ECNU at SemEval-2018 Task 10: Evaluating Simple but Effective Features on Machine Learning Methods for Semantic Difference Detection

Yunxiao Zhou; Man Lan; Yuanbin Wu

doi:10.18653/v1/s18-1165

ECNU at SemEval-2018 Task 10: Evaluating Simple but Effective Features on Machine Learning Methods for Semantic Difference Detection

Yunxiao Zhou
, Man Lan^*
, Yuanbin Wu

^*Corresponding author for this work

School of Computer Science and Technology

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

2 Scopus citations

Abstract

This paper describes the system we submitted to Task 10 (Capturing Discriminative Attributes) in SemEval 2018. Given a triple (word₁, word₂, attribute), this task is to predict whether it exemplifies a semantic difference or not. We design and investigate several word embedding features, PMI features and WordNet features together with supervised machine learning methods to address this task. Officially released results show that our system ranks above average.

Original language	English
Title of host publication	NAACL HLT 2018 - International Workshop on Semantic Evaluation, SemEval 2018 - Proceedings of the 12th Workshop
Editors	Marianna Apidianaki, Marianna Apidianaki, Saif M. Mohammad, Jonathan May, Ekaterina Shutova, Steven Bethard, Marine Carpuat
Publisher	Association for Computational Linguistics (ACL)
Pages	999-1002
Number of pages	4
ISBN (Electronic)	9781948087209
DOIs	https://doi.org/10.18653/v1/s18-1165
State	Published - 2018
Event	12th International Workshop on Semantic Evaluation, SemEval 2018, co-located with the 16th Annual Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL HLT 2018 - New Orleans, United States Duration: 5 Jun 2018 → 6 Jun 2018

Publication series

Name	NAACL HLT 2018 - International Workshop on Semantic Evaluation, SemEval 2018 - Proceedings of the 12th Workshop

Conference

Conference	12th International Workshop on Semantic Evaluation, SemEval 2018, co-located with the 16th Annual Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL HLT 2018
Country/Territory	United States
City	New Orleans
Period	5/06/18 → 6/06/18

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Zhou, Y., Lan, M., & Wu, Y. (2018). ECNU at SemEval-2018 Task 10: Evaluating Simple but Effective Features on Machine Learning Methods for Semantic Difference Detection. In M. Apidianaki, M. Apidianaki, S. M. Mohammad, J. May, E. Shutova, S. Bethard, & M. Carpuat (Eds.), NAACL HLT 2018 - International Workshop on Semantic Evaluation, SemEval 2018 - Proceedings of the 12th Workshop (pp. 999-1002). (NAACL HLT 2018 - International Workshop on Semantic Evaluation, SemEval 2018 - Proceedings of the 12th Workshop). Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/s18-1165

Zhou, Yunxiao ; Lan, Man ; Wu, Yuanbin. / ECNU at SemEval-2018 Task 10 : Evaluating Simple but Effective Features on Machine Learning Methods for Semantic Difference Detection. NAACL HLT 2018 - International Workshop on Semantic Evaluation, SemEval 2018 - Proceedings of the 12th Workshop. editor / Marianna Apidianaki ; Marianna Apidianaki ; Saif M. Mohammad ; Jonathan May ; Ekaterina Shutova ; Steven Bethard ; Marine Carpuat. Association for Computational Linguistics (ACL), 2018. pp. 999-1002 (NAACL HLT 2018 - International Workshop on Semantic Evaluation, SemEval 2018 - Proceedings of the 12th Workshop).

