tailieunhanh - Báo cáo khoa học: "Discovering Corpus-Specific Word Senses"

This paper presents an unsupervised algorithm which automatically discovers word senses from text. The algorithm is based on a graph model representing words and relationships between them. Sense clusters are iteratively computed by clustering the local graph of similar words around an ambiguous word. Discrimination against previously extracted sense clusters enables us to discover new senses. We use the same data for both recognising and resolving ambiguity. | Discovering Corpus-Specific Word Senses Beate Dorow Institut fur Maschinelle Sprachverarbeitung Universitãt Stuttgart Germany Dominic Widdows Center for the Study of Language and Information Stanford University California dwiddows@ Abstract This paper presents an unsupervised algorithm which automatically discovers word senses from text. The algorithm is based on a graph model representing words and relationships between them. Sense clusters are iteratively computed by clustering the local graph of similar words around an ambiguous word. Discrimination against previously extracted sense clusters enables us to discover new senses. We use the same data for both recognising and resolving ambiguity. 1 Introduction This paper describes an algorithm which automatically discovers word senses from free text and maps them to the appropriate entries of existing dictionaries or taxonomies. Automatic word sense discovery has applications of many kinds. It can greatly facilitate a lexicographer s work and can be used to automatically construct corpus-based taxonomies or to tune existing ones. The same corpus evidence which supports a clustering of an ambiguous word into distinct senses can be used to decide which sense is referred to in a given context Schiitze 1998 . This paper is organised as follows. In section 2 we present the graph model from which we discover word senses. Section 3 describes the way we divide graphs surrounding ambiguous words into different areas corresponding to different senses using Markov clustering van Dongen 2000 . The quality of the Markov clustering depends strongly on several parameters such as a granularity factor and the size of the local graph. In section 4 we outline a word sense discovery algorithm which bypasses the problem of parameter tuning. We conducted a pilot experiment to examine the performance of our algorithm on a set of words with varying degree of ambiguity. Section 5 describes

• Báo cáo khoa học
• Discovering Corpus Specific Word Senses
• Beate Dorow
• Long Papers
• scientific reports
• model language
• process natural language
• Dialog Control
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• Dialog Control in a Natural Language System
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• IiVFORMATION SCIENCE IIEW JERSEY INSTITUTE OF TECHNOLOGY
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• Bozena Henisz
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• DATABASE UPDATES
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• Hendrlx and William H
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• PLANNING NATURAL LANGUAGE
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• THE TEXT SYSTEM
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• MENTING A DATABASE
• KNOWLEDGE REPRESENTATION
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• Jeffrey Conklin David D
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