Machine learning in automated text categorization
ACM Computing Surveys (CSUR)
Machine Learning
Summarizing scientific articles: experiments with relevance and rhetorical status
Computational Linguistics - Summarization
A general computational treatment of the comparative
ACL '89 Proceedings of the 27th annual meeting on Association for Computational Linguistics
A general computational treatment of comparatives for natural language question answering
ACL '88 Proceedings of the 26th annual meeting on Association for Computational Linguistics
Identifying comparative sentences in text documents
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
Interpreting comparative constructions in biomedical text
BioNLP '07 Proceedings of the Workshop on BioNLP 2007: Biological, Translational, and Clinical Language Processing
Mining comparative sentences and relations
AAAI'06 proceedings of the 21st national conference on Artificial intelligence - Volume 2
Mining opinions in comparative sentences
COLING '08 Proceedings of the 22nd International Conference on Computational Linguistics - Volume 1
The WEKA data mining software: an update
ACM SIGKDD Explorations Newsletter
Journal of Biomedical Informatics
AAAI'97/IAAI'97 Proceedings of the fourteenth national conference on artificial intelligence and ninth conference on Innovative applications of artificial intelligence
Mining comparative opinions from customer reviews for Competitive Intelligence
Decision Support Systems
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
Extracting comparative entities and predicates from texts using comparative type classification
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies - Volume 1
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Comparisons play a critical role in scientific communication by allowing an author to situate their work in the context of earlier research problems, experimental approaches, and results. Our goal is to identify comparison claims automatically from full-text scientific articles. In this paper, we introduce a set of semantic and syntactic features that characterize a sentence and then demonstrate how those features can be used in three different classifiers: Naïve Bayes (NB), a Support Vector Machine (SVM) and a Bayesian network (BN). Experiments were conducted on 122 full-text toxicology articles containing 14,157 sentences, of which 1,735 (12.25%) were comparisons. Experiments show an F1 score of 0.71, 0.69, and 0.74 on the development set and 0.76, 0.65, and 0.74 on a validation set for the NB, SVM and BN, respectively.