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Learning Grammar Distributions With Limited Feedback- [electronic resource]
![Learning Grammar Distributions With Limited Feedback - [electronic resource]](/Users/Baul/Images/book.png)
Learning Grammar Distributions With Limited Feedback- [electronic resource]
상세정보
- 자료유형
- 학위논문파일 국외
- 최종처리일시
- 20240214101300
- ISBN
- 9798380384650
- DDC
- 401
- 서명/저자
- Learning Grammar Distributions With Limited Feedback - [electronic resource]
- 발행사항
- [S.l.]: : University of Pennsylvania., 2023
- 발행사항
- Ann Arbor : : ProQuest Dissertations & Theses,, 2023
- 형태사항
- 1 online resource(189 p.)
- 주기사항
- Source: Dissertations Abstracts International, Volume: 85-03, Section: A.
- 주기사항
- Advisor: Yang, Charles.
- 학위논문주기
- Thesis (Ph.D.)--University of Pennsylvania, 2023.
- 사용제한주기
- This item must not be sold to any third party vendors.
- 초록/해제
- 요약The past thirty years have shown a rise in models of language acquisition in which the state of the learner is characterized as a probability distribution over a set of non-stochastic grammars. In recent years, increasingly powerful models have been constructed as earlier models have failed to generalize well to increasingly complex and realistic learning domains. I particularly note that few recent models learn with limited feedback, which measures the amount of information brought to and taken from each learning instance.In this dissertation, I adopt a geometric lens for viewing this class of learning models. Viewing previous algorithms geometrically, I diagnose their flaws and motivate a novel, natural algorithm which can overcome those flaws while operating under limited feedback, which I call the barycentric learning model. Viewing representational theories geometrically, I apply the same learning algorithm successfully to learning problems across domains in parametric, ranked-constraint, and weighted-constraint theoretical frameworks. I apply novel formal tools to analyze the algorithm's behavior, which help us understand where the algorithm demonstrates convergence and non-convergence, as well as the dynamics of learning paths within individuals, and of language change paths across generations. The success of this model demonstrates that limited feedback suffices for a larger class of learning problems than previously known, while pointing a way forward for the formal and abstract understanding of language acquisition.
- 일반주제명
- Linguistics.
- 일반주제명
- Language.
- 키워드
- Parameters
- 키워드
- Grammar
- 기타저자
- University of Pennsylvania Linguistics
- 기본자료저록
- Dissertations Abstracts International. 85-03A.
- 기본자료저록
- Dissertation Abstract International
- 전자적 위치 및 접속
- 로그인 후 원문을 볼 수 있습니다.
MARC
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■00520240214101300
■006m o d
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■020 ▼a9798380384650
■035 ▼a(MiAaPQ)AAI30531004
■040 ▼aMiAaPQ▼cMiAaPQ
■0820 ▼a401
■1001 ▼aBudnick, Ryan Daniel.
■24510▼aLearning Grammar Distributions With Limited Feedback▼h[electronic resource]
■260 ▼a[S.l.]:▼bUniversity of Pennsylvania. ▼c2023
■260 1▼aAnn Arbor :▼bProQuest Dissertations & Theses, ▼c2023
■300 ▼a1 online resource(189 p.)
■500 ▼aSource: Dissertations Abstracts International, Volume: 85-03, Section: A.
■500 ▼aAdvisor: Yang, Charles.
■5021 ▼aThesis (Ph.D.)--University of Pennsylvania, 2023.
■506 ▼aThis item must not be sold to any third party vendors.
■520 ▼aThe past thirty years have shown a rise in models of language acquisition in which the state of the learner is characterized as a probability distribution over a set of non-stochastic grammars. In recent years, increasingly powerful models have been constructed as earlier models have failed to generalize well to increasingly complex and realistic learning domains. I particularly note that few recent models learn with limited feedback, which measures the amount of information brought to and taken from each learning instance.In this dissertation, I adopt a geometric lens for viewing this class of learning models. Viewing previous algorithms geometrically, I diagnose their flaws and motivate a novel, natural algorithm which can overcome those flaws while operating under limited feedback, which I call the barycentric learning model. Viewing representational theories geometrically, I apply the same learning algorithm successfully to learning problems across domains in parametric, ranked-constraint, and weighted-constraint theoretical frameworks. I apply novel formal tools to analyze the algorithm's behavior, which help us understand where the algorithm demonstrates convergence and non-convergence, as well as the dynamics of learning paths within individuals, and of language change paths across generations. The success of this model demonstrates that limited feedback suffices for a larger class of learning problems than previously known, while pointing a way forward for the formal and abstract understanding of language acquisition.
■590 ▼aSchool code: 0175.
■650 4▼aLinguistics.
■650 4▼aLanguage.
■653 ▼aLanguage acquisition
■653 ▼aBarycentric learning model
■653 ▼aParameters
■653 ▼aGrammar
■690 ▼a0290
■690 ▼a0679
■71020▼aUniversity of Pennsylvania▼bLinguistics.
■7730 ▼tDissertations Abstracts International▼g85-03A.
■773 ▼tDissertation Abstract International
■790 ▼a0175
■791 ▼aPh.D.
■792 ▼a2023
■793 ▼aEnglish
■85640▼uhttp://www.riss.kr/pdu/ddodLink.do?id=T16933542▼nKERIS▼z이 자료의 원문은 한국교육학술정보원에서 제공합니다.
■980 ▼a202402▼f2024
