Modeling Sentences in the Latent Space

By Weiwei Guo, Mona T. Diab. 2012

137 citations recorded by the source corpus.

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In Artificial Intelligence

Citations in the corpus

Multi-Relational Latent Semantic Analysis (2013, background): “, 2010) at the document level and sentential semantics (Guo and Diab, 2012; Guo and Diab, 2013) and syntactic parsing (Socher et al.”
Discriminative Improvements to Distributional Sentence Similarity (2013, methodology): “As in prior work (Guo and Diab, 2012), the threshold is tuned on held-out training data.”
Multi-Perspective Sentence Similarity Modeling with Convolutional Neural Networks (2015, methodology): “, modeling divergence of dependency syntax between the two sentences (Das and Smith, 2009); (4) corpus-based, using distributional models such as latent semantic analysis to obtain features (Hassan, 2011; Guo and Diab, 2012).”
ECNU: One Stone Two Birds: Ensemble of Heterogenous Measures for Semantic Relatedness and Textual Entailment (2014, methodology): “Specifically, we adopted the weighted textual matrix factorization (WTMF) (Guo and Diab, 2012) to model the semantics of sentences due to its reported good ability to model short texts.”
Detection and Resolution of Rumours in Social Media (2017, methodology): “In their recent paper, [Hamidian and Diab 2016] introduce the Tweet Latent Vector (TLV) approach that is obtained by applying the Semantic Textual Similarity model proposed by [Guo and Diab 2012].”
Improving Lexical Semantics for Sentential Semantics: Modeling Selectional Preference and Similar Words in a Latent Variable Model (2013, methodology): “01 with a regularization coefﬁcient set at λ = 20 , which is the best condition found in (Guo and Diab, 2012b).”
A Continuously Growing Dataset of Sentential Paraphrases (2017, background): “WMF/OrMF Weighted Matrix Factorization (WMF) (Guo and Diab, 2012) is an unsupervised latent space model.”
UMD-TTIC-UW at SemEval-2016 Task 1: Attention-Based Multi-Perspective Convolutional Neural Networks for Textual Similarity Measurement (2016, methodology): “Traditional approaches are based on hand-crafted feature engineering (Wan et al., 2006; Madnani et al., 2012; Fellbaum, 1998; Fern and Stevenson, 2008; Das and Smith, 2009; Guo and Diab, 2012; Sultan et al., 2014; Kashyap et al., 2014; Lynum et al., 2014).”
SemEval-2015 Task 1: Paraphrase and Semantic Similarity in Twitter (PIT) (2015, methodology): “, 2013) and semantic similarity techniques (Guo and Diab, 2012) specifically for tweets.”