@InProceedings{meng-EtAl:2017:SemEval2,
  author    = {Meng, Fanqing  and  Lu, Wenpeng  and  Zhang, Yuteng  and  Jian, Ping  and  Shi, Shumin  and  Huang, Heyan},
  title     = {QLUT at SemEval-2017 Task 2: Word Similarity Based on Word Embedding and Knowledge Base},
  booktitle = {Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017)},
  month     = {August},
  year      = {2017},
  address   = {Vancouver, Canada},
  publisher = {Association for Computational Linguistics},
  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.},
  url       = {http://www.aclweb.org/anthology/S17-2036}
}