THU_NGN at SemEval-2018 Task 10: Capturing Discriminative Attributes with MLP-CNN model

Chuhan Wu, Fangzhao Wu, Sixing Wu, Zhigang Yuan, Yongfeng Huang


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.
Anthology ID:
S18-1157
Volume:
Proceedings of The 12th International Workshop on Semantic Evaluation
Month:
June
Year:
2018
Address:
New Orleans, Louisiana
Venue:
*SEMEVAL
SIGs:
SIGLEX | SIGSEM
Publisher:
Association for Computational Linguistics
Note:
Pages:
958–962
Language:
URL:
https://www.aclweb.org/anthology/S18-1157
DOI:
10.18653/v1/S18-1157
Bib Export formats:
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PDF:
http://aclanthology.lst.uni-saarland.de/S18-1157.pdf