基于CRF的中文组块分析

提出一种基于条件随机域模型的方法用于中文文本组块分析. 该方法将中文组块分析转化为对每个词语赋予一个组块标注符号, 再根据条件随机域对标注好的训练语料建立模型, 从而预测测试语料中每个词语的组块标注符号. 使用北京大学中文树库的测试结果为F₁=85.5%, 高于隐马尔可夫模型和最大熵马尔可夫模型. 实验结果表明, 条件随机域在中文组块识别方面有效, 并避免了严格的独立性假设和数据归纳偏 置问题.

Chinese Text Chunking Based CRF

A new method to solve Chinese text chunking was introduced as conditional random fields (CRF) model, by which Chinese text chunking transformed into labeling the words with their chunk tags and establishinga model for tagged corpus according to conditional random fields so as to predict the chunk ta g of each word. An F₁ score of 85.5% is achieved by using the evaluation dataset of Chinese treebank of Beijing university, and obviously better than those of hidden Markov model and maximum entropy Markov model. Experimental results show that conditional random fields model is an effective way on Chinese text chunking and the strict Independence hypothesis and the label bias problem are avoided.