Radical-Enhanced Chinese Character Embedding
This work addresses a gap in Chinese natural language processing by leveraging radicals to improve character embeddings, offering incremental improvements for tasks like similarity judgment and segmentation.
The authors tackled the problem of learning Chinese character embeddings by incorporating radical information, which is often ignored in existing methods, and demonstrated that their radical-enhanced approach outperforms existing algorithms on Chinese character similarity judgment and word segmentation tasks.
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.