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» Improving Statistical Word Alignment with Ensemble Methods
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ACL
2011
12 years 11 months ago
An Algorithm for Unsupervised Transliteration Mining with an Application to Word Alignment
We propose a language-independent method for the automatic extraction of transliteration pairs from parallel corpora. In contrast to previous work, our method uses no form of supe...
Hassan Sajjad, Alexander Fraser, Helmut Schmid
ECML
2004
Springer
14 years 27 days ago
Improving Random Forests
Random forests are one of the most successful ensemble methods which exhibits performance on the level of boosting and support vector machines. The method is fast, robust to noise,...
Marko Robnik-Sikonja
ACL
2009
13 years 5 months ago
Better Word Alignments with Supervised ITG Models
This work investigates supervised word alignment methods that exploit inversion transduction grammar (ITG) constraints. We consider maximum margin and conditional likelihood objec...
Aria Haghighi, John Blitzer, John DeNero, Dan Klei...
IALP
2010
13 years 2 months ago
Hierarchical Pitman-Yor Language Model for Machine Translation
The hierarchical Pitman-Yor process-based smoothing method applied to language model was proposed by Goldwater and by Teh; the performance of this smoothing method is shown compara...
Tsuyoshi Okita, Andy Way
ACL
2008
13 years 9 months ago
Word Clustering and Word Selection Based Feature Reduction for MaxEnt Based Hindi NER
Statistical machine learning methods are employed to train a Named Entity Recognizer from annotated data. Methods like Maximum Entropy and Conditional Random Fields make use of fe...
Sujan Kumar Saha, Pabitra Mitra, Sudeshna Sarkar