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

^*此作品的通讯作者

计算机科学与技术学院

East China Normal University
Shanghai Key Laboratory of Multidimensional Information Processing

科研成果: 书/报告/会议事项章节 › 会议稿件 › 同行评审

2 引用（Scopus）

摘要

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.

源语言	英语
主期刊名	NAACL HLT 2018 - International Workshop on Semantic Evaluation, SemEval 2018 - Proceedings of the 12th Workshop
编辑	Marianna Apidianaki, Marianna Apidianaki, Saif M. Mohammad, Jonathan May, Ekaterina Shutova, Steven Bethard, Marine Carpuat
出版商	Association for Computational Linguistics (ACL)
页	999-1002
页数	4
ISBN（电子版）	9781948087209
DOI	https://doi.org/10.18653/v1/s18-1165
出版状态	已出版 - 2018
活动	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, 美国期限: 5 6月 2018 → 6 6月 2018

出版系列

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

会议

会议	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
国家/地区	美国
市	New Orleans
时期	5/06/18 → 6/06/18

访问文件

10.18653/v1/s18-1165

其它文件与链接

链接到 Scopus 的出版物

引用此

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. 在 M. Apidianaki, M. Apidianaki, S. M. Mohammad, J. May, E. Shutova, S. Bethard, & M. Carpuat (编辑), NAACL HLT 2018 - International Workshop on Semantic Evaluation, SemEval 2018 - Proceedings of the 12th Workshop (页码 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. 编辑 / Marianna Apidianaki ; Marianna Apidianaki ; Saif M. Mohammad ; Jonathan May ; Ekaterina Shutova ; Steven Bethard ; Marine Carpuat. Association for Computational Linguistics (ACL), 2018. 页码 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. 在 M Apidianaki, M Apidianaki, SM Mohammad, J May, E Shutova, S Bethard & M Carpuat (编辑), 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), 页码 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, 美国, 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. 编辑 / Marianna Apidianaki; Marianna Apidianaki; Saif M. Mohammad; Jonathan May; Ekaterina Shutova; Steven Bethard; Marine Carpuat. Association for Computational Linguistics (ACL), 2018. 页码 999-1002 (NAACL HLT 2018 - International Workshop on Semantic Evaluation, SemEval 2018 - Proceedings of the 12th Workshop).

科研成果: 书/报告/会议事项章节 › 会议稿件 › 同行评审

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AU - Lan, Man

AU - Wu, Yuanbin

PY - 2018

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N2 - 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.

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. 在 Apidianaki M, Apidianaki M, Mohammad SM, May J, Shutova E, Bethard S, Carpuat M, 编辑, NAACL HLT 2018 - International Workshop on Semantic Evaluation, SemEval 2018 - Proceedings of the 12th Workshop. Association for Computational Linguistics (ACL). 2018. 页码 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

摘要

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会议

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