Component-Enhanced Chinese Character Embeddings

Understand

Distributed word representations are very useful for capturing semantic information and have been successfully applied in a variety of NLP tasks, especially on English.

• In this work, we innovatively develop two component-enhanced Chinese character embedding models and their bigram extensions.
• Distinguished from English word embeddings, our models explore the compositions of Chinese characters, which often serve as semantic indictors inherently.
• The evaluations on both word similarity and text classification demonstrate the effectiveness of our models.