TAILIEUCHUNG - Báo cáo khoa học: "Representing Text Chunks"

Dividing sentences in chunks of words is a useful preprocessing step for parsing, information extraction and information retrieval. (l~mshaw and Marcus, 1995) have introduced a "convenient" data representation for chunking by converting it to a tagging task. In this paper we will examine seven different data representations for the problem of recognizing noun phrase chunks. We will show that the the data representation choice has a minor influence on chunking performance. However, equipped with the most suitable data representation, our memory-based learning chunker was able to improve the best published chunking results for a standard data set. . | Proceedings of EACL 99 Representing Text Chunks Erik F. Tjong Kim Sang Jorn Veenstra Center for Dutch Language and Speech Computational Linguistics University of Antwerp Tilburg University Universiteitsplein 1 . Box 90153 B-2610 Wilrijk Belgium 5000 LE Tilburg The Netherlands erikt@ veenstra@ Abstract Dividing sentences in chunks of words is a useful preprocessing step for parsing information extraction and information retrieval. Ramshaw and Marcus 1995 have introduced a convenient data representation for chunking by converting it to a tagging task. In this paper we will examine seven different data representations for the problem of recognizing noun phrase chunks. We will show that the the data representation choice has a minor influence on chunking performance. However equipped with the most suitable data representation our memory-based learning chunker was able to improve the best published chunking results for a standard data set. 1 Introduction The text corpus tasks parsing information extraction and information retrieval can benefit from dividing sentences in chunks of words. Ramshaw and Marcus 1995 describe an error-driven transformation-based learning TBL method for finding NP chunks in texts. NP chunks or baseNPs are non-overlapping non-recursive noun phrases. In their experiments they have modeled chunk recognition as a tagging task words that are inside a baseNP were marked I words outside a baseNP received an 0 tag and a special tag B was used for the first word inside a baseNP immediately following another baseNP. A text example original In yv early trading yv in V Hong Kong jv yv Monday yv at gold yv was quoted at tv yv V an ounce v tagged In O early I trading I in o Hong I Kong I Monday B O gold I was o quoted O at o I 1 an B ounce I . O Other representations for NP chunking can be used as well. An example is the representation used in Ratnaparkhi 1998 where all the chunkinitial words receive the same start tag .

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