【摘要】 针对传统的信息检索模型只能进行精确匹配的问题,提出一种基于混沌神经网络模型的查询扩展方法,利用混沌神经网络较强的记忆性、学习性和联想性,对用户查询行为进行学习,从而对用户的初始查询进行扩展和重构,以得到符合不同用户的检索结果。与传统的神经网络信息检索模型的对比实验表明,新模型具有更高的查全率和查准率。更多还原

【Abstract】 To resolve the problem that general information retrieval models need exact match,a new query expansion was provided.With strong memory,learning and association ability,the chaotic neural network was used to learn from users’ query operations.Then users’ initial query condition was expanded and reconstructed so that the different retrieval results could be acquired to satisfy the users’ query motivation.The experimental results prove that the new model has better performance than the traditional neural network information retrieval models.更多还原