Reference Information Extraction and Processing Using Random Conditional Fields

Groza, Tudor; Grimnes, Gunnar Aastrand; Handschuh, Siegfried

doi:10.6017/ital.v31i2.2163

Cited by 7 publications

(3 citation statements)

References 18 publications

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“…In [43], the authors use CRF Chunker, because sometime author and organization names make ambiguities. So, they make a chunk of patterns for extraction.…”

Section: B Related Workmentioning

confidence: 99%

An Ontological Framework for Information Extraction From Diverse Scientific Sources

et al. 2021

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Automatic information extraction from online published scientific documents is useful in various applications such as tagging, web indexing and search engine optimization. As a result, automatic information extraction has become among the hottest areas of research in text mining. Although various information extraction techniques have been proposed in the literature, their efficiency demands domain specific documents with static and well-defined format. Furthermore, their accuracy is challenged with a slight modification in the format. To overcome these issues, a novel ontological framework for information extraction (OFIE) using fuzzy rule-base (FRB) and word sense disambiguation (WSD) is proposed. The proposed approach is validated with a significantly wider document domains sourced from well-known publishing services such as IEEE, ACM, Elsevier, and Springer. We have also compared the proposed information extraction approach against state-of-the-art techniques. The results of the experiment show that the proposed approach is less sensitive to changes in the document format and has a significantly better average accuracy of 89.14% and F-score as 89%.

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“…In [43], the authors use CRF Chunker, because sometime author and organization names make ambiguities. So, they make a chunk of patterns for extraction.…”

Section: B Related Workmentioning

confidence: 99%

An Ontological Framework for Information Extraction From Diverse Scientific Sources

et al. 2021

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“…There is a large body of research that addresses the extraction of bibliographic information from the reference section of research papers [2,3,4,5,6,7,8]. One group focuses on the reference string segmentation task by assuming the reference strings to be given [2,3,4] while another group also considers the reference string extraction from a given publication in the PDF or text format [5,6,7,8].…”

Section: Related Workmentioning

confidence: 99%

Evaluating Reference String Extraction Using Line-Based Conditional Random Fields: A Case Study with German Language Publications

Körner¹,

Ghavimi²,

Mayr³

et al. 2017

Communications in Computer and Information Science

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The extraction of individual reference strings from the reference section of scientific publications is an important step in the citation extraction pipeline. Current approaches divide this task into two steps by first detecting the reference section areas and then grouping the text lines in such areas into reference strings. We propose a classification model that considers every line in a publication as a potential part of a reference string. By applying line-based conditional random fields rather than constructing the graphical model based on the individual words, dependencies and patterns that are typical in reference sections provide strong features while the overall complexity of the model is reduced.

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“…Moreover, automatic reference extraction is a task with high complexity and has only recently seen some progress by the use of machine learning approaches based on Conditional Random Fields (CRF). Groza et al (2012) summarizes the research performed over the last years on automatic metadata extraction from scientific publications.…”

Section: Introductionmentioning

confidence: 99%

Extraction, analysis and publication of bibliographical references within an institutional repository

Hatop

2016

Library Hi Tech

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Purpose – The academic tradition of adding a reference section with references to cited and otherwise related academic material to an article provides a natural starting point for finding links to other publications. These links can then be published as linked data. Natural language processing technologies are available today that can perform the task of bibliographical reference extraction from text. Publishing references by the means of semantic web technologies is a prerequisite for a broader study and analysis of citations and thus can help to improve academic communication in a general sense. The paper aims to discuss these issues. Design/methodology/approach – This paper examines the overall workflow required to extract, analyze and semantically publish bibliographical references within an Institutional Repository with the help of open source software components. Findings – A publication infrastructure where references are available for software agents would enable additional benefits like citation analysis, e.g. the collection of citations of a known paper and the investigation of citation sentiment.The publication of reference information as demonstrated in this article is possible with existing semantic web technologies based on established ontologies and open source software components. Research limitations/implications – Only a limited number of metadata extraction programs have been considered for performance evaluation and reference extraction was tested for journal articles only, whereas Institutional Repositories usually do contain a large number of other material like monographs. Also, citation analysis is in an experimental state and citation sentiment is currently not published at all. For future work, the problem of distributing reference information between repositories is an important problem that needs to be tackled. Originality/value – Publishing reference information as linked data are new within the academic publishing domain.

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Reference Information Extraction and Processing Using Random Conditional Fields

Abstract: ABSTRACT

Cited by 7 publications

References 18 publications

An Ontological Framework for Information Extraction From Diverse Scientific Sources

An Ontological Framework for Information Extraction From Diverse Scientific Sources

Evaluating Reference String Extraction Using Line-Based Conditional Random Fields: A Case Study with German Language Publications

Extraction, analysis and publication of bibliographical references within an institutional repository

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