{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/117821"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/117821","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"NumNER: Numerical named entity recognition in scientific literature","abstract":"Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2023-04-12 without embargo terms","abstract_html":"Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2023-04-12 without embargo terms","abstract_has_math":false,"creators":["Garg, Shweta"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Computer Science","degree_department":null,"school":null,"contributors":["Han, Jiawei"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2022,"date_issued":"2022-12","date_published":"2022-12","updated_at":"2026-07-22T22:24:56Z","subjects":["Named Entity Recognition","Ner","Numerical Entities","Numerical Ner","Quantities","Units Of Measurements","Scibert","Entity Disambiguation"],"languages":["en","eng"],"rights":["Copyright 2022 Shweta Garg"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/2142/117821","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Han, Jiawei"]},{"key":"dc:creator","label":"Author","values":["Garg, Shweta"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2022-12","2022-12-05"]},{"key":"dc:type","label":"Dc Type","values":["text","Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Computer Science"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["M.S."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Illinois at Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Named Entity Recognition","Ner","Numerical Entities","Numerical Ner","Quantities","Units Of Measurements","Scibert","Entity Disambiguation"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en","eng"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2022 Shweta Garg"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://hdl.handle.net/2142/117821"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2023-04-12 without embargo terms","The student, Shweta Garg, accepted the attached license on 2022-12-01 at 17:43.","The student, Shweta Garg, submitted this Thesis for approval on 2022-12-01 at 17:53.","This Thesis was approved for publication on 2022-12-05 at 16:49.","DSpace SAF Submission Ingestion Package generated from Vireo submission #18727 on 2023-04-12 at 07:36:59","In the scientific domain, scientists are dealing everyday with many numerical entities. For example, in the chemistry domain, it is very common to define an inorganic reaction with details about the weight or volume of the compounds, the temperature at which a reaction was carried out and the time it took to finish the reaction. Identifying such numerical entities is hence beneficial but we may not always have the data in a desired format free from ambiguities. For example, a numerical entity 10 m may have several meanings given the context in which it is used, such as - molarity entity defining 10 moles of a compound, length entity defining 10 meters in measurement or an abbreviation of the time entity i.e 10 minutes. Hence identifying and resolving such conflicts becomes a non-trivial task. We propose NumNER, a weakly-supervised Numerical Named Entity Recognition (NER) model that takes as input weak-supervision in the form of ontology and knowledge-base guided unit labels and unit prefixes dictionaries and performs numerical entity identification and classification on scientific corpus using a combination of rule-based and context-based methods. The model also performs entity disambiguation by leveraging the knowledge of SciBERT, a pre-trained language model that has been trained on a massive scientific corpus to understand the context of the surrounding words of the ambiguous numerical entity. The model is generalizable to any domain-specific units if the user can provide the labels and symbols for those units as weak supervision to the model. We compare the performance of our model with some popular baselines NER methods and analyze the results both quantitatively and qualitatively. We also show a real-world use case of the model for extracting numerical attributes for a patient in a clinical report."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["NumNER: Numerical named entity recognition in scientific literature"]}]}],"canonical_facts":{"dc:contributor":["Han, Jiawei"],"dc:creator":["Garg, Shweta"],"dc:date":["2022-12","2022-12-05"],"dc:description":["Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2023-04-12 without embargo terms","The student, Shweta Garg, accepted the attached license on 2022-12-01 at 17:43.","The student, Shweta Garg, submitted this Thesis for approval on 2022-12-01 at 17:53.","This Thesis was approved for publication on 2022-12-05 at 16:49.","DSpace SAF Submission Ingestion Package generated from Vireo submission #18727 on 2023-04-12 at 07:36:59","In the scientific domain, scientists are dealing everyday with many numerical entities. For example, in the chemistry domain, it is very common to define an inorganic reaction with details about the weight or volume of the compounds, the temperature at which a reaction was carried out and the time it took to finish the reaction. Identifying such numerical entities is hence beneficial but we may not always have the data in a desired format free from ambiguities. For example, a numerical entity 10 m may have several meanings given the context in which it is used, such as - molarity entity defining 10 moles of a compound, length entity defining 10 meters in measurement or an abbreviation of the time entity i.e 10 minutes. Hence identifying and resolving such conflicts becomes a non-trivial task. We propose NumNER, a weakly-supervised Numerical Named Entity Recognition (NER) model that takes as input weak-supervision in the form of ontology and knowledge-base guided unit labels and unit prefixes dictionaries and performs numerical entity identification and classification on scientific corpus using a combination of rule-based and context-based methods. The model also performs entity disambiguation by leveraging the knowledge of SciBERT, a pre-trained language model that has been trained on a massive scientific corpus to understand the context of the surrounding words of the ambiguous numerical entity. The model is generalizable to any domain-specific units if the user can provide the labels and symbols for those units as weak supervision to the model. We compare the performance of our model with some popular baselines NER methods and analyze the results both quantitatively and qualitatively. We also show a real-world use case of the model for extracting numerical attributes for a patient in a clinical report."],"dc:format":["application/pdf"],"dc:identifier":["https://hdl.handle.net/2142/117821"],"dc:language":["en","eng"],"dc:rights":["Copyright 2022 Shweta Garg"],"dc:subject":["Named Entity Recognition","Ner","Numerical Entities","Numerical Ner","Quantities","Units Of Measurements","Scibert","Entity Disambiguation"],"dc:title":["NumNER: Numerical named entity recognition in scientific literature"],"dc:type":["text","Thesis"],"thesis:degree_discipline":["Computer Science"],"thesis:degree_level":["Thesis"],"thesis:degree_name":["M.S."],"thesis:institution_name":["University of Illinois at Urbana-Champaign"]},"updated_at":"2026-07-22T22:24:56Z"}