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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. [ABSTRACT FROM AUTHOR]

This empirical research examines the effects of the COVID‐19 pandemic on information management (IM) in remote or hybrid work. We present an in‐depth statistical analysis of 716 responses to questionnaires received from employees and managers of four Israeli government ministries. The participants were asked to report characteristics such as accessibility, retrieval speed, ease of locating, and relevance of information, in order to assess the quality of organizational IM before and during COVID‐19. The findings reveal that IM quality was maintained even when organizations were forced to quickly adapt to working remotely during the pandemic. Regardless of work location, differences in perception of IM were found among organizations of different sizes: large, medium, and small. The majority of respondents who reported not using IM systems (IMS) before COVID‐19 also stated that even after the pandemic's onset, they still did not use them. A lower frequency of IMS use has been associated with a decline in IM quality. Given the far‐reaching changes in IM induced by the pandemic, many of which have the potential to be long‐lasting, these findings serve as an opening for valuable future research. [ABSTRACT FROM AUTHOR]