Sentence Filtering for Information Extraction in Genomics, a Classification Problem

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.

• Type: Conference
• Year: 2001
• Where: PKDD
• Authors: Claire Nedellec, Mohamed Ould Abdel Vetah, Philippe Bessières
• Keywords: Data Mining, Naive Bayes, Naive Bayes Methods, PKDD 2001, Texts Requires Syntactic