Identifying Strategic Information from Scientific Articles through Sentence Classification

We address here the need to assist users in rapidly accessing the most important or strategic information in the text corpus by identifying sentences carrying specific information. More precisely, we want to identify contribution of authors of scientific papers through a categorization of sentences using rhetorical and lexical cues. We built local grammars to annotate sentences in the corpus according to their rhetorical status: objective, new things, results, findings, hypotheses, conclusion, related_word, future work. The annotation is automatically projected automatically onto two other corpora to test their portability across several domains. The local grammars are implemented in the Unitex system. After sentence categorization, the annotated sentences are clustered and users can navigate the result by accessing specific information types. The results can be used for advanced information retrieval purposes.

• Type: Conference
• Year: 2008
• Where: LREC
• Authors: Fidelia Ibekwe-Sanjuan, Chaomei Chen, Roberto Pinho
• Keywords: Education, Local Grammars, LREC 2008, Specific Information, Text Corpus