基于条件随机场的汉语词汇特征研究

In Chinese written expression, there is no word segmentation between vocabularies, so the principle of writing (or called lexical features) is what it needs to process the segmentation of Chinese content. Former researches usually mark the lexical features into training content to improve the performance, which increases the manual processing flow and the workload of the algorithm transplantation. Based on Conditional Random Fields (CRF) and the simple tags, this paper improves the recognition performance by concluding the lexical features of Chinese and transforming them to complicated functions which used by CRF. Experiments show that applying complex lexical features in Chinese word segmentation can effectively improve recognition performance and provide a new way to improve the portability of recognition algorithms in applications.