'Translation lexicon' presentation slideshows

MT for Languages with Limited Resources

MT for Languages with Limited Resources. 11-731 Machine Translation April 20, 2011 Based on Joint Work with: Lori Levin, Jaime Carbonell, Stephan Vogel, Shuly Wintner, Danny Shacham, Katharina Probst, Erik Peterson, Christian Monson, Roberto Aranovich and Ariadna Font-Llitjos.

949 views • 82 slides

Stat-XFER: A General Framework for Search-based Syntax-driven MT

726 views • 65 slides

Stat-XFER: A General Framework for Search-based Syntax-driven MT

Stat-XFER: A General Framework for Search-based Syntax-driven MT. Alon Lavie Language Technologies Institute Carnegie Mellon University Joint work with:

668 views • 57 slides

Stat-XFER: A General Framework for Search-based Syntax-driven MT

Stat-XFER: A General Framework for Search-based Syntax-driven MT. Alon Lavie Language Technologies Institute Carnegie Mellon University Joint work with:

572 views • 57 slides

Lexical Trigger and Latent Semantic Analysis for Cross-Lingual Language Model Adaptation

Lexical Trigger and Latent Semantic Analysis for Cross-Lingual Language Model Adaptation. WOOSUNG KIM and SANJEEV KHUDANPUR 2005/01/12 ???. Outline. Introduction Cross-Lingual Story-Specific Adaptation Training and Test Corpora Experimental Results Conclusions. Introduction.

510 views • 36 slides

Chenhui Chu , Toshiaki Nakazawa , Sadao Kurohashi

Accurate Parallel Fragment Extraction from Quasi-Comparable Corpora using Alignment Model and Translation Lexicon. Chenhui Chu , Toshiaki Nakazawa , Sadao Kurohashi Graduate School of Informatics, Kyoto University. IJCNLP2013 (2013/10/17). Outline. Background Related Work

1.24k views • 25 slides

AMTEXT: Extraction-based MT for Arabic

AMTEXT: Extraction-based MT for Arabic. Faculty: Alon Lavie, Jaime Carbonell Students and Staff: Laura Kieras, Peter Jansen Informant: Loubna El Abadi. Goals and Approach. Analysts often are looking for limited concrete information within the text ? full MT may not be necessary

330 views • 21 slides

AMTEXT: Extraction-based MT for Arabic

AMTEXT: Extraction-based MT for Arabic. Faculty: Alon Lavie, Jaime Carbonell Students and Staff: Laura Kieras, Peter Jansen Informant: Loubna El Abadi. Background and Objectives. Full MT of text is problematic: Requires large amounts of resources, long development time

191 views • 19 slides

Hebrew-to-English XFER MT Project - Update

264 views • 18 slides