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Learning to Match and Cluster Entity Names (2001)
| Venue: | In ACM SIGIR-2001 Workshop on Mathematical/Formal Methods in Information Retrieval |
| Citations: | 36 - 1 self |
BibTeX
@INPROCEEDINGS{Cohen01learningto,
author = {William Cohen and Jacob Richman},
title = {Learning to Match and Cluster Entity Names},
booktitle = {In ACM SIGIR-2001 Workshop on Mathematical/Formal Methods in Information Retrieval},
year = {2001}
}
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Abstract
Introduction Information retrieval is, in large part, the study of methods for assessing the similarity of pairs of documents. Document similarity metrics have been used for many tasks including ad hoc document retrieval, text classification [YC1994], and summarization [GC1998,SSMB1997]. Another problem area in which similarity metrics are central is record linkage (e.g., [KA1985]), where one wishes to determine if two database records taken from different source databases refer to the same entity. For instance, one might wish to determine if two database records from two different hospitals, each containing a patient's name, address, and insurance information, refer to the same person; as another example, one might wish to determine if two bibliography records, each containing a paper title, list of authors, and journal name, refer to the same publication. In both of these examples (and in many other practical cases) most of the record fields
Keyphrases
cluster entity name database record different hospital paper title many practical case record linkage bibliography record similarity metric summarization gc1998 problem area ad hoc document retrieval document similarity metric insurance information text classification yc1994 record field journal name different source database many task large part introduction information retrieval
