Comparing features combination with features fusion in Chinese named entity recognition
中文名实体识别中的特征组合与特征融合的比较

Maximum entropy model is usually used for named entity recognition, in which the features related to a random event are linearly combined. The problem of the weight bias in the features combination was pointed out, and a strategy of performing features fusion before linearly combining was proposed. The result of experiment on corpus containing 2000 human names shows that features fusion can improve the precision and recall of named entity recognition effectively.