C4.5: programs for machine learning
C4.5: programs for machine learning
Wrappers for feature subset selection
Artificial Intelligence - Special issue on relevance
Learning Information Extraction Rules for Semi-Structured and Free Text
Machine Learning - Special issue on natural language learning
Machine Learning
A Comparative Study on Feature Selection in Text Categorization
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Automatic Extraction of Biological Information from Scientific Text: Protein-Protein Interactions
Proceedings of the Seventh International Conference on Intelligent Systems for Molecular Biology
Constructing Biological Knowledge Bases by Extracting Information from Text Sources
Proceedings of the Seventh International Conference on Intelligent Systems for Molecular Biology
Extracting the names of genes and gene products with a hidden Markov model
COLING '00 Proceedings of the 18th conference on Computational linguistics - Volume 1
A robust linguistic platform for efficient and domain specific web content analysis
Large Scale Semantic Access to Content (Text, Image, Video, and Sound)
Sentence filtering for BioNLP: searching for renaming acts
BioNLP Shared Task '11 Proceedings of the BioNLP Shared Task 2011 Workshop
Features combination for extracting gene functions from MEDLINE
ECIR'05 Proceedings of the 27th European conference on Advances in Information Retrieval Research
Beyond the bag of words: a text representation for sentence selection
AI'06 Proceedings of the 19th international conference on Advances in Artificial Intelligence: Canadian Society for Computational Studies of Intelligence
Information extraction as a filtering task
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
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In some domains, Information Extraction (IE) from texts requires syntactic and semantic parsing. This analysis is computationally expensive and IE is potentially noisy if it applies to the whole set of documents when the relevant information is sparse. A preprocessing phase that selects the fragments which are potentially relevant increases the efficiency of the IE process. This phase has to be fast and based on a shallow description of the texts. We applied various classification methods -- IVI, a Naive Bayes learner and C4.5 -- to this fragment filtering task in the domain of functional genomics. This paper describes the results of this study. We show that the IVI and Naive Bayes methods with feature selection gives the best results as compared with their results without feature selection and with C4.5 results.