Information extraction as a basis for high-precision text classification
ACM Transactions on Information Systems (TOIS)
Making large-scale support vector machine learning practical
Advances in kernel methods
A bootstrapping method for learning semantic lexicons using extraction pattern contexts
EMNLP '02 Proceedings of the ACL-02 conference on Empirical methods in natural language processing - Volume 10
Learning subjective nouns using extraction pattern bootstrapping
CONLL '03 Proceedings of the seventh conference on Natural language learning at HLT-NAACL 2003 - Volume 4
Reducing semantic drift with bagging and distributional similarity
ACL '09 Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP: Volume 1 - Volume 1
Automatically constructing a dictionary for information extraction tasks
AAAI'93 Proceedings of the eleventh national conference on Artificial intelligence
Unsupervised discovery of negative categories in lexicon bootstrapping
EMNLP '10 Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing
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Traditionally, automated triage of papers is performed using lexical (unigram, bigram, and sometimes trigram) features. This paper explores the use of information extraction (IE) techniques to create richer linguistic features than traditional bag-of-words models. Our classifier includes lexico-syntactic patterns and more-complex features that represent a pattern coupled with its extracted noun, represented both as a lexical term and as a semantic category. Our experimental results show that the IE-based features can improve performance over unigram and bigram features alone. We present intrinsic evaluation results of full-text document classification experiments to determine automatically whether a paper should be considered of interest to biologists at the Mouse Genome Informatics (MGI) system at the Jackson Laboratories. We also further discuss issues relating to design and deployment of our classifiers as an application to support scientific knowledge curation at MGI.