BIT at SemEval-2017 Task 1: Using Semantic Information Space to Evaluate Semantic Textual Similarity

Hao Wu, Heyan Huang, Ping Jian, Yuhang Guo, Chao Su


Abstract
This paper presents three systems for semantic textual similarity (STS) evaluation at SemEval-2017 STS task. One is an unsupervised system and the other two are supervised systems which simply employ the unsupervised one. All our systems mainly depend on the (SIS), which is constructed based on the semantic hierarchical taxonomy in WordNet, to compute non-overlapping information content (IC) of sentences. Our team ranked 2nd among 31 participating teams by the primary score of Pearson correlation coefficient (PCC) mean of 7 tracks and achieved the best performance on Track 1 (AR-AR) dataset.
Anthology ID:
S17-2007
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:
77–84
Language:
URL:
https://www.aclweb.org/anthology/S17-2007
DOI:
10.18653/v1/S17-2007
Bib Export formats:
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PDF:
http://aclanthology.lst.uni-saarland.de/S17-2007.pdf