FCICU: The Integration between Sense-Based Kernel and Surface-Based Methods to Measure Semantic Textual Similarity

摘要

This paper describes FCICU team participation in SemEval 2015 for Semantic Textual Similarity challenge. Our main contribution is to propose a word-sense similarity method using BabelNet relationships. In the English subtask challenge, we submitted three systems (runs) to assess the proposed method. In Run1, we used our proposed method coupled with a string kernel mapping function to calculate the textual similarity. In Run2, we used the method with a tree kernel function. In Run3, we averaged Runl with a previously proposed surface-based approach as a kind of integration. The three runs are ranked 41st, 57th, and 20th of 73 systems, with mean correlation 0.702, 0.597, and 0.759 respectively. For the interpretable task, we submitted a modified version of Runl achieving mean F1 0.846, 0.461, 0.722, and 0.44 for alignment, type, score, and score with type respectively.