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
Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017)
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.
A Parallel Recurrent Neural Network for Language Modeling with POS Tags
Chao Su | Heyan Huang | Shumin Shi | Yuhang Guo | Hao Wu
Proceedings of the 31st Pacific Asia Conference on Language, Information and Computation