@inproceedings{meng-etal-2017-qlut-semeval,
title = "{QLUT} at {S}em{E}val-2017 Task 2: Word Similarity Based on Word Embedding and Knowledge Base",
author = "Meng, Fanqing and
Lu, Wenpeng and
Zhang, Yuteng and
Jian, Ping and
Shi, Shumin and
Huang, Heyan",
editor = "Bethard, Steven and
Carpuat, Marine and
Apidianaki, Marianna and
Mohammad, Saif M. and
Cer, Daniel and
Jurgens, David",
booktitle = "Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017)",
month = aug,
year = "2017",
address = "Vancouver, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/S17-2036",
doi = "10.18653/v1/S17-2036",
pages = "235--238",
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.",
}
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%0 Conference Proceedings
%T QLUT at SemEval-2017 Task 2: Word Similarity Based on Word Embedding and Knowledge Base
%A Meng, Fanqing
%A Lu, Wenpeng
%A Zhang, Yuteng
%A Jian, Ping
%A Shi, Shumin
%A Huang, Heyan
%Y Bethard, Steven
%Y Carpuat, Marine
%Y Apidianaki, Marianna
%Y Mohammad, Saif M.
%Y Cer, Daniel
%Y Jurgens, David
%S Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017)
%D 2017
%8 August
%I Association for Computational Linguistics
%C Vancouver, Canada
%F meng-etal-2017-qlut-semeval
%X 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.
%R 10.18653/v1/S17-2036
%U https://aclanthology.org/S17-2036
%U https://doi.org/10.18653/v1/S17-2036
%P 235-238
Markdown (Informal)
[QLUT at SemEval-2017 Task 2: Word Similarity Based on Word Embedding and Knowledge Base](https://aclanthology.org/S17-2036) (Meng et al., SemEval 2017)
ACL