Event characterization for information extraction from business-based technical documents

Event identification plays a crucial role in several natural language processing applications such as information extraction, question answering, and text analysis. In this paper, we describe a novel approach for analyzing events, their distribution, and the event mentions from a corpus of unlabeled business-based technical documents—a specific genre. In order to infer such mentions, we analyze the subject-verbobject structure for semi-automatically extracting several lexical, syntactic, and semantic features for each event mention from the corpus. Extracting event mentions allows us to cast grouping together the mentions with same features and propose properties leading to the differences of the specific genre. The obtained results are used for supporting an eventcentered processing level, from an automated machine for processing texts.