@inproceedings{maharjan-etal-2017-dt,
title = "{DT}{\_}{T}eam at {S}em{E}val-2017 Task 1: Semantic Similarity Using Alignments, Sentence-Level Embeddings and {G}aussian Mixture Model Output",
author = "Maharjan, Nabin and
Banjade, Rajendra and
Gautam, Dipesh and
Tamang, Lasang J. and
Rus, Vasile",
editor = "Bethard, Steven and
Carpuat, Marine and
Apidianaki, Marianna and
Mohammad, Saif M. and
Cer, Daniel and
Jurgens, David",
booktitle = "Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017)",
month = aug,
year = "2017",
address = "Vancouver, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/S17-2014",
doi = "10.18653/v1/S17-2014",
pages = "120--124",
abstract = "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.",
}
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<abstract>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.</abstract>
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%0 Conference Proceedings
%T DT_Team at SemEval-2017 Task 1: Semantic Similarity Using Alignments, Sentence-Level Embeddings and Gaussian Mixture Model Output
%A Maharjan, Nabin
%A Banjade, Rajendra
%A Gautam, Dipesh
%A Tamang, Lasang J.
%A Rus, Vasile
%Y Bethard, Steven
%Y Carpuat, Marine
%Y Apidianaki, Marianna
%Y Mohammad, Saif M.
%Y Cer, Daniel
%Y Jurgens, David
%S Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017)
%D 2017
%8 August
%I Association for Computational Linguistics
%C Vancouver, Canada
%F maharjan-etal-2017-dt
%X 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.
%R 10.18653/v1/S17-2014
%U https://aclanthology.org/S17-2014
%U https://doi.org/10.18653/v1/S17-2014
%P 120-124
Markdown (Informal)
[DT_Team at SemEval-2017 Task 1: Semantic Similarity Using Alignments, Sentence-Level Embeddings and Gaussian Mixture Model Output](https://aclanthology.org/S17-2014) (Maharjan et al., SemEval 2017)
ACL