Structural Representations for Learning Relations between Pairs of Texts

This paper studies the use of structural representations for learning relations be-tween pairs of short texts (e.g., sentences or paragraphs) of the kind: the second text answers to, or conveys exactly the same information of, or is implied by, the first text. Engineering effective features that can capture syntactic and semantic re-lations between the constituents compos-ing the target text pairs is rather complex. Thus, we define syntactic and semantic structures representing the text pairs and then apply graph and tree kernels to them for automatically engineering features in Support Vector Machines. We carry out an extensive comparative analysis of state-of-the-art models for this type of relational learning. Our findings allow for achiev-ing the highest accuracy in two differ-ent and important related tasks, i.e., Para