Two supervised learning approaches for name disambiguation in author citations

Abstract

Due to name abbreviations, identical names, name misspellings, and pseudonyms inpublications or bibliographies (citations), an author may have multiple names and multiple authors may share the same name. Such name ambiguity affects the performance of document retrieval, web search, database integration, and may cause improper attribution to authors. This paper investigates two supervised learning approaches to disambiguate authors in the citations. One approach uses the naive Bayes probability model, a generative model; the other uses Support Vector Machines(SVMs) and the vector space representation of citations, a discriminative model. Both approaches utilize three types of citation attributes: co-author names, the title of the paper, and the title of the journal or proceeding. We illustrate these two approaches on two types of data, one collected from the web, mainly publication lists from homepages, the other collected from the DBLPcitation databases.

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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 — 366 citations exist upstream.

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