Structural, Transitive and Latent Models for Biographic Fact Extraction

Proceedings of the 12th Conference of the European Chapter of the ACL, pages 300–308, Athens, Greece, 30 March – 3 April 2009. c⃝2009 Association for Computational Linguistics Structural, Transitive and Latent Models for Biographic Fact Extraction Nikesh Garera and David Yarowsky Department of Computer Science, Johns Hopkins University Human Language Technology Center of Excellence Baltimore MD, USA {ngarera,yarowsky}@cs.jhu.edu Abstract This paper presents six novel approaches to biographic fact extraction that model structural, transitive and latent proper- ties of biographical data. The ensem- ble of these proposed models substantially outperforms standard pattern-based bio- graphic fact extraction methods and per- formance is further improved by modeling inter-attribute correlations and distribu- tions over functions of attributes, achiev- ing an average extraction accuracy of 80% over seven types of biographic attributes. 1 Introduction Extracting biographic facts such as “Birthdate”, “Occupation”, “Nationality”, etc. is a critical step for advancing the state of the art in information processing and retrieval. An important aspect of web search is to be able to narrow down search results by distinguishing among people with the same name leading to multiple efforts focusing on web person name disambiguation in the liter- ature (Mann and Yarowsky, 2003; Artiles et al., 2007, Cucerzan, 2007). While biographic facts are certainly useful for disambiguating person names, they also allow for automatic extraction of ency- lopedic knowledge that has been limited to man- ual efforts such as Britannica, Wikipedia, etc. Such encyploedic knowledge can advance verti- cal search engines such as http://www.spock.com that are focused on people searches where one can get an enhanced search interface for searching by various biographic attributes. Biographic facts are also useful for powerful query mechanisms such as finding what attributes are common between two people (Auer and Lehmann, 2007). Figure 1: Goal: extracting attribute-value bio- graphic fact pairs from biographic free-text While there are a large quantity of biographic texts available online, there are only a few biographic fact databases available1, and most of them have been created manually, are incomplete and are available primarily in English. This work presents multiple novel approaches for automatically extracting biographic facts such as “Birthdate”, “Occupation”, “Nationality”, and “Religion”, making use of diverse sources of in- formation present in biographies. In particular, we have proposed and evaluated the following 6 distinct original approaches to this 1E.g.: http://www.nndb.com, http://www.biography.com, Infoboxes in Wikipedia 300 task with large collective empirical gains: 1. An improvement to the Ravichandran and Hovy (2002) algorithm based on Partially Untethered Contextual Pattern Models 2. Learning a position-based model using ab- solute and relative positions and sequential order of hypotheses that satisfy the domain model. For example, “Deathdate” very often appears after “Birthdate” in a biography. 3. Using transitive models over attributes via co-occurring entities. For example, other people mentioned person’s biography page tend to have similar attributes such as occu- pation (See Figure 4). 4. Using latent wide-document-context models to detect attributes that may not be mentioned directly in the article (e.g. the words “song, hits, album, recorded,..” all collectively indi- cate …

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