SJTU-NLP at SemEval-2018 Task 9: Neural Hypernym Discovery with Term Embeddings

Zhuosheng Zhang, Jiangtong Li, Hai Zhao, Bingjie Tang


Abstract
This paper describes a hypernym discovery system for our participation in the SemEval-2018 Task 9, which aims to discover the best (set of) candidate hypernyms for input concepts or entities, given the search space of a pre-defined vocabulary. We introduce a neural network architecture for the concerned task and empirically study various neural network models to build the representations in latent space for words and phrases. The evaluated models include convolutional neural network, long-short term memory network, gated recurrent unit and recurrent convolutional neural network. We also explore different embedding methods, including word embedding and sense embedding for better performance.
Anthology ID:
S18-1147
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:
903–908
Language:
URL:
https://www.aclweb.org/anthology/S18-1147
DOI:
10.18653/v1/S18-1147
Bib Export formats:
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PDF:
http://aclanthology.lst.uni-saarland.de/S18-1147.pdf