We present in this paper our system developed for SemEval 2015 Shared Task 2 (2a - English Semantic Textual Similarity, STS, and 2c - Interpretable Similarity) and the results of the submitted runs. For the English STS subtask, we used regression models combining a wide array of features including semantic similarity scores obtained from various methods. One of our runs achieved weighted mean correlation score of 0.784 for sentence similarity subtask (i.e., English STS) and was ranked tenth among 74 runs submitted by 29 teams. For the interpretable similarity pilot task, we employed a rule-based approach blended with chunk alignment labeling and scoring based on semantic similarity features. Our system for interpretable text similarity was among the top three best performing systems.
['Rajendra Banjade', 'Nobal B. Niraula', 'Nabin Maharjan', 'Vasile Rus', 'Dan Stefanescu', 'Mihai C. Lintean', 'Dipesh Gautam']
NeRoSim: A System for Measuring and Interpreting Semantic Textual Similarity