ISCAS_NLP at SemEval-2016 Task 1: Sentence Similarity Based on Support Vector Regression using Multiple Features
Cheng Fu, Bo An, Xianpei Han, Le Sun · 2016
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.