Constituent Parsing with Incremental Sigmoid Belief Networks

By Ivan Titov, James Henderson. 2007

56 citations recorded by the source corpus.

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In Natural Language Processing

Citations in the corpus

Grammar as a Foreign Language (2014, methodology): “Titov & Henderson (2007) andHenderson & Titov (2010) introduced Incremental Sigmoid Belief Networks for syntactic parsing.”
Distributed Word Representation Learning for Cross-Lingual Dependency Parsing (2014, background): “Titov and Hender-son (2010) extended the incremental sigmoid Belief networks (Titov and Henderson, 2007) to a generative latent variable model for dependency parsing.”
Learning Deep Architectures for AI (2007, methodology): “…Ranzato et al., 2007), and Sigmoidal Belief Networks using variational approximations (Dayan, Hinton, Neal, & Zemel, 1995; Hinton, Dayan, Frey, & Neal, 1995; Saul, Jaakkola, & Jordan, 1996; Titov & Henderson, 2007), and more recently Deep Belief Networks (Hinton et al., 2006; Bengio et al., 2007).”
A Latent Variable Model for Generative Dependency Parsing (2007, methodology): “We proposed two approximate models based on the variational approach in [46].”
Parsing with Compositional Vector Grammars (2013, background): “Both the original parsing system and its probabilistic interpretation (Titov and Henderson, 2007) learn features that represent the parsing history and do not provide a principled linguistic representation like our phrase representations.”
Recursive deep learning for natural language processing and computer vision (2014, background): “Both the original parsing system and its probabilistic interpretation (Titov and Henderson, 2007) learn features that represent the parsing history and do not provide a principled linguistic representation like my phrase representations.”
A Latent Variable Model of Synchronous Parsing for Syntactic and Semantic Dependencies (2008, methodology): “The synchronous derivations described above are modelled with an Incremental Sigmoid Belief Network (ISBN) (Titov and Henderson, 2007a).”
A Latent Variable Model of Synchronous Syntactic-Semantic Parsing for Multiple Languages (2009, methodology): “As in Henderson et al. (2008), the ISBN model distinguishes two types of latent states: syntactic states, when syntactic decisions are considered, and semantic states, when semantic decision are considered.”
A Deep Learning Approach to Machine Transliteration (2009, methodology): “Neural networks have been used in NLP in the past, e.g. for machine translation (Asunci´on Casta˜no et al., 1997) and constituent parsing (Titov and Henderson, 2007).”
Simple Semi-supervised Dependency Parsing (2008, background): “Previous research in this area includes several models which incorporate hidden variables (Matsuzaki et al., 2005; Koo and Collins, 2005; Petrov et al., 2006; Titov and Henderson, 2007).”