CAJ | 학술논문

实体识别是自然语言处理领域中一个十分重要的问题，是信息提取的基础，其识别程度直接影响了后续的句法分析、篇章理解等工作的精确程度。“熵”最初是热力学的一个概念，用来表示不确定度，熵越大，不确定性越大。“最大熵”模型是一种融合多种特征于一体，并综合这些特征进行建模，在满足约束的模型中选择熵最大的模型。“最大熵”模型可以综合观察到各种相关或不相关的概率知识，对许多问题的处理都可以达到较好的效果。通过实验分析了在新闻报道领域最长地点实体的特征，并应用了最大熵模型进行了识别研究。
실체식별시자연어언처리영역중일개십분중요적문제，시신식제취적기출，기식별정도직접영향료후속적구법분석、편장리해등공작적정학정도。“적”최초시열역학적일개개념，용래표시불학정도，적월대，불학정성월대。“최대적”모형시일충융합다충특정우일체，병종합저사특정진행건모，재만족약속적모형중선택적최대적모형。“최대적”모형가이종합관찰도각충상관혹불상관적개솔지식，대허다문제적처리도가이체도교호적효과。통과실험분석료재신문보도영역최장지점실체적특정，병응용료최대적모형진행료식별연구。
Entity identification is an important field in Natural Language Processing （NLP）. It＇s foundation of intbrmation extracuon and its accuracy has a direct effect on many NLP tasks such as Syntactic analysis, reading comprehension and so on. ＂Entropy＂ was a concept of thermodynamics originally, used to represent uncertainty, and it decreases with increasing of uncertainty. A Maximum Entropy Model combines variety of features to model and fits the right model from all satisfied constraint model. It can observe a variety of related or not related to the probability, and can better problem solving. This paper statistically analyse characteristics of the LLE and identify using the maximum entropy model.