图 书 馆
从噪声和多主题文档中提取关键术语Extracting Key Terms From Noisy and Multitheme Documents |
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| 课程网址: | http://videolectures.net/www09_grineva_ektnmd/ |
| 主讲教师: | Dmitry Lizorkin; Maxim Grinev; Maria Grineva |
| 开课单位: | 俄罗斯科学院系统规划研究所 |
| 开课时间: | 2009-03-20 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | We present a novel method for key term extraction from text documents. In our method, document is modeled as a graph of semantic relationships between terms of that document. We exploit the following remarkable feature of the graph: the terms related to the main topics of the document tend to bunch up into densely interconnected subgraphs or communities, while non- important terms fall into weakly intercon-nected communities, or even become isolated vertices. We apply graph community detection techniques to partition the graph into thematically cohesive groups of terms. We introduce a criterion function to select groups that contain key terms discarding groups with unimportant terms. To weight terms and determine semantic relatedness between them we exploit information extracted from Wikipedia. Using such an approach gives us the following two advantages. First, it allows effectively processing multi-theme documents. Second, it is good at filtering out noise information in the document, such as, for example, navigational bars or headers in web pages. Evaluations of the method show that it outperforms existing methods producing key terms with higher precision and recall. Additional experiments on web pages prove that our method appears to be substantially more e ective on noisy and multi- theme documents than existing methods. |
| 关 键 词: | 计算机科学; 文本挖掘; 噪声; 关键术语 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2020-06-01：heyf |
| 阅读次数: | 44 |