TakeLab: Systems for Measuring Semantic Text Similarity

By Frane Saric, Goran Glavas, Mladen Karan, Jan Snajder, B. D. Basic. 2012

241 citations recorded by the source corpus.

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Citations in the corpus

SemEval-2014 Task 10: Multilingual Semantic Textual Similarity (2014, methodology): “We also run the freely available system, TakeLab (Šarić et al., 2012), which yielded state of the”
Pairwise Word Interaction Modeling with Deep Neural Networks for Semantic Similarity Measurement (2016, background): “Systems in the competition are ranked by the weighted mean (the of-943 Model Pearson’s r Beltagy et al. (2014) 0.8300 B¨ar et al. (2012) 0.8730 ˇSari´c et al. (2012) 0.8803 He et al. (2015) 0.9090 This work 0.9112 (Kashyap et al., 2014), 3rd (Lynum et al., 2014) systems in the STS2014 competition,…”
ECNU: One Stone Two Birds: Ensemble of Heterogenous Measures for Semantic Relatedness and Textual Entailment (2014, background): “Vector space sentence similarity This feature set is taken from (Šarić et al., 2012), which is based on distributional vectors of words.”
UMBC_EBIQUITY-CORE: Semantic Textual Similarity Systems (2013, methodology): “The other two runs, ranked second and fourth, used support vector regression models to combine larger sets of features.”
Learning to Solve Geometry Problems from Natural Language Demonstrations in Textbooks (2017, methodology): “Then, we use an off-the-shelf semantic text similarity system (Šari´c et al., 2012) and check if a contiguous sequence of sentences in the demonstration is a paraphrase of any of the gold theorem mentions.”
OPI-JSA at SemEval-2017 Task 1: Application of Ensemble learning for computing semantic textual similarity (2017, methodology): “Authors of (ˇSari´c et al., 2012) use Google Books Ngrams for computing information content used in the weighted word overlap measure — we, in comparison, use the frequency list from British National Corpus (Leech, 2016).”
Short Text Similarity with Word Embeddings (2015, methodology): “…from the handcrafted rules, is in the features extracted, and in particular the number of additional resources required (WordNet, a dependency parser, NER tools, lemmatizer, POS tagger, stop word list [37], and WordNet, Wikipedia, Wiktionary, POS tagger, SMT system for three language pairs [6]).”
Modelling input texts: from Tree Kernels to Deep Learning (2015, methodology): “We adopt similarity features from two best performing systems of STS-2012, which were publicly released(1): namely, the Takelab(2) system [172] and the UKP Lab’s system(3) [14].”
Align, Disambiguate and Walk: A Unified Approach for Measuring Semantic Similarity (2013, unknown): “MSRpar (MPar) is the only dataset in which TLsim (Šarić et al., 2012) achieves a higher correlation with human judgments.”
Neobility at SemEval-2017 Task 1: An Attention-based Sentence Similarity Model (2017, methodology): “•Semantic Sentence Similarity: We also compute token-based alignment overlap and vector space sentence similarity (Šarić et al., 2012).”
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,”