Neural models with minimal feature engineering have achieved competitive performance against traditional methods for the task of Chinese word segmentation. However, both training and working procedures of the current neural models are computationally inefficient. This paper presents a greedy neural word segmenter with balanced word and character embedding inputs to alleviate the existing drawbacks. Our segmenter is truly end-toend, capable of performing segmentation much faster and even more accurate than state-of-the-art neural models on Chinese benchmark datasets.
REF presented a greedy neural word segmenter with balanced word and character embedding inputs.
Fast and Accurate Neural Word Segmentation for Chinese
ArXiv
ArXiv
Computer Science