Addressing structural hurdles for metadata extraction from environmental impact statements

Egoitz Laparra, Alex Binford-Walsh, Kirk Emerson, Marc L. Miller, Laura López-Hoffman, Faiz Currim, Steven Bethard

Research output: Contribution to journalArticlepeer-review


Natural language processing techniques can be used to analyze the linguistic content of a document to extract missing pieces of metadata. However, accurate metadata extraction may not depend solely on the linguistics, but also on structural problems such as extremely large documents, unordered multi-file documents, and inconsistency in manually labeled metadata. In this work, we start from two standard machine learning solutions to extract pieces of metadata from Environmental Impact Statements, environmental policy documents that are regularly produced under the US National Environmental Policy Act of 1969. We present a series of experiments where we evaluate how these standard approaches are affected by different issues derived from real-world data. We find that metadata extraction can be strongly influenced by nonlinguistic factors such as document length and volume ordering and that the standard machine learning solutions often do not scale well to long documents. We demonstrate how such solutions can be better adapted to these scenarios, and conclude with suggestions for other NLP practitioners cataloging large document collections.

Original languageEnglish (US)
Pages (from-to)1124-1139
Number of pages16
JournalJournal of the Association for Information Science and Technology
Issue number9
StatePublished - Sep 2023

ASJC Scopus subject areas

  • Information Systems
  • Computer Networks and Communications
  • Information Systems and Management
  • Library and Information Sciences


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