QLUT at SemEval-2017 Task 2: Word Similarity Based on Word Embedding and Knowledge Base

Fanqing Meng, Wenpeng Lu, Yuteng Zhang, Ping Jian, Shumin Shi, Heyan Huang


Abstract
This paper shows the details of our system submissions in the task 2 of SemEval 2017. We take part in the subtask 1 of this task, which is an English monolingual subtask. This task is designed to evaluate the semantic word similarity of two linguistic items. The results of runs are assessed by standard Pearson and Spearman correlation, contrast with official gold standard set. The best performance of our runs is 0.781 (Final). The techniques of our runs mainly make use of the word embeddings and the knowledge-based method. The results demonstrate that the combined method is effective for the computation of word similarity, while the word embeddings and the knowledge-based technique, respectively, needs more deeply improvement in details.
Anthology ID:
S17-2036
Volume:
Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017)
Month:
August
Year:
2017
Address:
Vancouver, Canada
Venue:
*SEMEVAL
SIGs:
SIGLEX | SIGSEM
Publisher:
Association for Computational Linguistics
Note:
Pages:
235–238
Language:
URL:
https://www.aclweb.org/anthology/S17-2036
DOI:
10.18653/v1/S17-2036
Bib Export formats:
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PDF:
http://aclanthology.lst.uni-saarland.de/S17-2036.pdf