Learning Semantic Textual Similarity with Structural Representations

Abstract

Measuring semantic textual similarity (STS) is at the cornerstone of many NLP applications. Different from the major-ity of approaches, where a large number of pairwise similarity features are used to represent a text pair, our model features the following: (i) it directly encodes input texts into relational syntactic structures; (ii) relies on tree kernels to handle feature engineering automatically; (iii) combines both structural and feature vector repre-sentations in a single scoring model, i.e., in Support Vector Regression (SVR); and (iv) delivers significant improvement over the best STS systems. 1