Reference Metadata Extraction from Korean Research Papers

Seol, Jae-Wook; Choi, Wonjun; Jeong, Haedo; Hwang, Hyekyong; Yoon, Ho‐Geun

doi:10.1007/978-3-030-05918-7_5

Cited by 1 publication

(1 citation statement)

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“…Study in Ref. [57] proposed a new method to extract bibliographic information from heterogeneous references using the CRF, such as author name, title, year of publication, volume, edition, and journal name. The model of the CRF was selected because the fields in the reference list are often sequentially listed and have patterns.…”

Section: Conditional Random Fields (Crf)mentioning

confidence: 99%

Assessment of Information Extraction Techniques, Models and Systems

Rahman¹,

Musleh²,

Nabil³

et al. 2022

MMEP

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The present article aims to review and evaluate the practiced and classical techniques, tools, models, and systems concerning automatic information extraction (IE) from published scientific documents like research articles, patents, theses, technical reports, and case studies etc. IE is performed for various reasons such as better indexing, archiving, searching, and retrieving. That is mainly used by the search engines and the indexing services as well the digital libraries and semantic web. In this regard, several studies have been conducted targeting various nature of documents. The study pays special consideration to the successful IE models, algorithms and approaches applied to structural IE from published documents. To grasp this, the paper is classified into several segments and each segment covers a significant aspect of IE. Furthermore, to validate their benefits and drawbacks, a comparative study of all the approaches have been conducted in terms of various performance factors like precision, accuracy, recall and F-score. Potential areas of improvement have been emphasized as research gap for the scholars in the closely related areas. Ultimately, a comprehensive summary of the evaluation is presented in tabular form and review is concluded. It was observed that the hybrid methods outperform the other methods due to their versatile nature to address various document formats.

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Section: Conditional Random Fields (Crf)mentioning

confidence: 99%