An Algorithm that Learns What‘s in a Name
Machine Learning - Special issue on natural language learning
Database merging strategy based on logistic regression
Information Processing and Management: an International Journal
Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
Information Retrieval
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Maximum Entropy Markov Models for Information Extraction and Segmentation
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Shallow parsing with conditional random fields
NAACL '03 Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology - Volume 1
Introduction to the CoNLL-2003 shared task: language-independent named entity recognition
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
Incorporating lexical knowledge into biomedical NE recognition
JNLPBA '04 Proceedings of the International Joint Workshop on Natural Language Processing in Biomedicine and its Applications
Annotating multiple types of biomedical entities: a single word classification approach
JNLPBA '04 Proceedings of the International Joint Workshop on Natural Language Processing in Biomedicine and its Applications
Named entity recognition in biomedical texts using an HMM model
JNLPBA '04 Proceedings of the International Joint Workshop on Natural Language Processing in Biomedicine and its Applications
Exploiting context for biomedical entity recognition: from syntax to the web
JNLPBA '04 Proceedings of the International Joint Workshop on Natural Language Processing in Biomedicine and its Applications
Adapting an NER-system for German to the biomedical domain
JNLPBA '04 Proceedings of the International Joint Workshop on Natural Language Processing in Biomedicine and its Applications
Exploring deep knowledge resources in biomedical name recognition
JNLPBA '04 Proceedings of the International Joint Workshop on Natural Language Processing in Biomedicine and its Applications
POSBIOTM-NER in the shared task of BioNLP/NLPBA 2004
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
Improving optical character recognition through efficient multiple system alignment
Proceedings of the 9th ACM/IEEE-CS joint conference on Digital libraries
A dynamic window based passage extraction algorithm for genomics information retrieval
ISMIS'08 Proceedings of the 17th international conference on Foundations of intelligent systems
DAS '10 Proceedings of the 9th IAPR International Workshop on Document Analysis Systems
Aggregating semantic annotators
Proceedings of the VLDB Endowment
Identifying the Truth: Aggregation of Named Entity Extraction Results
Proceedings of International Conference on Information Integration and Web-based Applications & Services
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The task of biomedical named-entity recognition is to identify technical terms in the domain of biology that are of special interest to domain experts. While numerous algorithms have been proposed for this task, biomedical named-entity recognition remains a challenging task and an active area of research, as there is still a large accuracy gap between the best algorithms for biomedical named-entity recognition and those for general newswire named-entity recognition. The reason for such discrepancy in accuracy results is generally attributed to inadequate feature representations of individual entity recognition systems and external domain knowledge.In order to take advantage of the rich feature representations and external domain knowledge used by different systems, we propose several Meta biomedical named-entity recognition algorithms that combine recognition results of various recognition systems. The proposed algorithms -- majority vote, unstructured exponential model and conditional random field -- were tested on the GENIA biomedical corpus. Empirical results show that the F score can be improved from 0.72, which is attained by the best individual system, to 0.96 by our Meta entity recognition approach.