In this thesis , attention is concentrated on the english ner . ner is implemented with two methods , which are improved hmm and conditional random field , and the test result is analyzed . first , this thesis identifies the english named entity with improved hmm 分别利用了改进的隐马尔可夫模型( hiddenmarkovmodel , hmm )和条件随机域模型( conditionalrandomfield , crf )两种方法进行英文命名实体的识别,并对实验结果进行了分析。
The result shows that its performance is better than the standard hmm , but it is not good at integrating the features like context information , semantic information and so on . second , this thesis identifies the english entity names with conditional random field approach integrating many features 通过对结果的分析,发现虽然其效果要比传统的hmm模型有明显的提高,但是对文本中的上下文信息、词汇的语义信息等各种特征的结合能力还不是很理想。
Conditional random fields (CRFs) are a class of statistical modelling method often applied in pattern recognition and machine learning, where they are used for structured prediction. Whereas an ordinary classifier predicts a label for a single sample without regard to "neighboring" samples, a CRF can take context into account; e.