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Context Enhanced Keyword Extraction for Sparse Geo-Entity Relation from Web Texts

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Web Technologies and Applications (APWeb 2016)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9865))

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Abstract

Geo-entity relation recognition from rich texts requires robust and effective solutions on keyword extraction. Compared with supervised learning methods, unsupervised learning methods attract more attention for their capability to capture the dynamic feature variation in text and to discover additional relation types. The frequency-based methods of keyword extraction have been widely studied. However, it is difficult to be applied into geo-entity keyword extraction directly because of the sparse distribution of geo-entity relations in texts. Besides, there are few studies on Chinese keyword extraction. This paper proposes a context enhanced keyword extraction method. Firstly the contexts for geo-entities are enhanced to reduce the sparseness of terms. Secondly two well-known frequency-based statistical methods (i.e., DF and Entropy) are used to build a large-scale corpus automatically from the enhanced contexts. Thirdly the lexical features and their weights are statistically determined based on the corpus to enhance the distinction of the terms. Finally, all terms in the enhanced contexts are measured with the lexical features, and the most important terms are selected as the keywords of geo-entity pairs. Experiments are conducted with mass real Chinese web texts. Compared with DF and Entropy, the presented method improves the precision by 41 % and 36 % respectively in discovering the keywords with sparse distribution and generates additional 60 % correct keywords for geo-entity relation recognition.

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Notes

  1. 1.

    http://baike.baidu.com.

  2. 2.

    http://www.datatang.com/data/42306/.

  3. 3.

    http://www.360doc.com/content/11/0110/01/694750_85358960.shtml.

  4. 4.

    https://gate.ac.uk/.

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Acknowledgments

This work was partially supported by the National High-Tech Research and Development Program of China (2013AA120305) and the National Natural Science Foundation of China (41271408).

Author information

Authors and Affiliations

  1. State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, 100101, China

    Li Yu, Feng Lu & Xiliang Liu

  2. Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing, 210023, China

    Feng Lu

  3. Key Laboratory of Virtual Geography Environment, Nanjing Normal University, Nanjing, 210046, China

    Xueying Zhang

  4. Fujian Collaborative Innovation Center for Big Data Applications in Governments, Fuzhou, 350003, China

    Xiliang Liu

Authors
  1. Li Yu
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  2. Feng Lu
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  3. Xueying Zhang
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  4. Xiliang Liu
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Corresponding author

Correspondence to Feng Lu .

Editor information

Editors and Affiliations

  1. University of Tsukuba , Tsukuba, Japan

    Atsuyuki Morishima

  2. East China Normal University , Shanghai, China

    Rong Zhang

  3. University of New South Wales , Sydney, New South Wales, Australia

    Wenjie Zhang

  4. The University of New South Wales, School of Computer Science and Engineering, Sydney, Australia

    Lijun Chang

  5. Advanced Digital Sciences Center, Illinois at Singapore Pte Ltd, Advanced Digital Sciences Center, Fusionopolies Way, Singapore

    Tom Z. J Fu

  6. Hyundai Motor Tower, Pivotal Inc 15/F, Hyundai Motor Tower, Chaoyang, China

    Kuien Liu

  7. Illinois at Singapore Pte Ltd, Advanced Digital Sciences Center , Singapore, Singapore

    Xiaoyan Yang

  8. South China Normal University, School of Computer Science, Guangzhou, China

    Jia Zhu

  9. Department of Computer Science, Hong Kong Baptist University Department of Computer Science, Kowloon Tong, Hong Kong

    Zhiwei Zhang

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Yu, L., Lu, F., Zhang, X., Liu, X. (2016). Context Enhanced Keyword Extraction for Sparse Geo-Entity Relation from Web Texts. In: Morishima, A., et al. Web Technologies and Applications. APWeb 2016. Lecture Notes in Computer Science(), vol 9865. Springer, Cham. https://doi.org/10.1007/978-3-319-45835-9_22

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  • DOI: https://doi.org/10.1007/978-3-319-45835-9_22

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-45834-2

  • Online ISBN: 978-3-319-45835-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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