Abstract

We present the LIMSI submission to the Multilingual Word Sense Disambiguation and Entity Linking task of SemEval-2015. The system exploits the parallelism of the multilingual test data and uses translations as source of indirect supervision for sense selection. The LIMSI system gets best results in English in all domains and shows that alignment information can successfully guide disambiguation. This simple but effective method can serve to generate high quality sense annotated data for WSD system training.

Highlights

  • This paper describes the LIMSI system at the Multilingual Word Sense Disambiguation (WSD) and Entity Linking (EL) task of SemEval-2015 (Moro and Navigli, 2015)

  • The five best performing systems in both tasks (WSD & EL) and WSD only are compared to the BabelNet First Sense (BFS) baseline

  • We have described the LIMSI system submitted to the SemEval-2015 Multilingual All-Words Sense Disambiguation and Entity Linking task

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Summary

Introduction

This paper describes the LIMSI system at the Multilingual Word Sense Disambiguation (WSD) and Entity Linking (EL) task of SemEval-2015 (Moro and Navigli, 2015). The system performs sense selection by combining translation information obtained through alignment of the multilingual test set with sense ranking. It can be described as semisupervised given the indirect supervision provided by the translations. The alignment correspondences serve as constraints for reducing the search space for each word to BabelNet synsets (hereafter, BabelSynsets) containing the translation and the retained synsets are sorted according to the BabelNet sense ranking. Our goal is to test the contribution of translations in multilingual WSD with no recourse to context information. The system needs no training and can be applied directly to parallel data

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