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Building Blocks for Data-Driven Theories of Language Understanding- [electronic resource]
![Building Blocks for Data-Driven Theories of Language Understanding - [electronic resource]](/Users/Baul/Images/book.png)
Building Blocks for Data-Driven Theories of Language Understanding- [electronic resource]
상세정보
- 자료유형
- 학위논문파일 국외
- 최종처리일시
- 20240214101235
- ISBN
- 9798379912116
- DDC
- 401
- 저자명
- Michael, Julian.
- 서명/저자
- Building Blocks for Data-Driven Theories of Language Understanding - [electronic resource]
- 발행사항
- [S.l.]: : University of Washington., 2023
- 발행사항
- Ann Arbor : : ProQuest Dissertations & Theses,, 2023
- 형태사항
- 1 online resource(163 p.)
- 주기사항
- Source: Dissertations Abstracts International, Volume: 85-01, Section: B.
- 주기사항
- Advisor: Zettlemoyer, Luke.
- 학위논문주기
- Thesis (Ph.D.)--University of Washington, 2023.
- 사용제한주기
- This item must not be sold to any third party vendors.
- 초록/해제
- 요약I propose a paradigm for scientific progress in natural language processing, centered around the development of data-driven theories of language understanding. The central idea is to collect data in tightly scoped, carefully defined ways which allow for exhaustive annotation of a behavioral phenomenon of interest. With such data, we can use machine learning to construct explanatory theories of these phenomena which can be used as building blocks for intelligible AI systems. After laying some conceptual groundwork for the idea, I describe a series of investigations into the development of data and theory for representations of shallow semantic structure in natural language - in particular, using Question-Answer driven Semantic Role Labeling (QA-SRL), a simple schema for annotating verbal predicate-argument structure using highly constrained question-answer pairs. While this just scratches the surface of the complex language behaviors of interest in AI, I outline principles for data collection and theoretical modeling which can inform future scientific progress.
- 일반주제명
- Linguistics.
- 일반주제명
- Computer science.
- 키워드
- Crowdsourcing
- 키워드
- Pragmatism
- 키워드
- Semantic roles
- 기타저자
- University of Washington Computer Science and Engineering
- 기본자료저록
- Dissertations Abstracts International. 85-01B.
- 기본자료저록
- Dissertation Abstract International
- 전자적 위치 및 접속
- 로그인 후 원문을 볼 수 있습니다.
MARC
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■00520240214101235
■006m o d
■007cr#unu||||||||
■020 ▼a9798379912116
■035 ▼a(MiAaPQ)AAI30527900
■040 ▼aMiAaPQ▼cMiAaPQ
■0820 ▼a401
■1001 ▼aMichael, Julian.
■24510▼aBuilding Blocks for Data-Driven Theories of Language Understanding▼h[electronic resource]
■260 ▼a[S.l.]:▼bUniversity of Washington. ▼c2023
■260 1▼aAnn Arbor :▼bProQuest Dissertations & Theses, ▼c2023
■300 ▼a1 online resource(163 p.)
■500 ▼aSource: Dissertations Abstracts International, Volume: 85-01, Section: B.
■500 ▼aAdvisor: Zettlemoyer, Luke.
■5021 ▼aThesis (Ph.D.)--University of Washington, 2023.
■506 ▼aThis item must not be sold to any third party vendors.
■520 ▼aI propose a paradigm for scientific progress in natural language processing, centered around the development of data-driven theories of language understanding. The central idea is to collect data in tightly scoped, carefully defined ways which allow for exhaustive annotation of a behavioral phenomenon of interest. With such data, we can use machine learning to construct explanatory theories of these phenomena which can be used as building blocks for intelligible AI systems. After laying some conceptual groundwork for the idea, I describe a series of investigations into the development of data and theory for representations of shallow semantic structure in natural language - in particular, using Question-Answer driven Semantic Role Labeling (QA-SRL), a simple schema for annotating verbal predicate-argument structure using highly constrained question-answer pairs. While this just scratches the surface of the complex language behaviors of interest in AI, I outline principles for data collection and theoretical modeling which can inform future scientific progress.
■590 ▼aSchool code: 0250.
■650 4▼aLinguistics.
■650 4▼aComputer science.
■653 ▼aCrowdsourcing
■653 ▼aPragmatism
■653 ▼aSemantic roles
■653 ▼aNatural language processing
■653 ▼aScientific progress
■690 ▼a0800
■690 ▼a0290
■690 ▼a0984
■71020▼aUniversity of Washington▼bComputer Science and Engineering.
■7730 ▼tDissertations Abstracts International▼g85-01B.
■773 ▼tDissertation Abstract International
■790 ▼a0250
■791 ▼aPh.D.
■792 ▼a2023
■793 ▼aEnglish
■85640▼uhttp://www.riss.kr/pdu/ddodLink.do?id=T16933345▼nKERIS▼z이 자료의 원문은 한국교육학술정보원에서 제공합니다.
■980 ▼a202402▼f2024
