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Table classification using both structure and content information: A case study of financial documents | IEEE Conference Publication | IEEE Xplore

Table classification using both structure and content information: A case study of financial documents


Abstract:

Tables are significant document components. Table extraction and classification are critical for us to explore, retrieve and mine knowledge encoded in tables. This paper ...Show More

Abstract:

Tables are significant document components. Table extraction and classification are critical for us to explore, retrieve and mine knowledge encoded in tables. This paper presents a learning based approach for classifying tables based on their content and structural information, with focus on financial document tables. To the best of our knowledge, this is the first study on classifying tables in financial domain, and also the first study of table classification based on its semantics, a more fine-grained level than previous studies. The experimental results show that it can effectively classify financial tables. We also analyzed what features are important and how to generate them. The feature identification and generation approach can potentially apply to other domains.
Date of Conference: 05-08 December 2016
Date Added to IEEE Xplore: 06 February 2017
ISBN Information:
Conference Location: Washington, DC, USA

References

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