ISCAS-NLP at SemEval-2016 task 1: Sentence similarity based on support vector regression using multiple features

Abstract

This paper describes our system developed for English Monolingual subtask (STS Core) of SemEval-2016 Task 1: "Semantic Textual Similarity: A Unified Framework for Semantic Processing and Evaluation". We measure the similarity between two sentences using three different types of features, including word alignment-based similarity, sentence vector-based similarity and sentence constituent similarity. The best performance of our submitted runs is a mean 0.69996 Pearson correlation which outperforms the median score from all participating systems.