[Objective] This paper tries to improve the accuracy of word segmentation for literature with lots of scientific terms. [Methods] First, we programed the DBLC model, which combined the methods of dictionary, statistics and deep learning. Then, we retrieved articles from the Chinese Management Case Center to build the experimental corpus. Finally, we compared the performance of this new model with the existing ones. [Results] The performance of the DBLC model was better than others. Its word segmentation accuracy was up to 96.3%. [Limitations] We did not separate the words of the original dictionary from the new words. We did not re-design the storage structure of the dictionary, which prolonged the computing time of our model. [Conclusions] The proposed DBLC model improves the accuracy of word segmentation, which is also positively co-related to the dictionary size.
冯国明, 张晓冬, 刘素辉. 基于自主学习的专业领域文本DBLC分词模型[J]. 数据分析与知识发现, 2018, 2(5): 40-47.
Feng Guoming,Zhang Xiaodong,Liu Suhui. DBLC Model for Word Segmentation Based on Autonomous Learning. Data Analysis and Knowledge Discovery, 2018, 2(5): 40-47.
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