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» Extractive summarization using a latent variable model
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126
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EMNLP
2007
15 years 4 months ago
A Topic Model for Word Sense Disambiguation
We develop latent Dirichlet allocation with WORDNET (LDAWN), an unsupervised probabilistic topic model that includes word sense as a hidden variable. We develop a probabilistic po...
Jordan L. Boyd-Graber, David M. Blei, Xiaojin Zhu
91
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COLING
2010
14 years 9 months ago
Topic Models for Meaning Similarity in Context
Recent work on distributional methods for similarity focuses on using the context in which a target word occurs to derive context-sensitive similarity computations. In this paper ...
Georgiana Dinu, Mirella Lapata
144
Voted
ICDAR
2009
IEEE
15 years 9 days ago
Using Kernel Density Classifier with Topic Model and Cost Sensitive Learning for Automatic Text Categorization
This paper proposes a novel framework for automatic text categorization problem based on the kernel density classifier. The overall goal is to tackle two main issues in automatic ...
Dwi Sianto Mansjur, Ted S. Wada, Biing-Hwang Juang
3DIM
2003
IEEE
15 years 7 months ago
Solving architectural modelling problems using knowledge
This paper summarizes a series of recent research results made at Edinburgh University based applying domain knowledge of standard shapes and relationships to solve or improve arc...
Robert B. Fisher
DL
1999
Springer
187views Digital Library» more  DL 1999»
15 years 6 months ago
KEA: Practical Automatic Keyphrase Extraction
Keyphrases provide semantic metadata that summarize and characterize documents. This paper describes Kea, an algorithm for automatically extracting keyphrases from text. Kea ident...
Ian H. Witten, Gordon W. Paynter, Eibe Frank, Carl...