Inspire at SemEval-2016 task 2: Interpretable semantic textual similarity alignment based on answer set programming

Abstract

In this paper we present our system developed for the SemEval 2016 Task 2 - Interpretable Semantic Textual Similarity along with the results obtained for our submitted runs. Our system participated in the subtasks predicting chunk similarity alignments for gold chunks as well as for predicted chunks. The Inspire system extends the basic ideas from last years participant NeRoSim, however we realize the rules in logic programming and obtain the result with an Answer Set Solver. To prepare the input for the logic program, we use the PunktTokenizer, Word2Vec, and WordNet APIs of NLTK, and the POS- and NER-taggers from Stanford CoreNLP. For chunking we use a joint POS-tagger and dependency parser and based on that determine chunks with an Answer Set Program. Our system ranked third place overall and first place in the Headlines gold chunk subtask.