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Information Extraction of Technical Details From Scholarly Articles

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dc.contributor.authorKaushal, Kulendra Kumaren
dc.contributor.committeechairRamakrishnan, Narendranen
dc.contributor.committeememberButler, Patrick Julian Careyen
dc.contributor.committeememberLu, Chang Tienen
dc.contributor.departmentComputer Scienceen
dc.date.accessioned2022-12-09T07:00:25Zen
dc.date.available2022-12-09T07:00:25Zen
dc.date.issued2021-06-16en
dc.description.abstractResearchers have made significant progress in information extraction from short documents in the last few years, including social media interaction, news articles, and email excerpts. This research aims to extract technical entities like hardware resources, computing platforms, compute time, programming language, and libraries from scholarly research articles. Research articles are generally long documents having both salient as well as non-salient entities. Analyzing the cross-sectional relation, filtering the relevant information, measuring the saliency of mentioned entities, and extracting novel entities are some of the technical challenges involved in this research. This work presents a detailed study about the performance, effectiveness, and scalability of rule-based weakly supervised algorithms. We also develop an automated end-to-end Research Entity and Relationship Extractor (E2R Extractor). Additionally, we perform a comprehensive study about the effectiveness of existing deep learning-based information extraction tools like Dygie, Dygie++, SciREX. The research also contributes a dataset containing novel entities annotated in BILUO format and represents the baseline results using the E2R extractor on the proposed dataset. The results indicate that the E2R extractor successfully extracts salient entities from research articles.en
dc.description.abstractgeneralInformation extraction is a process of automatically extracting meaningful information from unstructured text such as articles, news feeds and presenting it in a structured format. Researchers have made significant progress in this domain over the past few years. However, their work primarily focuses on short documents such as social media interactions, news articles, email excerpts, and not on long documents such as scholarly articles and research papers. Long documents contain a lot of redundant data, so filtering and extracting meaningful information is quite challenging. This work focuses on extracting entities such as hardware resources, compute platforms, and programming languages used in scholarly articles. We present a deep learning-based model to extract such entities from research articles and research papers. We evaluate the performance of our deep learning model against simple rule-based algorithms and other state-of-the-art models for extracting the desired entities. Our work also contributes a labeled dataset containing the entities mentioned above and results obtained on this dataset using our deep learning model.en
dc.description.degreeMaster of Scienceen
dc.format.mediumETDen
dc.identifier.othervt_gsexam:31609en
dc.identifier.urihttp://hdl.handle.net/10919/112825en
dc.publisherVirginia Techen
dc.rightsIn Copyrighten
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/en
dc.subjectInformation Extractionen
dc.subjectLong Documentsen
dc.subjectResearch Articlesen
dc.subjectNamed Entity Recognitionen
dc.subjectHardware Resourcesen
dc.subjectCompute Platformen
dc.subjectProgramming Language and Librariesen
dc.titleInformation Extraction of Technical Details From Scholarly Articlesen
dc.typeThesisen
thesis.degree.disciplineComputer Science and Applicationsen
thesis.degree.grantorVirginia Polytechnic Institute and State Universityen
thesis.degree.levelmastersen
thesis.degree.nameMaster of Scienceen

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