Elements of machine learning
A re-examination of text categorization methods
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Modern Information Retrieval
The C-value/NC-value Method of Automatic Recognition for Multi-Word Terms
ECDL '98 Proceedings of the Second European Conference on Research and Advanced Technology for Digital Libraries
Recommender systems using linear classifiers
The Journal of Machine Learning Research
Extracting noun phrases from large-scale texts: a hybrid approach and its automatic evaluation
ACL '94 Proceedings of the 32nd annual meeting on Association for Computational Linguistics
Noun-phrase analysis in unrestricted text for information retrieval
ACL '96 Proceedings of the 34th annual meeting on Association for Computational Linguistics
RCV1: A New Benchmark Collection for Text Categorization Research
The Journal of Machine Learning Research
Solving large scale linear prediction problems using stochastic gradient descent algorithms
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Identifying Variable-Length Meaningful Phrases with Correlation Functions
ICTAI '04 Proceedings of the 16th IEEE International Conference on Tools with Artificial Intelligence
MedPost: a part-of-speech tagger for bioMedical text
Bioinformatics
Paradigmatic modifiability statistics for the extraction of complex multi-word terms
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
How to interpret PubMed queries and why it matters
Journal of the American Society for Information Science and Technology
The ineffectiveness of within-document term frequency in text classification
Information Retrieval
SMOTE: synthetic minority over-sampling technique
Journal of Artificial Intelligence Research
Handbook of Natural Language Processing
Handbook of Natural Language Processing
Text mining techniques for leveraging positively labeled data
BioNLP '11 Proceedings of BioNLP 2011 Workshop
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In the modern world people frequently interact with retrieval systems to satisfy their information needs. Humanly understandable well-formed phrases represent a crucial interface between humans and the web, and the ability to index and search with such phrases is beneficial for human-web interactions. In this paper we consider the problem of identifying humanly understandable, well formed, and high quality biomedical phrases in MEDLINE documents. The main approaches used previously for detecting such phrases are syntactic, statistical, and a hybrid approach combining these two. In this paper we propose a supervised learning approach for identifying high quality phrases. First we obtain a set of known well-formed useful phrases from an existing source and label these phrases as positive. We then extract from MEDLINE a large set of multiword strings that do not contain stop words or punctuation. We believe this unlabeled set contains many well-formed phrases. Our goal is to identify these additional high quality phrases. We examine various feature combinations and several machine learning strategies designed to solve this problem. A proper choice of machine learning methods and features identifies in the large collection strings that are likely to be high quality phrases. We evaluate our approach by making human judgments on multiword strings extracted from MEDLINE using our methods. We find that over 85% of such extracted phrase candidates are humanly judged to be of high quality.