Automating the assignment of submitted manuscripts to reviewers
SIGIR '92 Proceedings of the 15th annual international ACM SIGIR conference on Research and development in information retrieval
Personalized information delivery: an analysis of information filtering methods
Communications of the ACM - Special issue on information filtering
Class-based n-gram models of natural language
Computational Linguistics
Improving text retrieval for the routing problem using latent semantic indexing
SIGIR '94 Proceedings of the 17th annual international ACM SIGIR conference on Research and development in information retrieval
Computational Methods for Intelligent Information Access
Supercomputing '95 Proceedings of the 1995 ACM/IEEE conference on Supercomputing
Introduction to Modern Information Retrieval
Introduction to Modern Information Retrieval
Recommending from content: preliminary results from an e-commerce experiment
CHI '00 Extended Abstracts on Human Factors in Computing Systems
Expert Systems with Applications: An International Journal
The CONCUR framework forcommunity maintenance of curated resources
Proceedings of the eighth ACM symposium on Document engineering
Automatic discovery of synonyms and lexicalizations from the Web
Proceedings of the 2005 conference on Artificial Intelligence Research and Development
Development of new techniques to improve web search
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
The vector space models for finding co-occurrence names as aliases in Thai sports news
ACIIDS'10 Proceedings of the Second international conference on Intelligent information and database systems: Part I
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A common problem faced when gathering information from the web is the use of different names to refer to the same entity. For example, the city in India referred to as Bombay in some documents may be referred to as Mumbai in others because its name officially changed from the former to the latter in 1995. Multiplicity of names can cause relevant documents to be missed by search engines. Our goal is to develop an automated system that discovers additional names for an entity given just one of its names. Latent semantic analysis (LSA) is generally thought to be well-suited for this task [Numerical linear algebra with applications 3(4) (1996) 301]. We demonstrate empirically that under a broad range of circumstances LSA performs poorly, and describe a two-stage algorithm based on LSA that performs significantly better.