Compositional Matrix-Space Models for Sentiment Analysis

By Ainur Yessenalina, Claire Cardie. 2011

165 citations recorded by the source corpus.

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In Natural Language Processing

Citations in the corpus

Distributed Representations of Sentences and Documents (2014, methodology): “Representing phrases is a recent trend and received much attention (Mitchell & Lapata, 2010; Zanzotto et al., 2010; Yessenalina & Cardie, 2011; Grefenstette et al., 2013; Mikolov et al., 2013c).”
Recursive Deep Models for Semantic Compositionality Over a Sentiment Treebank (2013, background): “Because they cannot capture the meaning of longer phrases properly, compositionality in semantic vector spaces has recently received a lot of attention (Mitchell and Lapata, 2010; Socher et al., 2010; Zanzotto et al., 2010; Yessenalina and Cardie, 2011; Socher et al., 2012; Grefenstette et al., 2013”
A Neural Network for Factoid Question Answering over Paragraphs (2014, background): “Many computational models of compositionality focus on learning vector spaces (Zanzotto et al., 2010; Erk, 2012; Grefenstette et al., 2013; Yessenalina and Cardie, 2011).”
Improved Semantic Representations From Tree-Structured Long Short-Term Memory Networks (2015, background): “…success, there has been substantial interest in the area of learning distributed phrase and sentence representations (Mitchell and Lapata, 2010; Yessenalina and Cardie, 2011; Grefenstette et al., 2013; Mikolov et al., 2013), as well as distributed representations of longer bodies of text such…”
Compositional Vector Space Models for Knowledge Base Completion (2015, background): “6 Related Work 2008; Baroni and Zamparelli, 2010; Yessenalina and Cardie, 2011).”
Re-embedding words (2013, background): “Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics, pages 489–493, Sofia, Bulgaria, August 4-9 2013. c©2013 Association for Computational Linguistics”
Aspect Level Sentiment Classification with Deep Memory Network (2016, methodology): “Yessenalina and Cardie (2011) use matrix multiplication as compositional function to compute vectors for longer phrases.”
Challenges in Sentiment Analysis (2017, background): “…has gained substantial attention in recent years with work from Mitchell and Loapata (2010), Baroni and Zamparelli (2010), Rudolph and Giesbrecht (2010), Yessenalina and Cardie (2011), Grefenstette, Dinu, Zhang, Sadrzadeh, and Baroni (2013), Grefenstette and Sadrzadeh (2011), and Turney (2014).”
Global Belief Recursive Neural Networks (2014, background): “There has recently been a lot of work extending single word semantic vector spaces [27, 11, 16] to compositional models of bigrams [17, 29] or phrases of arbitrary length [25, 28, 24, 10].”
Exploring Demographic Language Variations to Improve Multilingual Sentiment Analysis in Social Media (2013, background): “…between positive and negative instances, which is the approach typically reported in the literature for recognizing polarity (Velikovich et al., 2010; Yessenalina and Cardie, 2011; Taboada et al., 2011) 10A set-count feature is a count of the number of instances from a set of terms that appears in…”
Sentiment Analysis and Opinion Mining (2012, methodology): “For example, Yi et al. (2003) used a mixture language model and likelihood ratio to extract product aspects.”
Predicting Polarities of Tweets by Composing Word Embeddings with Long Short-Term Memory (2015, background): “Composing these representations to fix-length vectors that contain phrase or sentence level information also improves performance of sentiment analysis (Yessenalina and Cardie, 2011).”
Learning Tag Embeddings and Tag-specific Composition Functions in Recursive Neural Network (2015, methodology): “In addition to using vector representations, matrices can also be used to represent phrases and the composition process can be done through matrix multiplication (Rudolph and Giesbrecht, 2010; Yessenalina and Cardie, 2011).”
Encoding Syntactic Knowledge in Neural Networks for Sentiment Classification (2017, methodology): “In addition to using vector representations, matrices can also be used to represent phrases and the composition process can be done through matrix multiplication [Rudolph and Giesbrecht 2010; Yessenalina and Cardie 2011].”
Recursive deep learning for natural language processing and computer vision (2014, background): “The RNNs of previous sections tackle the problem of compositionality in vector spaces which has been investigates by several researchers (Mitchell and Lapata, 2010; Baroni and Zamparelli, 2010; Zanzotto et al., 2010; Yessenalina and Cardie, 2011).”
Semantic Compositionality through Recursive Matrix-Vector Spaces (2012, background): “Yessenalina and Cardie (2011) introduce a sentiment analysis model that describes words as matrices and composition as matrix multiplication.”