RelaxCor: A Global Relaxation Labeling Approach to Coreference Resolution

Proceedings of the 5th International Workshop on Semantic Evaluation, ACL 2010, pages 88–91, Uppsala, Sweden, 15-16 July 2010. c⃝2010 Association for Computational Linguistics RelaxCor: A Global Relaxation Labeling Approach to Coreference Resolution Emili Sapena, Llu´ıs Padr´o and Jordi Turmo TALP Research Center Universitat Polit`ecnica de Catalunya Barcelona, Spain {esapena, padro, turmo}@lsi.upc.edu Abstract This paper describes the participation of RelaxCor in the Semeval-2010 task number 1: “Coreference Resolution in Multiple Languages“. RelaxCor is a constraint-based graph partitioning ap- proach to coreference resolution solved by relaxation labeling. The approach com- bines the strengths of groupwise classifiers and chain formation methods in one global method. 1 Introduction The Semeval-2010 task is concerned with intra- document coreference resolution for six different languages: Catalan, Dutch, English, German, Ital- ian and Spanish. The core of the task is to iden- tify which noun phrases (NPs) in a text refer to the same discourse entity (Recasens et al., 2010). RelaxCor (Sapena et al., 2010) is a graph rep- resentation of the problem solved by a relaxation labeling process, reducing coreference resolution to a graph partitioning problem given a set of con- straints. In this manner, decisions are taken con- sidering the whole set of mentions, ensuring con- sistency and avoiding that classification decisions are independently taken. The paper is organized as follows. Section 2 de- scribes RelaxCor, the system used in the Semeval task. Next, Section 3 describes the tuning needed by the system to adapt it to different languages and other task issues. The same section also analyzes the obtained results. Finally, Section 4 concludes the paper. 2 System Description This section briefly describes RelaxCor. First, the graph representation is presented. Next, there is an explanation of the methodology used to learn constraints and train the system. Finally, the algo- rithm used for resolution is described. 2.1 Problem Representation Let G = G(V, E) be an undirected graph where V is a set of vertices and E a set of edges. Let m = (m1, ..., mn) be the set of mentions of a document with n mentions to resolve. Each mention mi in the document is represented as a vertex vi ∈ V in the graph. An edge eij ∈ E is added to the graph for pairs of vertices (vi, vj) representing the possibility that both mentions corefer. Let C be our set of constraints. Given a pair of mentions (mi, mj), a subset of constraints Cij ⊆ C restrict the compatibility of both mentions. Cij is used to compute the weight value of the edge connecting vi and vj. Let wij ∈ W be the weight of the edge eij: wij = � k∈Cij λkfk(mi, mj) (1) where fk(·) is a function that evaluates the con- straint k and λk is the weight associated to the constraint. Note that λk and wij can be negative. In our approach, each vertex (vi) in the graph is a variable (vi) for the algorithm. Let Li be the number of different values (labels) that are possi- …

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