Metaextract

Yılmazel, Özgür; Finneran, Christina M.; Liddy, Elizabeth D.

doi:10.1145/996350.996405

Cited by 30 publications

(1 citation statement)

References 2 publications

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“…Natural language processing is part of current metadata extraction tools (Méndez et al, 2021) and has proven successful in a wide variety of domains such as e‐commerce (Paik et al, 2001), educational resources (Yilmazel et al, 2004), bio‐medical documents (Caufield et al, 2018; Valdez et al, 2016), legislative texts (Sleimi et al, 2018; Spinosa et al, 2009), software repositories (Tsay et al, 2020), and even archeological digital archives (Felicetti et al, 2018). These works show how to effectively integrate information extraction modules into processing pipelines to catalog large document libraries.…”

Section: Introductionmentioning

confidence: 99%

Addressing structural hurdles for metadata extraction from environmental impact statements

Laparra,

Binford‐Walsh,

Emerson

et al. 2023

Asso for Info Science & Tech

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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.

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Section: Introductionmentioning

confidence: 99%

Addressing structural hurdles for metadata extraction from environmental impact statements

Laparra,

Binford‐Walsh,

Emerson

et al. 2023

Asso for Info Science & Tech

View full text Add to dashboard Cite

show abstract

A flexible approach for extracting metadata from bibliographic citations

Cortez

Silva

Gonçalves

et al. 2009

J. Am. Soc. Inf. Sci.

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In this article we present FLUX-CiM, a novel method for extracting components (e.g., author names, article titles, venues, page numbers) from bibliographic citations. Our method does not rely on patterns encoding specific delimiters used in a particular citation style. This feature yields a high degree of automation and flexibility, and allows FLUX-CiM to extract from citations in any given format. Differently from previous methods that are based on models learned from user-driven training, our method relies on a knowledge base automatically constructed from an existing set of sample metadata records from a given field (e.g., computer science, health sciences, social sciences, etc.). These records are usually available on the Web or other public data repositories. To demonstrate the effectiveness and applicability of our proposed method, we present a series of experiments in which we apply it to extract bibliographic data from citations in articles of different fields. Results of these experiments exhibit precision and recall levels above 94% for all fields, and perfect extraction for the large majority of citations tested. In addition, in a comparison against a stateof-the-art information-extraction method, ours produced superior results without the training phase required by that method. Finally, we present a strategy for using bibliographic data resulting from the extraction process with FLUX-CiM to automatically update and expand the knowledge base of a given domain. We show that this strategy can be used to achieve good extraction results even if only a very small initial sample of bibliographic records is available for building the knowledge base.

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White

Boell

et al. 2009

J. Am. Soc. Inf. Sci.

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Metaextract

Cited by 30 publications

References 2 publications

Addressing structural hurdles for metadata extraction from environmental impact statements

Addressing structural hurdles for metadata extraction from environmental impact statements

A flexible approach for extracting metadata from bibliographic citations

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