UWB at SemEval-2016 Task 1: Semantic Textual Similarity using Lexical, Syntactic, and Semantic Information

By Tomas Brychcin, Lukás Svoboda. 2016

50 citations recorded by the source corpus.

We present our UWB system for Semantic Textual Similarity (STS) task at SemEval 2016. Given two sentences, the system estimates the degree of their semantic similarity. We use state-of-the-art algorithms for the meaning representation and combine them with the best performing approaches to STS from previous years. These methods beneﬁt from various sources of information, such as lexical, syntactic, and semantic. In the monolingual task, our system achieve mean Pearson correlation 75.7% compared with human annotators. In the cross-lingual task, our system has correlation 86.3% and is ranked ﬁrst among 26 systems.

Subfields: Artificial Intelligence, Natural Language Processing

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Citations in the corpus

SemEval-2017 Task 1: Semantic Textual Similarity Multilingual and Crosslingual Focused Evaluation (2017, methodology): “MT is used to incorporate a similarity score based on Brychcin and Svoboda (2016)’s improvements to Sultan et al. (2015)’s method.”
SemEval-2016 Task 1: Semantic Textual Similarity, Monolingual and Cross-Lingual Evaluation (2016, background): “In terms of performance on the manually culled news data set, the highest overall rank is achieved by an unsupervised system submitted by team UWB (Brychcin and Svoboda, 2016).”
BIT at SemEval-2017 Task 1: Using Semantic Information Space to Evaluate Semantic Textual Similarity (2017, methodology): “, 2014b; Wu and Huang, 2016), some of which are appeared for a long time when there wasn’t enough training data; The other kind is supervised systems (Bär et al., 2012; Šarić et al., 2012; Sultan et al., 2015; Rychalska et al., 2016; Brychcı́n and Svoboda, 2016) applying machine learning algorithms,”
STS-UHH at SemEval-2017 Task 1: Scoring Semantic Textual Similarity Using Supervised and Unsupervised Ensemble (2017, methodology): “from STS 2016 used sophisticated deep learning based models (Rychalska et al., 2016; Brychcı́n and Svoboda, 2016; Afzal et al., 2016).”
CompiLIG at SemEval-2017 Task 1: Cross-Language Plagiarism Detection Methods for Semantic Textual Similarity (2017, methodology): “Last year, among 26 submissions from 10 teams, the method that achieved the best performance (Brychcin and Svoboda, 2016) was a supervised system (SVM regression with RBF kernel) based on word alignment algorithm presented in Sultan et al. (2015).”
Neobility at SemEval-2017 Task 1: An Attention-based Sentence Similarity Model (2017, methodology): “More recent approaches generally include the word embedding-based similarity (Liebeck et al., 2016; Brychcı́n and Svoboda, 2016) as a component of the final ensemble.”
SEF@UHH at SemEval-2017 Task 1: Unsupervised Knowledge-Free Semantic Textual Similarity via Paragraph Vector (2017, background): “Brychcı́n and Svoboda (2016) follow a similar approach but apply it also to the cross-lingual task.”
L2F/INESC-ID at SemEval-2017 Tasks 1 and 2: Lexical and semantic features in word and textual similarity (2017, methodology): “Brychc´ın and Svoboda (2016) model feature dependence in Support Vector Machines by using the product between pairs of features as new features, while we rely on neural networks.”