基于相关性反馈的自适应热点话题追踪模型

针对在热点话题追踪过程中容易发生话题漂移的问题,提出了基于相关性反馈的自适应热点话题追踪模型。为准确把握话题的动态演变过程,首先,在词频-逆向文档频率(term frequency—inverse document frequency,TF-IDF)的基础上引入了相关度因子。其次,构造了报道与话题的相关度计算公式和新特征词能否反馈加入到话题词汇库的判别函数。同时,利用了话题词汇库本身的特性,构造了自适应更新阈值和自适应相关阈值的动态计算方法。最后,根据各个特征词对该话题贡献度的大小,对更新后的词汇库中的各特征词动态赋予新权重。实验结果显示,和其它3类追踪器相比,该追踪器模型的漏报率平均降低0.018、误报率平均降低0.063,这表明,该追踪模型更适合于解决话题漂移问题。

Adaptive hot topic tracking model based on relevance feedback

To solve the topic excursion problem in hot topic tracking process, an adaptive hot topic tracking model based on relevance feedback was proposed. To obtain the topic dynamic evolution procedure accurately, firstly, a correlation factor was introduced into TF-IDF (term frequency-inverse document frequency) for extracting feature words. Secondly, a formula for computing relevance degree between story and topic was constructed, and a discriminant function for determining whether the new feature word could be added into the topic lexicon was also constructed. At the same time, the methods for dynamically computing adaptive updating threshold and adaptive correlation threshold were given. Finally, in the updated topic lexicon, our approach gave the new weight to each feature word according to its contribution to the topic. The experimental results showed that the proposed method could reduce the false alarm rate 0018 and the miss alarm rate 0.063 compared with the other 3 trackers in the hot topic tracking process, which concluded that this proposed technique was more suitable for solving the problem of topic drift.