@inproceedings{wu-etal-2018-thu-ngn-semeval-2018,
title = "{THU}{\_}{NGN} at {S}em{E}val-2018 Task 10: Capturing Discriminative Attributes with {MLP}-{CNN} model",
author = "Wu, Chuhan and
Wu, Fangzhao and
Wu, Sixing and
Yuan, Zhigang and
Huang, Yongfeng",
editor = "Apidianaki, Marianna and
Mohammad, Saif M. and
May, Jonathan and
Shutova, Ekaterina and
Bethard, Steven and
Carpuat, Marine",
booktitle = "Proceedings of the 12th International Workshop on Semantic Evaluation",
month = jun,
year = "2018",
address = "New Orleans, Louisiana",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/S18-1157",
doi = "10.18653/v1/S18-1157",
pages = "958--962",
abstract = "Existing semantic models are capable of identifying the semantic similarity of words. However, it{'}s hard for these models to discriminate between a word and another similar word. Thus, the aim of SemEval-2018 Task 10 is to predict whether a word is a discriminative attribute between two concepts. In this task, we apply a multilayer perceptron (MLP)-convolutional neural network (CNN) model to identify whether an attribute is discriminative. The CNNs are used to extract low-level features from the inputs. The MLP takes both the flatten CNN maps and inputs to predict the labels. The evaluation F-score of our system on the test set is 0.629 (ranked 15th), which indicates that our system still needs to be improved. However, the behaviours of our system in our experiments provide useful information, which can help to improve the collective understanding of this novel task.",
}
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<abstract>Existing semantic models are capable of identifying the semantic similarity of words. However, it’s hard for these models to discriminate between a word and another similar word. Thus, the aim of SemEval-2018 Task 10 is to predict whether a word is a discriminative attribute between two concepts. In this task, we apply a multilayer perceptron (MLP)-convolutional neural network (CNN) model to identify whether an attribute is discriminative. The CNNs are used to extract low-level features from the inputs. The MLP takes both the flatten CNN maps and inputs to predict the labels. The evaluation F-score of our system on the test set is 0.629 (ranked 15th), which indicates that our system still needs to be improved. However, the behaviours of our system in our experiments provide useful information, which can help to improve the collective understanding of this novel task.</abstract>
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%0 Conference Proceedings
%T THU_NGN at SemEval-2018 Task 10: Capturing Discriminative Attributes with MLP-CNN model
%A Wu, Chuhan
%A Wu, Fangzhao
%A Wu, Sixing
%A Yuan, Zhigang
%A Huang, Yongfeng
%Y Apidianaki, Marianna
%Y Mohammad, Saif M.
%Y May, Jonathan
%Y Shutova, Ekaterina
%Y Bethard, Steven
%Y Carpuat, Marine
%S Proceedings of the 12th International Workshop on Semantic Evaluation
%D 2018
%8 June
%I Association for Computational Linguistics
%C New Orleans, Louisiana
%F wu-etal-2018-thu-ngn-semeval-2018
%X Existing semantic models are capable of identifying the semantic similarity of words. However, it’s hard for these models to discriminate between a word and another similar word. Thus, the aim of SemEval-2018 Task 10 is to predict whether a word is a discriminative attribute between two concepts. In this task, we apply a multilayer perceptron (MLP)-convolutional neural network (CNN) model to identify whether an attribute is discriminative. The CNNs are used to extract low-level features from the inputs. The MLP takes both the flatten CNN maps and inputs to predict the labels. The evaluation F-score of our system on the test set is 0.629 (ranked 15th), which indicates that our system still needs to be improved. However, the behaviours of our system in our experiments provide useful information, which can help to improve the collective understanding of this novel task.
%R 10.18653/v1/S18-1157
%U https://aclanthology.org/S18-1157
%U https://doi.org/10.18653/v1/S18-1157
%P 958-962
Markdown (Informal)
[THU_NGN at SemEval-2018 Task 10: Capturing Discriminative Attributes with MLP-CNN model](https://aclanthology.org/S18-1157) (Wu et al., SemEval 2018)
ACL