基于改进PMI和最小邻接熵结合策略的未登录词识别

中文分词是中文自然语言处理的重要任务,其目前存在的一个重大瓶颈是未登录词识别问题.该文提出一种非监督的基于改进PMI和最小邻接熵结合策略的未登录词识别方法.滤除文本中无关识别的标点符号和特殊字符后,此方法先运用改进PMI算法识别出文本中凝聚程度较强的字符串,并通过停用词词表和核心词库的筛选过滤,得到候选未登录词;然后,计算候选未登录词的最小邻接熵,并依据词频-最小邻接熵判定阈值,确定出文本中的未登录词.通过理论及实验分析,此方法对不同的文本,在不需要长时间学习训练调整参数的情况下,即可生成个性化的未登录词词典,应用于中文分词系统后,其分词正确率、召回率分别达到81.49%、80.30%.

Out-of-Vocabulary Detection Based on Combination Strategy of Improved PMI and Minimum Branch Entropy

Chinese word segmentation is an important task in Chinese natural language processing. One of bottleneck problems in Chinese word segmentation is Out-Of-Vocabulary (OOV) detection. This study proposes an unsupervised OOV detection method based on improved PMI algorithm and minimum branch entropy combining strategy. Firstly, the punctuation marks and special characters which are not related in the text are removed. The improved PMI algorithm recognizes the string with strong cohesion in the text, and gets the candidate OOV through the filtering of the stop word list and the core vocabulary. Then the minimum branch entropy of candidate OOV is calculated, when the term frequency-minimum branch entropy threshold is met, the output is the OOV. Through theoretical and experimental analysis, the algorithm can generate a personalized OOV dictionary for different texts, and does not require long-term learning and training to adjust parameters, and has a certain improvement in the accuracy and recall rate of detection.