基于反馈学习自适应的中文话题追踪

在话题追踪研究领域,由于话题是动态发展的,在追踪过程中会产生话题漂移的问题。针对该问题以及现有自适应方法的不足,本文提出基于反馈学习的自适应方法。该方法采用增量学习的思想,对话题追踪任务中的自适应学习机制提出了新的算法。该算法能够解决话题漂移现象,并能够弥补现有自适应方法的不足。该算法中还考虑了话题追踪任务的时序性,将时间信息引入到了算法中。本文实验采用TDT4语料中的中文部分作为测试语料,使用TDT2004的评测方法对基于反馈学习的自适应的中文话题追踪系统进行评价,实验数据表明基于反馈学习的自适应方法能够提高话题追踪的性能。

Adaptive Chinese Topic Tracking Based on Feedback Learning

In the field of topic detection and tracking,since topics develop dynamically,topic excursion problem may appear in the tracking process.To overcome this problem and the shortcomings of current adaptive methods,we propose a new adaptive method based on feedback learning.Based on the idea of increment learning,the paper presents a new algorithm for the adaptive learning mechanism in the task of topic tracking.This algorithm can solve the problem of topic excursion,and remedy the deficiency of current adaptive methods.Time sequence of topic tracking task is also considered in the algorithm,and time information is introduced.In the experiments,we use the Chinese part in TDT4 corpus as test corpus,and use the TDT2004 evaluation metric to evaluate the adaptive Chinese topic tracking system based on feedback learning.The experimental results show that the adaptive method based on feedback learning can improve the performance of topic tracking.