@inproceedings{lee-etal-2017-purduenlp,
title = "{P}urdue{NLP} at {S}em{E}val-2017 Task 1: Predicting Semantic Textual Similarity with Paraphrase and Event Embeddings",
author = "Lee, I-Ta and
Goindani, Mahak and
Li, Chang and
Jin, Di and
Johnson, Kristen Marie and
Zhang, Xiao and
Pacheco, Maria Leonor and
Goldwasser, Dan",
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/S17-2029",
doi = "10.18653/v1/S17-2029",
pages = "198--202",
abstract = "This paper describes our proposed solution for SemEval 2017 Task 1: Semantic Textual Similarity (Daniel Cer and Specia, 2017). The task aims at measuring the degree of equivalence between sentences given in English. Performance is evaluated by computing Pearson Correlation scores between the predicted scores and human judgements. Our proposed system consists of two subsystems and one regression model for predicting STS scores. The two subsystems are designed to learn Paraphrase and Event Embeddings that can take the consideration of paraphrasing characteristics and sentence structures into our system. The regression model associates these embeddings to make the final predictions. The experimental result shows that our system acquires 0.8 of Pearson Correlation Scores in this task.",
}
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<abstract>This paper describes our proposed solution for SemEval 2017 Task 1: Semantic Textual Similarity (Daniel Cer and Specia, 2017). The task aims at measuring the degree of equivalence between sentences given in English. Performance is evaluated by computing Pearson Correlation scores between the predicted scores and human judgements. Our proposed system consists of two subsystems and one regression model for predicting STS scores. The two subsystems are designed to learn Paraphrase and Event Embeddings that can take the consideration of paraphrasing characteristics and sentence structures into our system. The regression model associates these embeddings to make the final predictions. The experimental result shows that our system acquires 0.8 of Pearson Correlation Scores in this task.</abstract>
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%0 Conference Proceedings
%T PurdueNLP at SemEval-2017 Task 1: Predicting Semantic Textual Similarity with Paraphrase and Event Embeddings
%A Lee, I-Ta
%A Goindani, Mahak
%A Li, Chang
%A Jin, Di
%A Johnson, Kristen Marie
%A Zhang, Xiao
%A Pacheco, Maria Leonor
%A Goldwasser, Dan
%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 lee-etal-2017-purduenlp
%X This paper describes our proposed solution for SemEval 2017 Task 1: Semantic Textual Similarity (Daniel Cer and Specia, 2017). The task aims at measuring the degree of equivalence between sentences given in English. Performance is evaluated by computing Pearson Correlation scores between the predicted scores and human judgements. Our proposed system consists of two subsystems and one regression model for predicting STS scores. The two subsystems are designed to learn Paraphrase and Event Embeddings that can take the consideration of paraphrasing characteristics and sentence structures into our system. The regression model associates these embeddings to make the final predictions. The experimental result shows that our system acquires 0.8 of Pearson Correlation Scores in this task.
%R 10.18653/v1/S17-2029
%U https://aclanthology.org/S17-2029
%U https://doi.org/10.18653/v1/S17-2029
%P 198-202
Markdown (Informal)
[PurdueNLP at SemEval-2017 Task 1: Predicting Semantic Textual Similarity with Paraphrase and Event Embeddings](https://aclanthology.org/S17-2029) (Lee et al., SemEval 2017)
ACL
- I-Ta Lee, Mahak Goindani, Chang Li, Di Jin, Kristen Marie Johnson, Xiao Zhang, Maria Leonor Pacheco, and Dan Goldwasser. 2017. PurdueNLP at SemEval-2017 Task 1: Predicting Semantic Textual Similarity with Paraphrase and Event Embeddings. In Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017), pages 198–202, Vancouver, Canada. Association for Computational Linguistics.