Selecting relevant text subsets from web-data for building topic specific language models

HLT-NAACL 2006 - Human Language Technology Conference of the North American Chapter of the Association of Computational Linguistics, Short Papers

Volumn , Issue , 2006, Pages 145-148

  Sethy, Abhinav ^a   Georgiou, Panayiotis G ^a   Narayanan, Shrikanth ^a  

^a University of Southern California   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

MODELING LANGUAGES; NATURAL LANGUAGE PROCESSING SYSTEMS;

ENTROPY-BASED CRITERION; LANGUAGE MODEL; PERFORMANCE; RELATIVE ENTROPY; SELECTION SCHEME; SPECIFIC LANGUAGES; SUBSET SELECTION; WEB CORPORA; WEB DATA; WORD ERROR RATE;

COMPUTATIONAL LINGUISTICS;

EID: 77950550321     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (9)

1. Abhinav Sethy and Panayiotis Georgiou et al.. Building topic specific language models from web-data using competitive models. Proceedings of Eurospeech. 2005
2. Bing Liu and Yang Dai et al.. Building Text Classifiers Using Positive and Unlabeled Examples. Proceedings of ICDM. 2003
3. Kamal Nigam and Andrew Kachites McCallum et al.. Text Classification from Labeled and Unlabeled Documents using EM. Journal of Machine Learning. 39(2:3)103-134. 2000
4. Mirella Lapata and Frank Keller. Web-based models for natural language processing. ACM Transactions on Speech and Language Processing. 2(1),2005.
5. Philip Resnik and Noah A. Smith. The Web as a parallel corpus. Computational Linguistics. 29(3),2003.
6. P.G. Georgiou and S.Narayanan et al.. Transonics: A speech to speech system for English-Persian Interactions. Proceedings of IEEE ASRU. 2003
7. Ruhi Sarikaya and Agustin Gravano et al. Rapid Language Model Development Using External Resources For New Spoken Dialog Domains Proceedings of ICASSP. 2005
8. Tim Ng and Mari Ostendorf et al.. Web-data Augmented Language Model for Mandarin Speech Recognition. Proceedings of ICASSP. 2005
9. Xiaojin Zhu. Semi-Supervised Learning Literature Survey. Computer Science, University of Wisconsin-Madison.