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» Domain Adaptation of Maximum Entropy Language Models
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78
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LREC
2008
131views Education» more  LREC 2008»
14 years 11 months ago
Learning Morphology with Morfette
Morfette is a modular, data-driven, probabilistic system which learns to perform joint morphological tagging and lemmatization from morphologically annotated corpora. The system i...
Grzegorz Chrupala, Georgiana Dinu, Josef van Genab...
EMNLP
2006
14 years 11 months ago
Statistical Ranking in Tactical Generation
In this paper we describe and evaluate several statistical models for the task of realization ranking, i.e. the problem of discriminating between competing surface realizations ge...
Erik Velldal, Stephan Oepen
SIGIR
2008
ACM
14 years 9 months ago
Measuring concept relatedness using language models
Over the years, the notion of concept relatedness has attracted considerable attention. A variety of approaches, based on ontology structure, information content, association, or ...
Dolf Trieschnigg, Edgar Meij, Maarten de Rijke, We...
67
Voted
ACL
2007
14 years 11 months ago
K-best Spanning Tree Parsing
This paper introduces a Maximum Entropy dependency parser based on an efficient kbest Maximum Spanning Tree (MST) algorithm. Although recent work suggests that the edge-factored ...
Keith Hall
67
Voted
ICASSP
2009
IEEE
15 years 4 months ago
Modeling of contours in wavelet domain for generalized lifting image compression
This paper introduces the design of context-based models of contours in the wavelet domain, which are used to construct generalized lifting (GL) mappings for image compression. Th...
Julio C. Rolon, Antonio Ortega, Philippe Salembier