significant growth of biomedical publications in other languages. For example, the Chinese Biomedical Bibliographic Database has collected over 3 million articles published after 1978 from 1600 Chinese biomedical journals. We present here a Conditional Random Field (CRF) based system for recognizing biomedical named entities in Chinese texts. Viewing Chinese sentences as sequences of characters, we trained and tested the CRF model using a manually annotated corpus containing 106 research abstracts (481 sentences in total). The features we used for the CRF model include word segmentation tags provided by a segmenter trained on newswire corpora, and lists of frequent characters gathered from training data and external resources. Randomly selecting 400 sentences for training and the rest for testing, our system obtained an 68.60% F-score on average, significantly outperforming the baseline system (F-score 60.54% using a simple dictionary match). This suggests that statistical approaches such as CRFs based on annotated corpora hold promise for the biomedical NER task in Chinese texts.