A Topic Model for Word Sense Disambiguation

By Jordan L. Boyd-Graber, David M. Blei, Xiaojin Zhu. 2007

273 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Jun Zhu
2. Mohit Bansal
3. Yoshua Bengio
4. Yinpeng Dong
5. Percy Liang
6. Jianfeng Gao
7. Yoav Goldberg
8. Zhe Gan

Citations in the corpus

• Improving Interpretability of Deep Neural Networks with Semantic Information (2017, methodology): “Here, we adopt the most popular topic model, i.e ., Latent Dirichlet Allocation (LDA) [4], which has been applied to image/video/text analysis tasks [13, 7, 6, 40].”
• Multi-Relational Latent Semantic Analysis (2013, background): “Such representations also play an important role in applications for lexical semantics, such as word sense disambiguation (Boyd-Graber et al., 2007), measuring word”
• Graph-Sparse LDA: A Topic Model with Structured Sparsity (2014, methodology): “This idea was later incorporated into a topic modeling context by [30].”
• Improving Lexical Semantics for Sentential Semantics: Modeling Selectional Preference and Similar Words in a Latent Variable Model (2013, background): “There are latent variable models designed for lexical semantics, such as word senses (Boyd-Graber et al., 2007; Guo and Diab, 2011), function words (Griffiths et al.”
• WarpLDA: a Cache Efficient O(1) Algorithm for Latent Dirichlet Allocation (2015, methodology): “We then develop WarpLDA, which achieves O(1) time complexity per-token and fits the randomly accessed memory perdocument in the L3 cache.”
• Interactive Topic Modeling (2011, background): “(For a derivation, see Boyd-Graber, Blei, and Zhu (2007) for the general case or Andrzejewski, Zhu, and Craven (2009) for the specific case of constraints.)”
• WarpLDA: a Simple and Efficient O(1) Algorithm for Latent Dirichlet Allocation (2015, background): “Advantages of WarpLDA include 1) Efficiency and scalability: WarpLDA has good locality and carefully designed partition method, and can be scaled to hundreds of machines; 2) Simplicity: WarpLDA does not have any complicated modules such as alias tables, hybrid data structures, or parameter servers,…”
• Evaluating Topic Coherence Using Distributional Semantics (2013, background): “, 2003), one type of topic model, has been widely used in NLP and applied to a range of tasks including word sense disambiguation (Boyd-Graber et al., 2007), multi-document summarisation (Haghighi and Vanderwende, 2009) and generation of comparable corpora (Preiss, 2012).”
• Incorporating Lexical Priors into Topic Models (2012, background): “LDAWN (Boyd-Graber et al., 2007) models sets of words for the word sense disambiguation”
• A Context-Theoretic Framework for Compositionality in Distributional Semantics (2011, methodology): “LDA has been applied to the task of word sense disambiguation (Boyd-Graber, Blei, and Zhu 2007 ; Cai, Lee, and Teh 2007 ) with significant success.”
• Latent Variable Models of Selectional Preference (2010, methodology): “Boyd-Graber et al. (2007) integrate a model of random walks on the WordNet graph into an LDA topic model to build an unsupervised word sense disambiguation system.”