Lexicalizing a shallow parser

Current NL parsers are expected to run with throughput rate suitable to satisfy ”time constraints” in real applications. The aim of the present work is, on the one hand, to investigate the effects of lexical information in a shallow parsing environment, on the other hand, to study the limits of a bootstrapping architecture that, automatically learning the lexical information in an unsupervised fashion, guarantees the reliability and portability of the parser to different domains. The investigated parser is Chaos (Chunk analysis oriented system), a robust parser based on stratification and lexicalization. Large scale evaluation over a standard tree bank is discussed.

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