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Information Extraction

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Text Data Mining

Abstract

Massive text data can help users obtain information and expand the boundaries of human knowledge, but most text data cannot be easily processed and understood by computers. According to statistics, more than 80% of the text information in the Internet is unstructured. These unstructured text data greatly increase the difficulty and cost to users of gaining information. Therefore, there is an urgent need for a technology that can automatically analyze unstructured text data, mine relevant and valuable knowledge from them, and present the results to users in a structured form. Thus, information extraction (IE) technology has emerged. This chapter first introduces the concepts and history of IE and then details the typical methods for IE tasks, such as entity recognition, entity disambiguation, relation extraction, and event extraction.

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Notes

  1. 1.

    https://www.ldc.upenn.edu/collaborations/past-projects/ace.

  2. 2.

    https://www.idc.upenn.edu/collaborations/past-projects/ace.

  3. 3.

    https://www.sinall.org/conll.

  4. 4.

    https://www.signll.org/conll.

  5. 5.

    https://taku910.github.io/crfpp.

  6. 6.

    Multiview refers to multiple views of data, such as speech and vision views in videos. The two views are independent of each other and can be regarded as two dimensions of the data.

  7. 7.

    This idea is very similar to the PageRank algorithm in which the importance of a webpage is determined by the pages linking to it.

  8. 8.

    The entity category can be retrieved from the knowledge base and is generally expressed by a phrase. For example, Donald Trump’s entity category is president of the United States.

  9. 9.

    The transition is unidirectional and there is no transition from the entity to the mention.

  10. 10.

    The mention is usually a person’s name that is ambiguous because it corresponds to different entities in different contexts. For example, Michael Jordan corresponds to entities in different documents. The clustering-based entity disambiguation method performs clustering on all appearances of Michael Jordan in the document set.

  11. 11.

    CFG represents context-free grammar, e.g., VP → PP VP.

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Authors and Affiliations

  1. Institute of Automation, Chinese Academy of Sciences, Beijing, Beijing, China

    Chengqing Zong & Jiajun Zhang

  2. School of Computer Science & Engineering, Nanjing University of Science and Technology, Nanjing, Jiangsu, China

    Rui Xia

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  1. Chengqing Zong
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  2. Rui Xia
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Zong, C., Xia, R., Zhang, J. (2021). Information Extraction. In: Text Data Mining. Springer, Singapore. https://doi.org/10.1007/978-981-16-0100-2_10

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  • DOI: https://doi.org/10.1007/978-981-16-0100-2_10

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