Clique-Based Clustering for Improving Named Entity Recognition Systems

Proceedings of the 12th Conference of the European Chapter of the ACL, pages 51–59, Athens, Greece, 30 March – 3 April 2009. c⃝2009 Association for Computational Linguistics Clique-Based Clustering for improving Named Entity Recognition systems Julien Ah-Pine Xerox Research Centre Europe 6, chemin de Maupertuis 38240 Meylan, France julien.ah-pine@xrce.xerox.com Guillaume Jacquet Xerox Research Centre Europe 6, chemin de Maupertuis 38240 Meylan, France guillaume.jacquet@xrce.xerox.com Abstract We propose a system which builds, in a semi-supervised manner, a resource that aims at helping a NER system to anno- tate corpus-specific named entities. This system is based on a distributional ap- proach which uses syntactic dependen- cies for measuring similarities between named entities. The specificity of the presented method however, is to combine a clique-based approach and a clustering technique that amounts to a soft clustering method. Our experiments show that the resource constructed by using this clique- based clustering system allows to improve different NER systems. 1 Introduction In Information Extraction domain, named entities (NEs) are one of the most important textual units as they express an important part of the meaning of a document. Named entity recognition (NER) is not a new domain (see MUC1 and ACE2 confer- ences) but some new needs appeared concerning NEs processing. For instance the NE Oxford illus- trates the different ambiguity types that are inter- esting to address: • intra-annotation ambiguity: Wikipedia lists more than 25 cities named Oxford in the world • systematic inter-annotation ambiguity: the name of cities could be used to refer to the uni- versity of this city or the football club of this city. This is the case for Oxford or Newcastle • non-systematic inter-annotation ambiguity: Oxford is also a company unlike Newcastle. The main goal of our system is to act in a com- plementary way with an existing NER system, in order to enhance its results. We address two kinds 1http://www-nlpir.nist.gov/related projects/muc/ 2http://www.nist.gov/speech/tests/ace of issues: first, we want to detect and correctly annotate corpus-specific NEs3 that the NER sys- tem could have missed; second, we want to correct some wrong annotations provided by the existing NER system due to ambiguity. In section 3, we give some examples of such corrections. The paper is organized as follows. We present, in section 2, the global architecture of our system and from §2.1 to §2.6, we give details about each of its steps. In section 3, we present the evalu- ation of our approach when it is combined with other classic NER systems. We show that the re- sulting hybrid systems perform better with respect to F-measure. In the best case, the latter increased by 4.84 points. Furthermore, we give examples of successful correction of NEs annotation thanks to our approach. Then, in section 4, we discuss about related works. Finally we sum up the main points of this paper in section 5. 2 Description of the system Given a corpus, the main objectives of our system are: to detect potential NEs; to compute the possi- ble annotations for each NE and …

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