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Distributional Graph: Connecting Language Towards a Representation of Knowledge and Meaning
Distributional Graph: Connecting Language Towards a Representation of Knowledge and Meaning
상세정보
- 자료유형
- 학위논문 서양
- 최종처리일시
- 20260202105246
- ISBN
- 9798291572696
- DDC
- 153
- 저자명
- Mao, Shufan.
- 서명/저자
- Distributional Graph: Connecting Language Towards a Representation of Knowledge and Meaning
- 발행사항
- [Sl] : University of Illinois at Urbana-Champaign, 2023
- 발행사항
- Ann Arbor : ProQuest Dissertations & Theses, 2023
- 형태사항
- 156 p
- 주기사항
- Source: Dissertations Abstracts International, Volume: 87-03, Section: B.
- 주기사항
- Advisor: Willits, Jon.
- 학위논문주기
- Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 2023.
- 초록/해제
- 요약What is the nature of the relationship between Knowledge and Language How does knowledge representation interact with language use? The critical role of world knowledge (lexical semantic) in language processing has long been noticed by psycholinguists, but relatively less attention has been paid to incorporating rich linguistic information in modeling knowledge and semantic representations. Current distributional models do form semantic representations from linguistic input. Despite their success in representing relatively simple semantic relations, these models are less effective in extracting rich linguistic structures from corpus, resulting in limited capability in representing complex lexical dependencies, achieving challenging semantic tasks, and accounting for relatively involved semantic behaviors. In this dissertation, I develop a novel type of distributional model - Distributional Graph - that transforms raw linguistic input into graphical forms and connects the graphlets to build a semantic network. The model encodes distributional patterns of linguistic units by a graphical topology, so that linguistically expressed concepts can be evaluated by network metrics. In particular, I adopt a spreading activation algorithm that gives rise to a graded measure of semantic relatedness on the network. I show, with two groups of studies, that distributional graphs equipped with spreading activation may better represent complex lexical relationships, compared to existing distributional models. In particular, I show that (i) The graphical encoding of co-occurrence leads to effective representation of indirect semantic relations which facilitates generalizing word-word lexical dependencies, and (ii) The explicit encoding of constituent structures in Distributional Graph leads to effective representation of multi-way lexical dependencies and success in compositional generalization tasks. The modeling works are conducted on artificially generated corpora with controlled distributional constraints, leading to a clear mechanistic account for the formal capabilities of the model. Meanwhile, the modeling results have profound implications on theory of language cognition and knowledge development in human. Considerable amount of behavioral studies are needed to validate Distributional Graph as a model for human semantic memory, and the experimental works require scaling up the Distributional Graph approach with naturalistic linguistic input. For the long-term vision, by integrating multi-modal inputs and finer grammatical information, the more full-fledged distributional graphs may give rise to generation of novel concepts that are both grammatical and meaningful. Importantly, as distributional graphs are based on explainable computational mechanisms, such advance may contribute to more interpretable AIs, and further the understanding of knowledge development and innovation in humanity.
- 일반주제명
- Cognitive psychology
- 일반주제명
- Computer engineering
- 일반주제명
- Experimental psychology
- 키워드
- Semantic memory
- 키워드
- Semantic network
- 기타저자
- University of Illinois at Urbana-Champaign Psychology
- 기본자료저록
- Dissertations Abstracts International. 87-03B.
- 전자적 위치 및 접속
- 로그인 후 원문을 볼 수 있습니다.
MARC
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■0820 ▼a153
■1001 ▼aMao, Shufan.
■24510▼aDistributional Graph: Connecting Language Towards a Representation of Knowledge and Meaning
■260 ▼a[Sl]▼bUniversity of Illinois at Urbana-Champaign▼c2023
■260 1▼aAnn Arbor▼bProQuest Dissertations & Theses▼c2023
■300 ▼a156 p
■500 ▼aSource: Dissertations Abstracts International, Volume: 87-03, Section: B.
■500 ▼aAdvisor: Willits, Jon.
■5021 ▼aThesis (Ph.D.)--University of Illinois at Urbana-Champaign, 2023.
■520 ▼aWhat is the nature of the relationship between Knowledge and Language How does knowledge representation interact with language use? The critical role of world knowledge (lexical semantic) in language processing has long been noticed by psycholinguists, but relatively less attention has been paid to incorporating rich linguistic information in modeling knowledge and semantic representations. Current distributional models do form semantic representations from linguistic input. Despite their success in representing relatively simple semantic relations, these models are less effective in extracting rich linguistic structures from corpus, resulting in limited capability in representing complex lexical dependencies, achieving challenging semantic tasks, and accounting for relatively involved semantic behaviors. In this dissertation, I develop a novel type of distributional model - Distributional Graph - that transforms raw linguistic input into graphical forms and connects the graphlets to build a semantic network. The model encodes distributional patterns of linguistic units by a graphical topology, so that linguistically expressed concepts can be evaluated by network metrics. In particular, I adopt a spreading activation algorithm that gives rise to a graded measure of semantic relatedness on the network. I show, with two groups of studies, that distributional graphs equipped with spreading activation may better represent complex lexical relationships, compared to existing distributional models. In particular, I show that (i) The graphical encoding of co-occurrence leads to effective representation of indirect semantic relations which facilitates generalizing word-word lexical dependencies, and (ii) The explicit encoding of constituent structures in Distributional Graph leads to effective representation of multi-way lexical dependencies and success in compositional generalization tasks. The modeling works are conducted on artificially generated corpora with controlled distributional constraints, leading to a clear mechanistic account for the formal capabilities of the model. Meanwhile, the modeling results have profound implications on theory of language cognition and knowledge development in human. Considerable amount of behavioral studies are needed to validate Distributional Graph as a model for human semantic memory, and the experimental works require scaling up the Distributional Graph approach with naturalistic linguistic input. For the long-term vision, by integrating multi-modal inputs and finer grammatical information, the more full-fledged distributional graphs may give rise to generation of novel concepts that are both grammatical and meaningful. Importantly, as distributional graphs are based on explainable computational mechanisms, such advance may contribute to more interpretable AIs, and further the understanding of knowledge development and innovation in humanity.
■590 ▼aSchool code: 0090.
■650 4▼aCognitive psychology
■650 4▼aComputer engineering
■650 4▼aExperimental psychology
■653 ▼aSemantic memory
■653 ▼aDistributional semantics
■653 ▼aKnowledge representation
■653 ▼aLanguage comprehension
■653 ▼aSemantic network
■690 ▼a0633
■690 ▼a0464
■690 ▼a0623
■71020▼aUniversity of Illinois at Urbana-Champaign▼bPsychology.
■7730 ▼tDissertations Abstracts International▼g87-03B.
■790 ▼a0090
■791 ▼aPh.D.
■792 ▼a2023
■793 ▼aEnglish
■85640▼uhttp://www.riss.kr/pdu/ddodLink.do?id=T17359988▼nKERIS▼z이 자료의 원문은 한국교육학술정보원에서 제공합니다.
