{"id":{"repo_id":"vt","oai_identifier":"oai:vtechworks.lib.vt.edu:10919/112825"},"canonical_url":"https://search.dev.ndltd.org/etd/vt/oai:vtechworks.lib.vt.edu:10919/112825","repository":{"repo_id":"vt","name":"Virginia Tech","base_url":"https://vtechworks.lib.vt.edu/oai/request"},"display":{"title":"Information Extraction of Technical Details From Scholarly Articles","abstract":"Researchers 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.","abstract_html":"Researchers 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.","abstract_has_math":false,"creators":["Kaushal, Kulendra Kumar"],"institution":"Virginia Tech","degree_name":"Master of Science","degree_level":"masters","degree_discipline":"Computer Science and Applications","degree_department":"Computer Science","school":null,"contributors":[],"advisors":[],"committee_chairs":["Ramakrishnan, Narendran"],"committee_members":["Butler, Patrick Julian Carey","Lu, Chang Tien"],"year":2021,"date_issued":"2021-06-16","date_published":"2021-06-16","updated_at":"2026-07-22T22:20:27Z","subjects":["Information Extraction","Long Documents","Research Articles","Named Entity Recognition","Hardware Resources","Compute Platform","Programming Language and Libraries"],"languages":[],"rights":["In Copyright"],"rights_urls":["http://rightsstatements.org/vocab/InC/1.0/"],"identifier_entries":[{"key":"dc:identifier.other","label":"Dc Identifier Other","values":["vt_gsexam:31609"],"render_values":[{"text":"vt_gsexam:31609","href":null,"code":true}]}]},"links":{"outbound_url":"http://hdl.handle.net/10919/112825","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.committeechair","label":"Committee Chair","values":["Ramakrishnan, Narendran"]},{"key":"dc:contributor.committeemember","label":"Committee Member","values":["Butler, Patrick Julian Carey","Lu, Chang Tien"]},{"key":"dc:contributor.department","label":"Department","values":["Computer Science"]},{"key":"dc:creator","label":"Author","values":["Kaushal, Kulendra Kumar"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2022-12-09T07:00:25Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2022-12-09T07:00:25Z"]},{"key":"dc:date.issued","label":"Date","values":["2021-06-16"]},{"key":"dc:publisher","label":"Institution","values":["Virginia Tech"]},{"key":"dc:type","label":"Dc Type","values":["Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Computer Science and Applications"]},{"key":"thesis:degree_level","label":"Degree Level","values":["masters"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Master of Science"]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["Virginia Polytechnic Institute and State University"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Information Extraction","Long Documents","Research Articles","Named Entity Recognition","Hardware Resources","Compute Platform","Programming Language and Libraries"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:rights","label":"Dc Rights","values":["In Copyright"]},{"key":"dc:rights.uri","label":"Rights URI","values":["http://rightsstatements.org/vocab/InC/1.0/"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.other","label":"Dc Identifier Other","values":["vt_gsexam:31609"]},{"key":"dc:identifier.uri","label":"Identifier URI","values":["http://hdl.handle.net/10919/112825"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["Researchers 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."]},{"key":"dc:description.abstractgeneral","label":"General Abstract","values":["Information 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."]},{"key":"dc:description.degree","label":"Dc Description Degree","values":["Master of Science"]},{"key":"dc:format.medium","label":"Dc Format Medium","values":["ETD"]},{"key":"dc:title","label":"Title","values":["Information Extraction of Technical Details From Scholarly Articles"]}]}],"canonical_facts":{"dc:contributor.committeechair":["Ramakrishnan, Narendran"],"dc:contributor.committeemember":["Butler, Patrick Julian Carey","Lu, Chang Tien"],"dc:contributor.department":["Computer Science"],"dc:creator":["Kaushal, Kulendra Kumar"],"dc:date.accessioned":["2022-12-09T07:00:25Z"],"dc:date.available":["2022-12-09T07:00:25Z"],"dc:date.issued":["2021-06-16"],"dc:description.abstract":["Researchers 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."],"dc:description.abstractgeneral":["Information 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."],"dc:description.degree":["Master of Science"],"dc:format.medium":["ETD"],"dc:identifier.other":["vt_gsexam:31609"],"dc:identifier.uri":["http://hdl.handle.net/10919/112825"],"dc:publisher":["Virginia Tech"],"dc:rights":["In Copyright"],"dc:rights.uri":["http://rightsstatements.org/vocab/InC/1.0/"],"dc:subject":["Information Extraction","Long Documents","Research Articles","Named Entity Recognition","Hardware Resources","Compute Platform","Programming Language and Libraries"],"dc:title":["Information Extraction of Technical Details From Scholarly Articles"],"dc:type":["Thesis"],"thesis:degree_discipline":["Computer Science and Applications"],"thesis:degree_level":["masters"],"thesis:degree_name":["Master of Science"],"thesis:institution_name":["Virginia Polytechnic Institute and State University"]},"updated_at":"2026-07-22T22:20:27Z"}