Unsupervised Personal Name Disambiguation

By Gideon S. Mann, David Yarowsky. Conference on Computational Natural Language Learning, 2003

376 citations recorded by the source corpus.

This paper presents a set of algorithms for distinguishing personal names with multiple real referents in text, based on little or no supervision. The approach utilizes an unsupervised clustering technique over a rich feature space of biographic facts, which are automatically extracted via a language-independent bootstrapping process. The induced clustering of named entities are then partitioned and linked to their real referents via the automatically extracted biographic data. Performance is evaluated based on both a test set of handlabeled multi-referent personal names and via automatically generated pseudonames.

Subfields: Artificial Intelligence, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/4fcc7e9c449e8a6e04c00ce38967e706c4540591

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In Artificial Intelligence

In Management Science and Operations Research

Citations in the corpus

Entity Linking with a Knowledge Base: Issues, Techniques, and Solutions (2015, background): “In the entity coreference resolution problem [25], [26], [27], [28], [29], [30], entity mentions within one document or across multiple documents are clustered into several different clusters each of which represents one specific entity, based on the entity mention itself, context, and…”
Entity Disambiguation for Knowledge Base Population (2010, neutral): “Motivated by ambiguity in personal name search, Mann and Yarowsky (2003) disambiguate person names using biographic facts, like birth year, occupation and afﬁliation.”
Large-Scale Named Entity Disambiguation Based on Wikipedia Data (2007, background): “Mann and Yarowsky (2003) addressed the task of clustering the Web search re-sults for a set of ambiguous personal names by employing a rich feature space of biographic facts obtained via bootstrapped extraction patterns.”
Disambiguating Web appearances of people in a social network (2005, methodology): “There has been some research on person name disambiguation in the Web domain [2, 13, 7], within the general framework of entity coreference (see, e.”
Making sense of credibility on the Web: Models for evaluating online information and recommendations for future research (2007, background): “... in the citation indexing systems calls for methods that distinguish the contexts in which authors or individuals appear, such as cross-document co-reference resolution algorithms that use the vector space model to resolve ambiguities (Bagga & Baldwin, 1998; Gooi & Allan, 2004; Han, Giles, Zha, L”
Entity Linking: Finding Extracted Entities in a Knowledge Base (2013, methodology): “To address ambiguity in personal name search, Mann and Yarowsky [26] disambiguates person names using biographic facts, like birth year, occupation and affiliation.”
Author Name Disambiguation in MEDLINE (2009, background): “2005], and entity resolution, for example, mentions of a personal name across multiple different websites [Mann and Yarowsky 2003].”
POLYPHONET: an advanced social network extraction system from the web (2006, background): “In addition, the natural language community has specifically addressed name disambiguation as a class of word sense disambiguation [45, 28].”
Evaluating Entity Linking with Wikipedia (2013, background): “For instance, Mann and Yarowsky (2003) collected a data set of web pages returned from 32 search engine queries for person names sampled from US census data.”