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This paper proposes a word-by-word model selection approach to domain adaptation for Word Sense Disambiguation. By this approach, the model for a target word is automatically selected from a candidate model set, which is comprised of improved self-training models and a supervised model. The improved self-training uses sense priors to prevent its iteration from converging into undesirable states. Experimental...
This paper presents an overview of learning to rank. It includes three parts: related concepts including the definitions of ranking and learning to rank; a summary of pointwise models, pairwise models, and listwise models; estimation measures such as Normalized Discount Cumulative Gain and Mean Average Precision, respectively. Considering the deficiency that current learning to rank models lack of...
Machine transliteration is to translate the proper nouns in the source language according to its pronunciation into the target language. Recent orthographical based approach has improved the performance of machine translation significantly. Focusing on the transliteration unit alignment that provides a fundamental parameter for the model, this paper adopts a semi-supervised EMD approach-applying discriminative...
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