Profile Based Cross-Document Coreference Using Kernelized Fuzzy Relational Clustering

Proceedings of the 47th Annual Meeting of the ACL and the 4th IJCNLP of the AFNLP, pages 414–422, Suntec, Singapore, 2-7 August 2009. c⃝2009 ACL and AFNLP Profile Based Cross-Document Coreference Using Kernelized Fuzzy Relational Clustering Jian Huang† Sarah M. Taylor‡ Jonathan L. Smith‡ Konstantinos A. Fotiadis‡ C. Lee Giles† †College of Information Sciences and Technology Pennsylvania State University, University Park, PA 16802, USA {jhuang, giles}@ist.psu.edu ‡Advanced Technology Office, Lockheed Martin IS&GS, Arlington, VA 22203, USA {sarah.m.taylor, jonathan.l.smith, konstantinos.a.fotiadis}@lmco.com Abstract inCoreferencing entities across documents a large corpus enables advanced document understanding tasks such as aquestion answering. This paper presents novel cross document coreference approach that leverages the profiles of entities which are constructed by using information extraction tools and reconciled by using a within-document coreference module. We propose to match the profiles by using a learned ensemble distance function comprised of a suite of similarity specialists. We develop a kernelized soft relational clustering algorithm that makes use of the learned distance function to partition the entities into fuzzy sets of identities. We compare the kernelized clustering method with a popular fuzzy relation clustering algorithm (FRC) and show 5% improvement in coreference performance. Evaluation of our proposed methods on a large benchmark disambiguation collection shows that they compare favorably with the top runs in the SemEval evaluation. 1 Introduction A named entity that represents a person, an or- ganization or a geo-location may appear within and across documents in different forms. Cross document coreference (CDC) is the task of con- solidating named entities that appear in multiple documents according to their real referents. CDC is a stepping stone for achieving intelligent in- formation access to vast and heterogeneous text corpora, which includes advanced NLP techniques such as document summarization and question an- swering. A related and well studied task is within document coreference (WDC), which limits the scope of disambiguation to within the boundary of a document. When namesakes appear in an article, the author can explicitly help to disambiguate, us- ing titles and suffixes (as in the example, “George Bush Sr. ... the younger Bush”) besides other means. Cross document coreference, on the other hand, is a more challenging task because these linguistics cues and sentence structures no longer apply, given the wide variety of context and styles in different documents. Cross document coreference research has re- cently become more popular due to the increasing interests in the web person search task (Artiles et al., 2007). Here, a search query for a person name is entered into a search engine and the desired outputs are documents clustered according to the identities of the entities in question. In our work, we propose to drill down to the sub- document mention level and construct an entity profile with the support of information extraction tools and reconciled with WDC methods. Hence our IE based approach has access to accurate information such as a person’s mentions and geo- locations for disambiguation. Simple IR based CDC approaches (e.g. (Gooi and Allan, 2004)), on the other …

Content truncated to 500 words