Identifying Repeated Sections within Documents

Authors

  • Girish K. Palshikar TCS Research & Innovation, Pune
  • Sachin Pawar TCS Research & Innovation, Pune
  • Rajiv Srivastava TCS Research & Innovation, Pune
  • Mahek Shah TCS Research & Innovation, Pune

DOI:

https://doi.org/10.13053/cys-23-3-3263

Keywords:

Section identification, fine-grained IR, resume searching

Abstract

Identifying sections containing a logically coherent text about a particular aspect is important for fine-grained IR, question-answering and information extraction. We propose a novel problem of identifying repeated sections, such as project details in resumes and different sports events in the transcript of a news broadcast. We focus on resumes and present four techniques (2 unsupervised, 2 supervised) for automatically identifying repeated project sections. The knowledge-based method is modeled after the human way closely. The other methods are based on integer linear programming and sequence labeling. The proposed techniques are general and can be used for identifying other kinds of repeated sections (and even non-repeating sections) in different types of documents. We compared the four methods on a dataset of resumes of IT professionals and also evaluated the benefits of identifying such repeated sections in practical IR tasks. To the best of our knowledge, this paper is the first to propose and solve the problem of repeated sections identification.

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Published

2019-09-25

Issue

Vol. 23 No. 3 (2019): Thematic issue: Computational Linguistics (Guest Editor A. Gelbukh)

Section

Articles of the Thematic Issue

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