The nature of statistical learning theory
The nature of statistical learning theory
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Learning to Classify Text Using Support Vector Machines: Methods, Theory and Algorithms
Learning to Classify Text Using Support Vector Machines: Methods, Theory and Algorithms
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
Summarizing scientific articles: experiments with relevance and rhetorical status
Computational Linguistics - Summarization
Constructing Biological Knowledge Bases by Extracting Information from Text Sources
Proceedings of the Seventh International Conference on Intelligent Systems for Molecular Biology
A non-projective dependency parser
ANLC '97 Proceedings of the fifth conference on Applied natural language processing
Using argumentation to retrieve articles with similar citations from MEDLINE
JNLPBA '04 Proceedings of the International Joint Workshop on Natural Language Processing in Biomedicine and its Applications
Zone identification in biology articles as a basis for information extraction
JNLPBA '04 Proceedings of the International Joint Workshop on Natural Language Processing in Biomedicine and its Applications
BioNLP '10 Proceedings of the 2010 Workshop on Biomedical Natural Language Processing
Section classification in clinical notes using supervised hidden markov model
Proceedings of the 1st ACM International Health Informatics Symposium
Mining methodologies from NLP publications: A case study in automatic terminology recognition
Computer Speech and Language
A weakly-supervised approach to argumentative zoning of scientific documents
EMNLP '11 Proceedings of the Conference on Empirical Methods in Natural Language Processing
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At a time when experimental throughput in the field of molecular biology is increasing, it is necessary for biologists and people working in related fields to have access to sophisticated tools to enable them to efficiently process large amounts of information in order to stay abreast of current research.Rhetorical zone analysis is an application of natural language processing in which areas of text in scientific papers are classified in terms of argumentation and intellectual contribution in order to pinpoint and distinguish certain types of information. Such analysis can be employed to assist in information extraction, helping to assess and integrate data generated by experiments into the scientific community's store of knowledge.We present results for several experiments in automatic zone identification on the ZAISA-1 dataset, a new dataset composed of full biomedical research papers hand-annotated for rhetorical zones. We concentrate on general purpose and linguistically motivated features, and report results for a variety of sets of features. It is our intention to provide a baseline feature set for modeling, which can be extended in future work using combinations of heuristics and more sophisticated and task-specific modeling techniques.