Paper Info
Title
Efficient and robust phrase chunking using support vector machines
Abstract
Automatic text chunking is a task which aims to recognize phrase structures in natural language text. It is the key technology of knowledge-based system where phrase structures provide important syntactic information for knowledge representation. Support Vector Machine (SVM-based) phrase chunking system had been shown to achieve high performance for text chunking. But its inefficiency limits the actual use on large dataset that only handles several thousands tokens per second. In this paper, we firstly show that the state-of-the-art performance (94.25) in the CoNLL-2000 shared task based on conventional SVM learning. However, the off-the-shelf SVM classifiers are inefficient when the number of phrase types scales to high. Therefore, we present two novel methods that make the system substantially faster in terms of training and testing while only results in a slightly decrease of system performance. Experimental result shows that our method achieves 94.09 in F rate, which handles 13000 tokens per second in the CoNLL-2000 chunking task.
Year
DOI
Venue
2006
10.1007/11880592_27
AIRS
Keywords
Field
DocType
knowledge-based system,support vector machine,phrase structure,robust phrase,system performance,high performance,state-of-the-art performance,natural language text,automatic text chunk,conll-2000 shared task,phrase types scale,conll-2000 chunking task,knowledge representation,knowledge based system,natural language
Chunking (computing),Noun phrase,Verb phrase,Phrase chunking,Computer science,Support vector machine,Phrase,Speech recognition,Chunking (psychology),Natural language processing,Artificial intelligence,Parsing
Conference
Volume
ISSN
ISBN
4182
0302-9743
3-540-45780-1
Citations 
PageRank 
References 
1
0.37
16
Authors
4
Name
Order
Citations
PageRank
Yu-Chieh Wu124723.16
Jie-Chi Yang235043.91
Yue-Shi Lee354341.14
Show-Jane Yen4537130.05