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Information Extraction on Scientific Literature Under Limited Supervision
Information Extraction on Scientific Literature Under Limited Supervision
상세정보
- 자료유형
- 학위논문 서양
- 최종처리일시
- 20260209102904
- ISBN
- 9798265401007
- DDC
- 574
- 저자명
- Bai, Fan.
- 서명/저자
- Information Extraction on Scientific Literature Under Limited Supervision
- 발행사항
- [Sl] : Georgia Institute of Technology, 2023
- 발행사항
- Ann Arbor : ProQuest Dissertations & Theses, 2023
- 형태사항
- 147 p
- 주기사항
- Source: Dissertations Abstracts International, Volume: 87-06, Section: B.
- 주기사항
- Advisor: Ritter, Alan.
- 학위논문주기
- Thesis (Ph.D.)--Georgia Institute of Technology, 2023.
- 초록/해제
- 요약The exponential growth of scientific literature presents both challenges and opportunities for researchers across various disciplines. Effectively extracting pertinent information from this extensive corpus is crucial for advancing knowledge, enhancing collaboration, and driving innovation. However, manual extraction is a laborious and time-consuming process, underscoring the demand for automated solutions. Information extraction (IE), a subfield of natural language processing (NLP) focused on automatically extracting structured information from unstructured data sources, plays a crucial role in addressing this challenge. Despite their success, many IE methods often require substantial human-annotated data, which might not be easily accessible, particularly in specialized scientific domains. This highlights the need for adaptable and robust techniques capable of functioning with limited supervision.In this thesis, we study the task of information extraction on scientific literature, particularly addressing the challenge of limited (human) supervision. Specifically, our work has delved into four key dimensions of this problem. First, we explore the potential of harnessing easily accessible resources, like knowledge bases, to develop IE systems without direct human supervision. Second, we examine the use of pre-trained language models to create effective and efficient scientific IE systems, experimenting with various fine-tuning architectures and learning strategies. Next, we investigate the balance between the labor expenditure of human annotation and the computational cost linked with domain-specific pre-training, to achieve optimal performance under the budget constraints. Lastly, we capitalize on the emerging capabilities of large pre-trained language models by showcasing how information extraction can be achieved solely based on a human-crafted data schema. Through these explorations, this thesis aims to lay a solid foundation for the continued advancement of scientific IE under limited supervision.
- 일반주제명
- Adaptation
- 일반주제명
- Human performance
- 일반주제명
- Semantics
- 일반주제명
- Chemical synthesis
- 일반주제명
- Language
- 일반주제명
- Computer science
- 기본자료저록
- Dissertations Abstracts International. 87-06B.
- 전자적 위치 및 접속
- 로그인 후 원문을 볼 수 있습니다.
MARC
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■00520260209102904
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■007cr#unu||||||||
■020 ▼a9798265401007
■035 ▼a(MiAaPQ)AAI32315616
■035 ▼a(MiAaPQ)GeorgiaTech73149
■040 ▼aMiAaPQ▼cMiAaPQ
■0820 ▼a574
■1001 ▼aBai, Fan.
■24510▼aInformation Extraction on Scientific Literature Under Limited Supervision
■260 ▼a[Sl]▼bGeorgia Institute of Technology▼c2023
■260 1▼aAnn Arbor▼bProQuest Dissertations & Theses▼c2023
■300 ▼a147 p
■500 ▼aSource: Dissertations Abstracts International, Volume: 87-06, Section: B.
■500 ▼aAdvisor: Ritter, Alan.
■5021 ▼aThesis (Ph.D.)--Georgia Institute of Technology, 2023.
■520 ▼aThe exponential growth of scientific literature presents both challenges and opportunities for researchers across various disciplines. Effectively extracting pertinent information from this extensive corpus is crucial for advancing knowledge, enhancing collaboration, and driving innovation. However, manual extraction is a laborious and time-consuming process, underscoring the demand for automated solutions. Information extraction (IE), a subfield of natural language processing (NLP) focused on automatically extracting structured information from unstructured data sources, plays a crucial role in addressing this challenge. Despite their success, many IE methods often require substantial human-annotated data, which might not be easily accessible, particularly in specialized scientific domains. This highlights the need for adaptable and robust techniques capable of functioning with limited supervision.In this thesis, we study the task of information extraction on scientific literature, particularly addressing the challenge of limited (human) supervision. Specifically, our work has delved into four key dimensions of this problem. First, we explore the potential of harnessing easily accessible resources, like knowledge bases, to develop IE systems without direct human supervision. Second, we examine the use of pre-trained language models to create effective and efficient scientific IE systems, experimenting with various fine-tuning architectures and learning strategies. Next, we investigate the balance between the labor expenditure of human annotation and the computational cost linked with domain-specific pre-training, to achieve optimal performance under the budget constraints. Lastly, we capitalize on the emerging capabilities of large pre-trained language models by showcasing how information extraction can be achieved solely based on a human-crafted data schema. Through these explorations, this thesis aims to lay a solid foundation for the continued advancement of scientific IE under limited supervision.
■590 ▼aSchool code: 0078.
■650 4▼aAdaptation
■650 4▼aHuman performance
■650 4▼aSemantics
■650 4▼aChemical synthesis
■650 4▼aLanguage
■650 4▼aComputer science
■653 ▼aNatural language processing
■690 ▼a0984
■690 ▼a0679
■71020▼aGeorgia Institute of Technology.
■7730 ▼tDissertations Abstracts International▼g87-06B.
■790 ▼a0078
■791 ▼aPh.D.
■792 ▼a2023
■793 ▼aEnglish
■85640▼uhttp://www.riss.kr/pdu/ddodLink.do?id=T17365964▼nKERIS▼z이 자료의 원문은 한국교육학술정보원에서 제공합니다.
