Computing Semantic Text Similarity Using Rich Features
Yang Liu, Chengjie Sun, Lei Lin, Xiaolong Wang, Yuming Zhao · Institutional Repositories DataBase (IRDB) · 2015
Semantic text similarity (STS) is an essential problem in many Natural Language Pro-cessing tasks, which has drawn a considerable amount of attention by research community in recent years. In this paper, our work focused on computing semantic similarity between texts of sentence length. We employed a Sup-port Vector Regression model with rich effec-tive features to predict the similarity scores between short English sentence pairs. Our model used WordNet-Based features, Corpus-Based features, Word2Vec-based features, Alignment-Based feature and Literal-Based features to cover various aspects of sentences.