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Lexical Processing in Context Interpretation
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作者 LI Xiao (Foreign Languages College,Guangxi Normal University, Guilin 541004, China) 《广西师范大学学报(哲学社会科学版)》 2002年第S2期46-49,共4页
Elaborate explanations of word definitions and word distinctions are the usual practice in reading class in China. This paper proposes a 3P method in vocabulary learning and teaching which includes vocabulary preview,... Elaborate explanations of word definitions and word distinctions are the usual practice in reading class in China. This paper proposes a 3P method in vocabulary learning and teaching which includes vocabulary preview, vocabulary processing, and vocabulary practice, aiming for a better knowledge of vocabulary and a better comprehension of the text. 展开更多
关键词 lexical processing interactive reading CONTEXT real world communication
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Graph-based Lexicalized Reordering Models for Statistical Machine Translation
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作者 SU Jinsong LIU Yang +1 位作者 LIU Qun DONG Huailin 《China Communications》 SCIE CSCD 2014年第5期71-82,共12页
Lexicalized reordering models are very important components of phrasebased translation systems.By examining the reordering relationships between adjacent phrases,conventional methods learn these models from the word a... Lexicalized reordering models are very important components of phrasebased translation systems.By examining the reordering relationships between adjacent phrases,conventional methods learn these models from the word aligned bilingual corpus,while ignoring the effect of the number of adjacent bilingual phrases.In this paper,we propose a method to take the number of adjacent phrases into account for better estimation of reordering models.Instead of just checking whether there is one phrase adjacent to a given phrase,our method firstly uses a compact structure named reordering graph to represent all phrase segmentations of a parallel sentence,then the effect of the adjacent phrase number can be quantified in a forward-backward fashion,and finally incorporated into the estimation of reordering models.Experimental results on the NIST Chinese-English and WMT French-Spanish data sets show that our approach significantly outperforms the baseline method. 展开更多
关键词 natural language processing statistical machine translation lexicalized reordering model reordering graph
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Chinese EFL Learners' Actual Word Processing and Lexical Learning in Performing a Collaborative Output Task
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作者 牛瑞英 《Chinese Journal of Applied Linguistics》 2014年第3期309-333,406,共26页
The word processing depth hypothesis implies a positive association between learners' word processing and their lexical learning. In research, learners' task-inherent involvement load (i.e., word processing) has n... The word processing depth hypothesis implies a positive association between learners' word processing and their lexical learning. In research, learners' task-inherent involvement load (i.e., word processing) has not been found to be consistently associated with their lexical learning. Meanwhile, existing studies have not obtained consensus results, either, from directly associating learners' actual word processing and their lexical learning. Against this backdrop, this paper reports a study investigating the association between Chinese EFL learners' actual word processing and their lexical learning in performing a collaborative oral output task. Interactional and statistical analyses revealed that the participants engaged in four types of word processing; their overall word processing was significantly correlated with both their productive and receptive word acquisition and retention; their different types of word processing were significantly correlated with their productive word learning, but showed variances in correlations with their receptive word learning. The findings were discussed from the perspectives of word processing in collaborative output, word processing and lexical learning, and word processing and different modes of lexical learning. 展开更多
关键词 collaborative oral output actual word processing lexical learning association between actual word processing and lexical learning
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