Component-Enhanced Chinese Character Embeddings
Yanran Li, Wenjie Li, Fei Long Sun, Sujian Li · 2015
Distributed word representations are very useful for capturing semantic information and have been successfully applied in a variety of NLP tasks, especially on En-glish. In this work, we innovatively de-velop two component-enhanced Chinese character embedding models and their bi-gram extensions. Distinguished from En-glish word embeddings, our models ex-plore the compositions of Chinese char-acters, which often serve as semantic in-dictors inherently. The evaluations on both word similarity and text classification demonstrate the effectiveness of our mod-els. 1