A tutorial on hidden Markov models and selected applications in speech recognition
Readings in speech recognition
Introduction to Modern Information Retrieval
Introduction to Modern Information Retrieval
Summarizing scientific articles: experiments with relevance and rhetorical status
Computational Linguistics - Summarization
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
A maximum entropy approach to information extraction from semi-structured and free text
Eighteenth national conference on Artificial intelligence
EACL '99 Proceedings of the ninth conference on European chapter of the Association for Computational Linguistics
An unsupervised approach to recognizing discourse relations
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
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
Tuning support vector machines for biomedical named entity recognition
BioMed '02 Proceedings of the ACL-02 workshop on Natural language processing in the biomedical domain - Volume 3
A Sentence Classification System for Multi Biomedical Literature Summarization
ICDEW '05 Proceedings of the 21st International Conference on Data Engineering Workshops
Computational analysis of move structures in academic abstracts
COLING-ACL '06 Proceedings of the COLING/ACL on Interactive presentation sessions
Integrated Computer-Aided Engineering
Detecting data records in semi-structured web sites based on text token clustering
Integrated Computer-Aided Engineering
A knowledge retrieval model using ontology mining and user profiling
Integrated Computer-Aided Engineering
Utilizing phrase-similarity measures for detecting and clustering informative RSS news articles
Integrated Computer-Aided Engineering
Ontology-based inference for causal explanation
Integrated Computer-Aided Engineering
Rule-based dependency models for security protocol analysis
Integrated Computer-Aided Engineering
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
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
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
Generative content models for structural analysis of medical abstracts
BioNLP '06 Proceedings of the Workshop on Linking Natural Language Processing and Biology: Towards Deeper Biological Literature Analysis
Developing a robust part-of-speech tagger for biomedical text
PCI'05 Proceedings of the 10th Panhellenic conference on Advances in Informatics
Enhancing search results with semantic annotation using augmented browsing
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Three
Integrated Computer-Aided Engineering
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The abstracts of biomedical papers usually contain three sections: objective, methods, and results-conclusion. The results-conclusion section is the most important because it usually describes the main contribution of a paper. Unfortunately, not all biomedical journals follow this three-section format. In this paper, we propose a machine learning (ML) based approach to automatically identify the results-conclusion section. The results-conclusion section identification problem is formulated as a sequence labeling task. Four feature sets, including Position, Named Entity, Tense, and Word Frequency, are employed with Conditional Random Fields (CRFs) as the underlying ML model. The experiment results show that the proposed approach can achieve F-measure, precision, and recall of 97.08%, 96.63% and 97.53%, respectively.}