基于CRF模型和文档结构相结合的关键短语提取

Feng Yu, H. Xuan, Dequan Zheng
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引用次数: 11

摘要

关键词不仅要反映文档的主要内容,而且要反映文档的特色。关键短语提取是文本信息处理领域的一项重要技术。随着互联网时代的到来,在线文件呈现出惊人的几何增长,信息爆炸成为这个时代的主要特征。搜索和利用网络信息变得更加困难。因此,需要对关键字进行自动提取。本文采用分类的思想来完成关键词提取的任务,使用SVM建立分类模型,使用CRF提取关键短语。测试结果表明,该方法在提取精度和查全率方面均有显著提高。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Key-Phrase Extraction Based on a Combination of CRF Model with Document Structure
Key-Phrase should not only reflect the main content of a document, but also reflect the specialty of this document. Key-Phrase extraction is an important technique in the field of text information processing. With the advent of the Internet age, on-line file shows an astonishing increase in geometry and information explosion has became the main character of this age. Searching and making use of network information becomes more difficult. Therefore, automatically extraction on keyword is required. This paper uses the idea of classification to complete the task of Key-Phrase extraction, which uses SVM to build classification model and uses CRF to extract Key-Phrases. The testing result shows that, the mentioned extraction approach has improved dramatically compared with previous methods in precision and recall rate.
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