Dipesh Gautam


DT_Team at SemEval-2017 Task 1: Semantic Similarity Using Alignments, Sentence-Level Embeddings and Gaussian Mixture Model Output
Nabin Maharjan | Rajendra Banjade | Dipesh Gautam | Lasang J. Tamang | Vasile Rus
Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017)

We describe our system (DT Team) submitted at SemEval-2017 Task 1, Semantic Textual Similarity (STS) challenge for English (Track 5). We developed three different models with various features including similarity scores calculated using word and chunk alignments, word/sentence embeddings, and Gaussian Mixture Model(GMM). The correlation between our system’s output and the human judgments were up to 0.8536, which is more than 10% above baseline, and almost as good as the best performing system which was at 0.8547 correlation (the difference is just about 0.1%). Also, our system produced leading results when evaluated with a separate STS benchmark dataset. The word alignment and sentence embeddings based features were found to be very effective.


Evaluation Dataset (DT-Grade) and Word Weighting Approach towards Constructed Short Answers Assessment in Tutorial Dialogue Context
Rajendra Banjade | Nabin Maharjan | Nobal Bikram Niraula | Dipesh Gautam | Borhan Samei | Vasile Rus
Proceedings of the 11th Workshop on Innovative Use of NLP for Building Educational Applications

DTSim at SemEval-2016 Task 1: Semantic Similarity Model Including Multi-Level Alignment and Vector-Based Compositional Semantics
Rajendra Banjade | Nabin Maharjan | Dipesh Gautam | Vasile Rus
Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval-2016)


NeRoSim: A System for Measuring and Interpreting Semantic Textual Similarity
Rajendra Banjade | Nobal Bikram Niraula | Nabin Maharjan | Vasile Rus | Dan Stefanescu | Mihai Lintean | Dipesh Gautam
Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015)