Automatically assigned DDC number: 00635

Manually assigned DDC number: 00635

Number of references: 16

Title: Memory-Based Shallow Parsing

Author:

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Subject: Walter Daelemans,Sabine Buchholz,Jorn Veenstra Memory-Based Shallow Parsing

Description: We present a memory-based learning (MBL) approach to shallow parsing in which POS tagging, chunking, and identification of syntactic relations are formulated as memory-based modules. The experiments reported in this paper show competitive results, the F fi=1 for the Wall Street Journal (WSJ) treebank is: 93.8% for NP chunking, 94.7% for VP chunking, 77.1% for subject detection and 79.0% for object detection. Introduction Recently, there has been an increased interest in approaches to automatically learning to recognize shallow linguistic patterns in text [ Ramshaw and Marcus, 1995; Vilain and Day, 1996; Argamon et al., 1998; Buchholz, 1998; Cardie and Pierce, 1998; Veenstra, 1998; Daelemans et al., 1999a ] . Shallow parsing is an important component of most text analysis systems in applications such as information extraction and summary generation. It includes discovering the main constituents of sentences (NPs, VPs, PPs) and their heads, and determining syntactic relationships like ...

Contributor: The Pennsylvania State University CiteSeer Archives

Publisher: unknown

Date: 1999-05-25

Pubyear: 1999

Format: ps

Identifier: http://citeseer.ist.psu.edu/226208.html

Source: ftp://ilk.kub.nl/pub/papers/ilk.9907.ps.gz

Language: en

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