@inproceedings{galbraith-etal-2017-talla,
title = "Talla at {S}em{E}val-2017 Task 3: Identifying Similar Questions Through Paraphrase Detection",
author = "Galbraith, Byron and
Pratap, Bhanu and
Shank, Daniel",
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/S17-2062",
doi = "10.18653/v1/S17-2062",
pages = "375--379",
abstract = "This paper describes our approach to the SemEval-2017 shared task of determining question-question similarity in a community question-answering setting (Task 3B). We extracted both syntactic and semantic similarity features between candidate questions, performed pairwise-preference learning to optimize for ranking order, and then trained a random forest classifier to predict whether the candidate questions are paraphrases of each other. This approach achieved a MAP of 45.7{\%} out of max achievable 67.0{\%} on the test set.",
}
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<abstract>This paper describes our approach to the SemEval-2017 shared task of determining question-question similarity in a community question-answering setting (Task 3B). We extracted both syntactic and semantic similarity features between candidate questions, performed pairwise-preference learning to optimize for ranking order, and then trained a random forest classifier to predict whether the candidate questions are paraphrases of each other. This approach achieved a MAP of 45.7% out of max achievable 67.0% on the test set.</abstract>
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%0 Conference Proceedings
%T Talla at SemEval-2017 Task 3: Identifying Similar Questions Through Paraphrase Detection
%A Galbraith, Byron
%A Pratap, Bhanu
%A Shank, Daniel
%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 galbraith-etal-2017-talla
%X This paper describes our approach to the SemEval-2017 shared task of determining question-question similarity in a community question-answering setting (Task 3B). We extracted both syntactic and semantic similarity features between candidate questions, performed pairwise-preference learning to optimize for ranking order, and then trained a random forest classifier to predict whether the candidate questions are paraphrases of each other. This approach achieved a MAP of 45.7% out of max achievable 67.0% on the test set.
%R 10.18653/v1/S17-2062
%U https://aclanthology.org/S17-2062
%U https://doi.org/10.18653/v1/S17-2062
%P 375-379
Markdown (Informal)
[Talla at SemEval-2017 Task 3: Identifying Similar Questions Through Paraphrase Detection](https://aclanthology.org/S17-2062) (Galbraith et al., SemEval 2017)
ACL