Text generation: using discourse strategies and focus constraints to generate natural language text
Text generation: using discourse strategies and focus constraints to generate natural language text
Text Categorization with Suport Vector Machines: Learning with Many Relevant Features
ECML '98 Proceedings of the 10th European Conference on Machine Learning
Proceedings of the 3rd ACM/IEEE-CS joint conference on Digital libraries
Journal of Biomedical Informatics - Special issue: Unified medical language system
An unsupervised approach to recognizing discourse relations
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
What's yours and what's mine: determining intellectual attribution in scientific text
EMNLP '00 Proceedings of the 2000 Joint SIGDAT conference on Empirical methods in natural language processing and very large corpora: held in conjunction with the 38th Annual Meeting of the Association for Computational Linguistics - Volume 13
Journal of Biomedical Informatics
Answering Clinical Questions with Knowledge-Based and Statistical Techniques
Computational Linguistics
Journal of Information Science
Psychiatric document retrieval using a discourse-aware model
Artificial Intelligence
A study of structured clinical abstracts and the semantic classification of sentences
BioNLP '07 Proceedings of the Workshop on BioNLP 2007: Biological, Translational, and Clinical Language Processing
Using conditional random fields for result identification in biomedical abstracts
Integrated Computer-Aided Engineering
Biomedical question answering: A survey
Computer Methods and Programs in Biomedicine
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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The ability to accurately model the content structure of text is important for many natural language processing applications. This paper describes experiments with generative models for analyzing the discourse structure of medical abstracts, which generally follow the pattern of "introduction", "methods", "results", and "conclusions". We demonstrate that Hidden Markov Models are capable of accurately capturing the structure of such texts, and can achieve classification accuracy comparable to that of discriminative techniques. In addition, generative approaches provide advantages that may make them preferable to discriminative techniques such as Support Vector Machines under certain conditions. Our work makes two contributions: at the application level, we report good performance on an interesting task in an important domain; more generally, our results contribute to an ongoing discussion regarding the tradeoffs between generative and discriminative techniques.