3,956 research outputs found
Radical-Enhanced Chinese Character Embedding
We present a method to leverage radical for learning Chinese character
embedding. Radical is a semantic and phonetic component of Chinese character.
It plays an important role as characters with the same radical usually have
similar semantic meaning and grammatical usage. However, existing Chinese
processing algorithms typically regard word or character as the basic unit but
ignore the crucial radical information. In this paper, we fill this gap by
leveraging radical for learning continuous representation of Chinese character.
We develop a dedicated neural architecture to effectively learn character
embedding and apply it on Chinese character similarity judgement and Chinese
word segmentation. Experiment results show that our radical-enhanced method
outperforms existing embedding learning algorithms on both tasks.Comment: 8 pages, 4 figure
Dependency relations as source context in phrase-based SMT
The Phrase-Based Statistical Machine Translation (PB-SMT) model has recently begun to include source context modeling, under the assumption that the proper lexical
choice of an ambiguous word can be determined from the context in which it appears. Various types of lexical and syntactic features such as words, parts-of-speech, and
supertags have been explored as effective source context in SMT. In this paper, we show that position-independent syntactic dependency relations of the head of a source phrase can be modeled as useful source context to improve target phrase selection and thereby improve overall performance of PB-SMT. On a Dutch—English translation task, by combining dependency relations and syntactic contextual features (part-of-speech), we achieved a 1.0 BLEU (Papineni et al., 2002) point improvement (3.1% relative) over the baseline
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