Paper Info
Title
Detecting Subject Boundaries Within Text: A Language Independent Statistical Approach
Abstract
We describe here an algorithm for detect- ing subject boundaries within text based on a statistical lexical similarity measure. Hearst has already tackled this problem with good results (Hearst, 1994). One of her main assumptions is that a change in subject is accompanied by a change in vo- cabulary. Using this assumption, but by introducing a new measure of word signif- icance, we have been able to build a ro- bust and reliable algorithm which exhibits improved accuracy without sacrificing lan- guage independency.
Year
Venue
Field
1997
EMNLP
Computer science,Speech recognition,Artificial intelligence,Natural language processing
DocType
Volume
Citations 
Conference
W97-03
17
PageRank 
References 
Authors
2.93
5
3
Name
Order
Citations
PageRank
Korin Richmond153146.14
Andrew Smith2276.55
Einat Amitay359754.66