BIT at SemEval-2017 Task 1: Using Semantic Information Space to Evaluate Semantic Textual Similarity
By Hao Wu, Heyan Huang, Ping Jian, Yuhang Guo, Chao Su. 2017
40 citations recorded by the source corpus.
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.
Subfields: Artificial Intelligence, Natural Language Processing
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Yinfei Yang
- Daniel Matthew Cer
- Noah Constant
- Iryna Gurevych
- Brian Strope
- R. Kurzweil
- Yun-Hsuan Sung
- Nils Reimers
In Natural Language Processing
- Yinfei Yang
- Daniel Matthew Cer
- Noah Constant
- Brian Strope
- Iryna Gurevych
- R. Kurzweil
- Yun-Hsuan Sung
- Nils Reimers
Citations in the corpus
- Learning Semantic Textual Similarity from Conversations (2018, methodology): “(2) For model comparisons, we include the state-of-the-art neural STS model CNN (HCTI) (Shao, InferSent (Conneau et al., 2017), Sent2Vec (Pagliardini et al., 2017), SIF (Arora et al., 2017), PV-DBOW (Lau and Baldwin, 2016), C-PHRASE (Kruszewski et al., 2015), ECNU (Tian et al., 2017) and BIT (Wu et ”