DTSim at SemEval-2016 Task 1: Semantic similarity model including multi-level alignment and vector-based compositional semantics
Abstract
In this paper we describe our system (DT-Sim) submitted at SemEval-2016 Task 1: Semantic Textual Similarity (STS Core). We developed Support Vector Regression model with various features including the similarity scores calculated using alignment based methods and semantic composition based methods. The correlations between our system output and the human ratings were above 0.8 in three datasets.
Publication Title
SemEval 2016 - 10th International Workshop on Semantic Evaluation, Proceedings
Recommended Citation
Banjade, R., Maharjan, N., Gautam, D., & Rus, V. (2016). DTSim at SemEval-2016 Task 1: Semantic similarity model including multi-level alignment and vector-based compositional semantics. SemEval 2016 - 10th International Workshop on Semantic Evaluation, Proceedings, 640-644. https://doi.org/10.18653/v1/s16-1097
