Compositional Matrix-Space Models of Language

By S. Rudolph, Eugenie Giesbrecht. 2010

63 citations recorded by the source corpus.

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In Natural Language Processing

Citations in the corpus

Recursive Deep Models for Semantic Compositionality Over a Sentiment Treebank (2013, background): “Some related models such as holographic reduced representations (Plate, 1995), quantum logic (Widdows, 2008), discrete-continuous models (Clark and Pulman, 2007) and the recent compositional matrix space model (Rudolph and Giesbrecht, 2010) have not been experimentally validated on larger corpora.”
Nouns are Vectors, Adjectives are Matrices: Representing Adjective-Noun Constructions in Semantic Space (2010, unknown): “…2007; Sahlgren et al., 2008) or sophisticated composition methods based on tensor products, quantum logic, etc., that have not yet been empirically tested on large-scale corpus-based semantic space tasks (Clark and Pulman, 2007; Rudolph and Giesbrecht, 2010; Smolensky, 1990; Widdows, 2008).”
Compositional Matrix-Space Models for Sentiment Analysis (2011, methodology): “Since matrix-space model can encode a vector-space model (Rudolph and Giesbrecht, 2010), we can initialize the matrices to exactly mimic the bag-of-words model.”
Challenges in Sentiment Analysis (2017, background): “…representations, 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…”
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): “Some related models such as holographic reduced representations (Plate, 1995), quantum logic (Widdows, 2008), discrete-continuous models (Clark and Pulman, 2007) and the recent compositional matrix space model (Rudolph and Giesbrecht, 2010) have not been experimentally validated on larger corpora.”
A Context-Theoretic Framework for Compositionality in Distributional Semantics (2011, background): “This lattice structure is implicit in most vector spaces used in computational linguistics, and an important part of our thesis is that the partial ordering can be interpreted as an entailment relation.”
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).”