摘要
针对现有答疑系统缺乏智能性和人机交互不够友好的不足,提出了一个智能答疑系统实现方案。为提高系统中问题与答案的匹配准确程度,着重对问题相关度算法进行了研究,在自动分词后用关键词集合相似度来计算问题的相关度,通过有监督的机器学习BP模型建立一个适合智能答疑系统的学习模型来优化分词权值。实验证明,这种算法可以帮助智能答疑系统提高准确性和智能性,具有一定的实用价值。
The intelligence and human-computer interaction of existing answering system are not good enough. To overcome the shortages, this paper presented a complete intelligent question answering System (IQAS) implementation. To enhance the veracity of matching between question from student and questions in database, the algorithm of question similarity was researched. By using auto segmenting algorithm, question similarity transformed the relativity of
出处
《计算机应用》
CSCD
北大核心
2005年第2期449-452,共4页
journal of Computer Applications
基金
国家自然科学基金资助项目(60203011)
上海市科委重点攻关项目(025111055)
关键词
智能答疑系统
自动分词
问题相关度:BP模型
collections. A good study model was found to optimize segmenting weights from BP model, a supervised-machine learning task. Test results show that the algorithm can help IQAS improve veracity and intelligence and has practical value.Key words: IQAS
auto segmenting
question similarity
BP model
