Efficient parsing for Information Extraction

Several (and successfull) Information Extraction systems have recently replaced the core parsing components with shallow but more efficient recognizers. In this paper we argue that the absence of an underlying grammatical recognizer, given the complex nature of several (non-english) languages, is a strong limitation for text processing functionalities, like those an IE system needs. We propose a robust and efficient syntactic recognizer mainly aimed to capture grammatical information crucial for several linguistic and non linguistic inferences. The proposed system is based on a novel architecture exploiting two major principles: lexicalization and stratification of the parsing process. As several linguistic theories (e.g. HPSG) and parsing frameworks (e.g. LTAG, SLTAG, lexicalized probabilistic parsing) suggest, lexicon-driven systems ensure the suitable forms of grammatical control for many complex phenomena. In our system an analysis guided by information on typical verb projections (e.g. verb subcategorization structures) is coupled with extended locality constraints (i.e. recognition of clause boundaries). Futhermore, stratification is also employed. A cascade of processing steps starts from chunk recognition and proceeds through clause analysis to dependency detection. Recognition of chunks allows to minimize the input ambiguity to the remaining phases. The resulting system is thus robust against ungrammatical phenomena (e.g. complex clause embedding, misspellings, unknown words). Efficiency is also retained, although ambiguous phenomena (multiple PP attachments) are recognized.