CoTO: A novel approach for fuzzy aggregation of semantic similarity measures |
| |
| Institution: | 1. School of Computer Science, Carleton University, Ottawa, Canada;2. Department of Mathematics and Computer Science, TU Eindhoven, Eindhoven, the Netherlands;3. Computer Science Department, Université libre de Bruxelles (ULB), Brussels, Belgium;1. Lab-STICC (CNRS) Télécom Bretagne, Brest, France;2. European University of Brittany, Université de Bretagne‐Sud Lorient, France;1. LIDIC (Research Group), Universidad Nacional de San Luis, Argentina;2. Natural Language Engineering Lab. – ELiRF, DSIC, Universitat Politècnica de València, Spain;1. College of Automation, Shenyang Aerospace University, Shenyang 110136, PR China;2. Department of Computing, Curtin University, Perth, WA 6102, Australia |
| |
| Abstract: | Semantic similarity measurement aims to determine the likeness between two text expressions that use different lexicographies for representing the same real object or idea. There are a lot of semantic similarity measures for addressing this problem. However, the best results have been achieved when aggregating a number of simple similarity measures. This means that after the various similarity values have been calculated, the overall similarity for a pair of text expressions is computed using an aggregation function of these individual semantic similarity values. This aggregation is often computed by means of statistical functions. In this work, we present CoTO (Consensus or Trade-Off) a solution based on fuzzy logic that is able to outperform these traditional approaches. |
| |
| Keywords: | Knowledge-based analysis Text mining Semantic similarity measurement Fuzzy logic |
| 本文献已被 ScienceDirect 等数据库收录！ |
|
