Word sense discrimination in information retrieval: A spectral clustering-based approach |
| |
| Authors: | Adrian-Gabriel Chifu Florentina Hristea Josiane Mothe Marius Popescu |
| |
| Institution: | 1. IRIT UMR5505, CNRS, Université de Toulouse, Université Paul Sabatier, 118 Route de Narbonne, F-31062 TOULOUSE CEDEX 9, France;2. University of Bucharest, Faculty of Mathematics and Computer Science, Department of Computer Science, Academiei 14, RO-010014 Bucharest, Romania;3. IRIT UMR5505, CNRS, Université de Toulouse, Ecole Supérieure du Professorat et de l’Education, 118 Route de Narbonne, F-31062 TOULOUSE CEDEX 9, France |
| |
| Abstract: | Word sense ambiguity has been identified as a cause of poor precision in information retrieval (IR) systems. Word sense disambiguation and discrimination methods have been defined to help systems choose which documents should be retrieved in relation to an ambiguous query. However, the only approaches that show a genuine benefit for word sense discrimination or disambiguation in IR are generally supervised ones. In this paper we propose a new unsupervised method that uses word sense discrimination in IR. The method we develop is based on spectral clustering and reorders an initially retrieved document list by boosting documents that are semantically similar to the target query. For several TREC ad hoc collections we show that our method is useful in the case of queries which contain ambiguous terms. We are interested in improving the level of precision after 5, 10 and 30 retrieved documents (P@5, P@10, P@30) respectively. We show that precision can be improved by 8% above current state-of-the-art baselines. We also focus on poor performing queries. |
| |
| Keywords: | Information retrieval Word sense disambiguation Word sense discrimination Spectral clustering High precision |
| 本文献已被 ScienceDirect 等数据库收录！ |
|
