Learning to Match and Cluster Entity Names

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

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No public copy is stored for this record, so only the metadata is cited.

Stored only when both papers are in the corpus — 53 citations exist upstream.

Papers that cite the same references — the relational fallback while embeddings are still backfilling.