学习从文本中提取关键词
Learning to Extract Keyphrases from Text
摘要：
许多学术期刊会要求作者提供一个包含5到15个关键词的列表，出现在每篇文章的首页。由于这些关键词通常是由两个或两个以上的词组成的短语，我们更喜欢称之为关键短语。正如我们在本文中讨论的那样，关键词对各种各样的任务都很有用。最近的商业软件，如微软的Word 97和Verity的Search 97，包括自动从文档中提取关键词的算法。在本文中，我们将自动提取关键词的问题作为一个监督学习任务。我们将文档视为一组短语，学习算法必须学习将其分类为关键短语的正面或负面示例。我们的第一组实验将C4.5决策树归纳算法应用于此学习任务。第二组实验将GenEx算法应用于任务。我们专门为此任务开发了GenEx算法。第三组实验研究的性能GenEx的元数据生成的任务，相对于微软的Word 97的性能。第四组也是最后一组实验研究了GenEx在突出显示任务上的性能，相对于Verity的Search 97。实验结果支持这样的说法，即专门的学习算法（GenEx）可以生成比通用学习算法（C4.5）和商业软件（Word 97和Search 97）中使用的非学习算法更好的关键词。
英文摘要：
Many academic journals ask their authors to provide a list of about five to fifteen key words, to appear on the first page of each article. Since these key words are often phrases of two or more words, we prefer to call them keyphrases. There is a surprisingly wide variety of tasks for which keyphrases are useful, as we discuss in this paper. Recent commercial software, such as Microsoft's Word 97 and Verity's Search 97, includes algorithms that automatically extract keyphrases from documents. In this paper, we approach the problem of automatically extracting keyphrases from text as a supervised learning task. We treat a document as a set of phrases, which the learning algorithm must learn to classify as positive or negative examples of keyphrases. Our first set of experiments applies the C4.5 decision tree induction algorithm to this learning task. The second set of experiments applies the GenEx algorithm to the task. We developed the GenEx algorithm specifically for this task. The third set of experiments examines the performance of GenEx on the task of metadata generation, relative to the performance of Microsoft's Word 97. The fourth and final set of experiments investigates the performance of GenEx on the task of highlighting, relative to Verity's Search 97. The experimental results support the claim that a specialized learning algorithm (GenEx) can generate better keyphrases than a general-purpose learning algorithm (C4.5) and the non-learning algorithms that are used in commercial software (Word 97 and Search 97).
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。
