Towards hybrid human-machine scientific information extraction

Tchoua, Roselyne; Ajith, Aswathy; Hong, Zhi; Ward, Logan; Chard, Kyle; Audus, Debra J.; Patel, Shrayesh N.; Pablo, Juan; Foster, Ian

doi:10.1109/nysds.2018.8538953

Cited by 5 publications

(4 citation statements)

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“…For example, the Polymer Property Predictor and Database includes proprieties such as the polymer interaction parameter (v) and glass-transition temperature (T g ) extracted semiautomatically from literature. 40 In sociology, the Inter-university Consortium for Political and Social Research (ICPSR) maintains a database of sociology datasets and related publications via manual curation. 49 These databases usually store the extracted data in a structured format, but they often have an identifier, e.g., a digital object identifier (DOI), pointing to the source of the each entry, with which discovering the source text is feasible programmatically.…”

Section: Harvesting Existing Datamentioning

confidence: 99%

Challenges and Advances in Information Extraction from Scientific Literature: a Review

Hong

Ward

Chard

et al. 2021

JOM

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Scientific articles have long been the primary means of disseminating scientific discoveries. Over the centuries, valuable data and potentially groundbreaking insights have been collected and buried deep in the mountain of publications. In materials engineering, such data are spread across technical handbooks specification sheets, journal articles, and laboratory notebooks in myriad formats. Extracting information from papers on a large scale has been a tedious and time-consuming job to which few researchers have wanted to devote their limited time and effort, yet is an activity that is essential for modern data-driven design practices. However, in recent years, significant progress has been made by the computer science community on techniques for automated information extraction from free text. Yet, transformative application of these techniques to scientific literature remains elusive-due not to a lack of interest or effort but to technical and logistical challenges. Using the challenges in the materials science literature as a driving motivation, we review the gaps between state-of-the-art information extraction methods and the practical application of such methods to scientific texts, and offer a comprehensive overview of work that can be undertaken to close these gaps.

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Section: Harvesting Existing Datamentioning

confidence: 99%

Challenges and Advances in Information Extraction from Scientific Literature: a Review

Hong

Ward

Chard

et al. 2021

JOM

Self Cite

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“…The overwhelming volume of scientific papers have made extracting knowledge from them an unmanageable task [30], making automatic IE especially relevant for this domain [31]. Scientific IE has been of interest since the early 1990s [32].…”

Section: Related Workmentioning

confidence: 99%

“…The main challenge is the expertise that is needed for annotated data, making these data costly and hard to obtain, resulting in very limited data available [7]. However, there is a significant focus on this kind of research in the scientific sub-domains: medicine and biology [30].…”

Section: Related Workmentioning

confidence: 99%

“…Where at the beginning of Scientific IE, the focus mainly laid upon citations and topic analyses [13], now, the focus has become broader and has shifted toward scientific fact extraction (for example, population statistics, variants of genomics, material properties, etc.) [30]. Research on the dataset name extraction task uses a great variety of methods throughout the NER spectrum, including, but not limited to, the following: rule-based, BiLSTM-CRF and BERT [3,[6][7][8][9][10][11][12][13][14].…”

Section: Related Workmentioning

confidence: 99%

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The Automatic Detection of Dataset Names in Scientific Articles

Heddes

Meerdink

Pieters

et al. 2021

Data

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We study the task of recognizing named datasets in scientific articles as a Named Entity Recognition (NER) problem. Noticing that available annotated datasets were not adequate for our goals, we annotated 6000 sentences extracted from four major AI conferences, with roughly half of them containing one or more named datasets. A distinguishing feature of this set is the many sentences using enumerations, conjunctions and ellipses, resulting in long BI+ tag sequences. On all measures, the SciBERT NER tagger performed best and most robustly. Our baseline rule based tagger performed remarkably well and better than several state-of-the-art methods. The gold standard dataset, with links and offsets from each sentence to the (open access available) articles together with the annotation guidelines and all code used in the experiments, is available on GitHub.

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