动态文本中趋势的自动检测：一种进化方法
Automatic Detection of Trends in Dynamical Text: An Evolutionary Approach
摘要：
本文提出了一种进化算法来模拟文档流的到达日期，文档流是任何带有时间戳的文档集合，例如新闻广播，电子邮件，IRC对话，科学期刊档案和weblog帖子。该算法将频率（每个时间单位的文档到达数量）分配给时间间隔，以便产生与数据的最佳拟合。优化是在准确拟合数据和避免太多频率变化之间的权衡;这样，分析就能够找到忽略噪声的拟合。经典的动态规划算法受到内存和效率要求的限制，这在处理长流时可能是一个问题。这表明，探索替代搜索方法，允许一定程度的不确定性，以实现易处理性。实验表明，所设计的进化算法能够在较短的时间内达到与经典动态规划算法相同的解质量。我们还探索了不同的概率模型来优化数据流的拟合，并应用这些算法来推断新的到达是否增加或减少文档流所涉及的主题中的{ \em兴趣}。
英文摘要：
This paper presents an evolutionary algorithm for modeling the arrival dates of document streams, which is any time-stamped collection of documents, such as newscasts, e-mails, IRC conversations, scientific journals archives and weblog postings. This algorithm assigns frequencies (number of document arrivals per time unit) to time intervals so that it produces an optimal fit to the data. The optimization is a trade off between accurately fitting the data and avoiding too many frequency changes; this way the analysis is able to find fits which ignore the noise. Classical dynamic programming algorithms are limited by memory and efficiency requirements, which can be a problem when dealing with long streams. This suggests to explore alternative search methods which allow for some degree of uncertainty to achieve tractability. Experiments have shown that the designed evolutionary algorithm is able to reach the same solution quality as those classical dynamic programming algorithms in a shorter time. We have also explored different probabilistic models to optimize the fitting of the date streams, and applied these algorithms to infer whether a new arrival increases or decreases {\em interest} in the topic the document stream is about.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