@inproceedings{c45e8048dc9144ff847c3863615e4bbc,

title = "ECNU at SemEval-2018 Task 10: Evaluating Simple but Effective Features on Machine Learning Methods for Semantic Difference Detection",

abstract = "This paper describes the system we submitted to Task 10 (Capturing Discriminative Attributes) in SemEval 2018. Given a triple (word1, word2, attribute), this task is to predict whether it exemplifies a semantic difference or not. We design and investigate several word embedding features, PMI features and WordNet features together with supervised machine learning methods to address this task. Officially released results show that our system ranks above average.",

author = "Yunxiao Zhou and Man Lan and Yuanbin Wu",

note = "Publisher Copyright: {\textcopyright} 2018 Association for Computational Linguistics; 12th International Workshop on Semantic Evaluation, SemEval 2018, co-located with the 16th Annual Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL HLT 2018 ; Conference date: 05-06-2018 Through 06-06-2018",

year = "2018",

doi = "10.18653/v1/s18-1165",

language = "英语",

series = "NAACL HLT 2018 - International Workshop on Semantic Evaluation, SemEval 2018 - Proceedings of the 12th Workshop",

publisher = "Association for Computational Linguistics (ACL)",

pages = "999--1002",

editor = "Marianna Apidianaki and Marianna Apidianaki and Mohammad, \{Saif M.\} and Jonathan May and Ekaterina Shutova and Steven Bethard and Marine Carpuat",

booktitle = "NAACL HLT 2018 - International Workshop on Semantic Evaluation, SemEval 2018 - Proceedings of the 12th Workshop",

address = "澳大利亚",

}

Zhou, Y, Lan, M & Wu, Y 2018, ECNU at SemEval-2018 Task 10: Evaluating Simple but Effective Features on Machine Learning Methods for Semantic Difference Detection. in M Apidianaki, M Apidianaki, SM Mohammad, J May, E Shutova, S Bethard & M Carpuat (eds), NAACL HLT 2018 - International Workshop on Semantic Evaluation, SemEval 2018 - Proceedings of the 12th Workshop. NAACL HLT 2018 - International Workshop on Semantic Evaluation, SemEval 2018 - Proceedings of the 12th Workshop, Association for Computational Linguistics (ACL), pp. 999-1002, 12th International Workshop on Semantic Evaluation, SemEval 2018, co-located with the 16th Annual Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL HLT 2018, New Orleans, United States, 5/06/18. https://doi.org/10.18653/v1/s18-1165

ECNU at SemEval-2018 Task 10: Evaluating Simple but Effective Features on Machine Learning Methods for Semantic Difference Detection. / Zhou, Yunxiao; Lan, Man; Wu, Yuanbin.
NAACL HLT 2018 - International Workshop on Semantic Evaluation, SemEval 2018 - Proceedings of the 12th Workshop. ed. / Marianna Apidianaki; Marianna Apidianaki; Saif M. Mohammad; Jonathan May; Ekaterina Shutova; Steven Bethard; Marine Carpuat. Association for Computational Linguistics (ACL), 2018. p. 999-1002 (NAACL HLT 2018 - International Workshop on Semantic Evaluation, SemEval 2018 - Proceedings of the 12th Workshop).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Wu, Yuanbin

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AB - This paper describes the system we submitted to Task 10 (Capturing Discriminative Attributes) in SemEval 2018. Given a triple (word1, word2, attribute), this task is to predict whether it exemplifies a semantic difference or not. We design and investigate several word embedding features, PMI features and WordNet features together with supervised machine learning methods to address this task. Officially released results show that our system ranks above average.

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Zhou Y, Lan M, Wu Y. ECNU at SemEval-2018 Task 10: Evaluating Simple but Effective Features on Machine Learning Methods for Semantic Difference Detection. In Apidianaki M, Apidianaki M, Mohammad SM, May J, Shutova E, Bethard S, Carpuat M, editors, NAACL HLT 2018 - International Workshop on Semantic Evaluation, SemEval 2018 - Proceedings of the 12th Workshop. Association for Computational Linguistics (ACL). 2018. p. 999-1002. (NAACL HLT 2018 - International Workshop on Semantic Evaluation, SemEval 2018 - Proceedings of the 12th Workshop). doi: 10.18653/v1/s18-1165

ECNU at SemEval-2018 Task 10: Evaluating Simple but Effective Features on Machine Learning Methods for Semantic Difference Detection

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