Extracting key terms from noisy and multitheme documents

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Extracting key terms from noisy and multitheme documents

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International World Wide Web Conference archive
Proceedings of the 18th international conference on World wide web table of contents

Madrid, Spain

SESSION: Semantic/data web/session: mining for semantics table of contents

Pages 661-670

Year of Publication: 2009

ISBN:978-1-60558-487-4

Authors

Maria Grineva	ISP RAS, Moscow, Russian Fed.
Maxim Grinev	ISP RAS, Moscow, Russian Fed.
Dmitry Lizorkin	ISP RAS, Moscow, Russian Fed.

Sponsor

ACM: Association for Computing Machinery

Publisher

ACM New York, NY, USA

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ABSTRACT

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 interconnected 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 effective on noisy and multi-theme documents than existing methods.

REFERENCES

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