Towards combining web classification and web information extraction

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Towards combining web classification and web information extraction: a case study

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International Conference on Knowledge Discovery and Data Mining archive
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining table of contents

Paris, France

SESSION: Industrial track papers table of contents

Pages 1235-1244

Year of Publication: 2009

ISBN:978-1-60558-495-9

Authors

Ping Luo	HP Labs China, Beijing, China
Fen Lin	Institute of Computing Technology, CAS, Beijing, China
Yuhong Xiong	HP Labs China, Beijing, China
Yong Zhao	HP Labs China, Beijing, China
Zhongzhi Shi	Institute of Computing Technology, CAS, Beijing, China

Sponsors

ACM: Association for Computing Machinery
SIGKDD: ACM Special Interest Group on Knowledge Discovery in Data
SIGMOD: ACM Special Interest Group on Management of Data

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ACM New York, NY, USA

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ABSTRACT

Web content analysis often has two sequential and separate steps: Web Classification to identify the target Web pages, and Web Information Extraction to extract the metadata contained in the target Web pages. This decoupled strategy is highly ineffective since the errors in Web classification will be propagated to Web information extraction and eventually accumulate to a high level. In this paper we study the mutual dependencies between these two steps and propose to combine them by using a model of Conditional Random Fields (CRFs). This model can be used to simultaneously recognize the target Web pages and extract the corresponding metadata. Systematic experiments in our project OfCourse for online course search show that this model significantly improves the F1 value for both of the two steps. We believe that our model can be easily generalized to many Web applications.

REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

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