DTSim at SemEval-2016 Task 2: Interpreting Similarity of Texts Based on Automated Chunking, Chunk Alignment and Semantic Relation Prediction

In this paper we describe our system (DTSim) submitted at SemEval-2016 Task 2: Interpretable Semantic Textual Similarity (iSTS). We participated in both gold chunks category (texts chunked by human experts and provided by the task organizers) and system chunks category (participants had to automatically chunk the input texts). We developed a Conditional Random Fields based chunker and applied rules blended with semantic similarity methods in order to predict chunk alignments, alignment types and similarity scores. Our system obtained F1 score up to 0.648 in predicting the chunk alignment types and scores together and was one of the top performing systems overall.