Fast and Accurate Neural Word Segmentation for Chinese
Table 4: Comparison with previous models. Results with * are from Cai and Zhao (2016).444To distinguish the performance improvement from model optimization, we especially list the results of stand-alone neural models in Zhang et al. (2016) and Liu et al. (2016). All the running time results are from our runs of released implementations on a single CPU (Intel i7-5960X) with two threads only, except for those of Zhang et al. (2016) which are from personal communication. The results of Xu and Sun (2016) are not listed due to their use of external Chinese idiom dictionary.
['Models', 'PKU F1 +  [ITALIC] pre-train', 'PKU F1', 'PKU Training (hours)', 'PKU Test (sec.)', 'MSR F1 +  [ITALIC] pre-train', 'MSR F1', 'MSR Training (hours)', 'MSR Test (sec.)']
Pre-training character embeddings on large-scale unlabeled corpus (not limited to the training corpus) has been shown helpful for extra performance improvement. The results with or without pre-trained character embeddings are listed separately for following the strict closed test setting of SIGHAN Bakeoff in which no linguistic resource other than training corpus is allowed. We also show the state-of-the-art results in (Zhao and Kit, 2008b) of traditional methods. The comparison shows our neural word segmenter outperforms all state-of-the-art neural systems with much less computational cost.