Title
Experiments In Learning Models For Functional Chunking Of Chinese Text
Abstract
This paper introduces a system of chunk-like annotation to describe Chinese predicate-argument structures, and describe some of our work in developing learned models for automatically annotating fresh text according to this system. The annotation is very similar in form to other chunking systems, except that chunks are defined not bottom-up but top-down, in terms of relationship to a main predicate. Bottom-up parsing of these structures seems to require great consideration of structural information and long-distance influences. Explicit representation of chunk structure during parsing allows us to provide more informative features, and experiments show that these give significant improvements in performance.
Year
DOI
Venue
2001
null
2001 IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN, AND CYBERNETICS, VOLS 1-5: E-SYSTEMS AND E-MAN FOR CYBERNETICS IN CYBERSPACE
Keywords
Field
DocType
chunking, partial parsing, predicate -argument structure
Rule-based machine translation,Chunking (computing),Annotation,Intelligent decision support system,Computer science,Computational linguistics,Natural language,Chunking (psychology),Natural language processing,Artificial intelligence,Parsing,Machine learning
Conference
Volume
Issue
ISSN
2
null
1062-922X
Citations 
PageRank 
References 
2
0.42
3
Authors
2
Name
Order
Citations
PageRank
Elliott Franco Drábek120616.02
Qiang Zhou231.79