Multilingual Coreference Resolution

Multilingual Coreference Resolution Sanda M. Harabagiu Southern Methodist University Dallas, TX 75275-0122 sanda@seas, smu. edu Steven J. Maiorano IPO Washington, D.C. 20505 maiorano@cais, com Abstract In this paper we present a new, multi- lingual data-driven method for coreference resolution as implemented in the SWIZZLE system. The results obtained after training this system on a bilingual corpus of English and Romanian tagged texts, outperformed coreference resolution in each of the indi- vidual languages. 1 Introduction The recent availability of large bilingual corpora has spawned interest in several areas of multilingual text processing. Most of the research has focused on bilingual terminology identification, either as par- allel multiwords forms (e.g. the ChampoUion sys- tem (Smadja et a1.1996)), technical terminology (e.g. the Termight system (Dagan and Church, 1994) or broad-coverage translation lexicons (e.g. the SABLE system (Resnik and Melamed, 1997)). In addition, the Multilingual Entity Task (MET) from the TIP- STER program 1 (http://www-nlpir.nist.gov/related- projeets/tipster/met.htm) challenged the partici- pants in the Message Understanding Conference (MUC) to extract named entities across several for- eign language corpora, such as Chinese, Japanese and Spanish. In this paper we present a new application of aligned multilinguai texts. Since coreference reso- lution is a pervasive discourse phenomenon causing performance impediments in current IE systems, we considered a corpus of aligned English and Roma- nian texts to identify coreferring expressions. Our task focused on the same kind of coreference as considered in the past MUC competitions, namely 1The TIPSTER Text Program was a DARPA-Ied government effort to advance the state of the art in text processing technologies. the identity coreference. Identity coreference links nouns, pronouns and noun phrases (including proper names) to their corresponding antecedents. We created our bilingual collection by translating the MUC-6 and MUC-7 coreference training texts into Romanian using native speakers. The train- ing data set for Romanian coreference used, wher- ever possible, the same coreference identifiers as the English data and incorporated additional tags as needed. Our claim is that by adding the wealth of coreferential features provided by multilingual data, new powerful heuristics for coreference resolu- tion can be developed that outperform monolingual coreference resolution systems. For both languages, we resolved coreference by using SWIZZLE, our implementation of a bilingual coreference resolver. SWIZZLE is a multilingual en- hancement of COCKTAIL (Harabagiu and Maiorano, 1999), a coreference resolution system that operates on a mixture of heuristics that combine semantic and textual cohesive information 2. When COCKTAIL was applied separately on the English and the Ro- manian texts, coreferring links were identified for each English and Romanian document respectively. When aligned referential expressions corefer with non-aligned anaphors, SWIZZLE derived new heuris- tics for coreference. Our experiments show that SWIZZLE outperformed COCKTAIL on both English and Romanian test documents. The rest of the paper is organized as follows. Sec- tion 2 presents COCKTAIL, a monolingnai coreference resolution system used separately on both the En- glish and Romanian texts. Section 3 details the data-driven approach used in SWIZZLE and presents some of its resources. Section 4 reports and …

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