Database Ralationship
Linking Named Entities to Any Database
Avirup Sil∗ Temple University Philadelphia, PA avi@temple.edu Yinfei Yang St. Joseph’s University Philadelphia, PA yangyin7@gmail.com Abstract
Existing techniques for disambiguating named entities in text mostly focus on Wikipedia as a target catalog of entities. Yet for many types of entities, such as restaurants and cult movies, relational databases exist that contain far more extensive information than Wikipedia. This paper introduces a new task, called Open-Database Named-Entity Disambiguation (Open-DB NED), in which a system must be able to resolve named entities to symbols in an arbitrary database, without requiring labeled data for each new database. We introduce two techniques for Open-DB NED, one based on distant supervision and the other based on domain adaptation. In experiments on two domains, one with poor coverage by Wikipedia and the other with near-perfect coverage, our Open-DB NED strategies outperform a state-of-the-art Wikipedia NED system by over 25% in accuracy.
Ernest Cronin∗ Penghai Nie St. Joseph’s University St. Joseph’s University Philadelphia, PA Philadelphia, PA ernest.cronin@gmail.com nph87903@gmail.com Ana-Maria Popescu Yahoo! Labs Sunnyvale, CA amp@yahoo-inc.com Alexander Yates Temple University Philadelphia, PA yates@temple.edu
referents, but exclusive focus on Wikipedia as a target for NED systems has significant drawbacks: despite its breadth, Wikipedia still does not contain all or even most real-world entities mentioned in text. As one example, it has poor coverage of entities that are mostly important in a small geographical region, such as hotels and restaurants, which are widely discussed on the Web. 57% of the named-entities in the Text Analysis Conference’s (TAC) 2009 entity linking task refer to an entity that does not appear in Wikipedia (McNamee et al., 2009). Wikipedia is clearly a highly valuable resource, but it should not be thought of as the only one. Instead of relying
Avirup Sil∗ Temple University Philadelphia, PA avi@temple.edu Yinfei Yang St. Joseph’s University Philadelphia, PA yangyin7@gmail.com Abstract
Existing techniques for disambiguating named entities in text mostly focus on Wikipedia as a target catalog of entities. Yet for many types of entities, such as restaurants and cult movies, relational databases exist that contain far more extensive information than Wikipedia. This paper introduces a new task, called Open-Database Named-Entity Disambiguation (Open-DB NED), in which a system must be able to resolve named entities to symbols in an arbitrary database, without requiring labeled data for each new database. We introduce two techniques for Open-DB NED, one based on distant supervision and the other based on domain adaptation. In experiments on two domains, one with poor coverage by Wikipedia and the other with near-perfect coverage, our Open-DB NED strategies outperform a state-of-the-art Wikipedia NED system by over 25% in accuracy.
Ernest Cronin∗ Penghai Nie St. Joseph’s University St. Joseph’s University Philadelphia, PA Philadelphia, PA ernest.cronin@gmail.com nph87903@gmail.com Ana-Maria Popescu Yahoo! Labs Sunnyvale, CA amp@yahoo-inc.com Alexander Yates Temple University Philadelphia, PA yates@temple.edu
referents, but exclusive focus on Wikipedia as a target for NED systems has significant drawbacks: despite its breadth, Wikipedia still does not contain all or even most real-world entities mentioned in text. As one example, it has poor coverage of entities that are mostly important in a small geographical region, such as hotels and restaurants, which are widely discussed on the Web. 57% of the named-entities in the Text Analysis Conference’s (TAC) 2009 entity linking task refer to an entity that does not appear in Wikipedia (McNamee et al., 2009). Wikipedia is clearly a highly valuable resource, but it should not be thought of as the only one. Instead of relying
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