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Annotating and Modeling Shallow Semantics Directly from Text
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| 자료유형 | 학위논문 |
|---|---|
| 서명/저자사항 | Annotating and Modeling Shallow Semantics Directly from Text. |
| 개인저자 | He, Luheng. |
| 단체저자명 | University of Washington. Computer Science and Engineering. |
| 발행사항 | [S.l.]: University of Washington., 2018. |
| 발행사항 | Ann Arbor: ProQuest Dissertations & Theses, 2018. |
| 형태사항 | 103 p. |
| 기본자료 저록 | Dissertation Abstracts International 79-12B(E). Dissertation Abstract International |
| ISBN | 9780438174009 |
| 학위논문주기 | Thesis (Ph.D.)--University of Washington, 2018. |
| 일반주기 |
Source: Dissertation Abstracts International, Volume: 79-12(E), Section: B.
Adviser: Luke S. Zettlemoyer. |
| 요약 | One key challenge to understanding human language is to find out the word to word semantic relations, such as "who does what to whom", "when", and "where". Semantic role labeling (SRL) is the widely studied challenge of recovering such predicate |
| 요약 | We first introduce question-answer driven semantic role labeling (QA-SRL), an annotation framework that allows us to gather SRL information from non-expert annotators. Different from the traditional SRL formalisms (e.g. PropBank), this new task |
| 요약 | We also develop two general-purpose, syntax-independent neural models that lead to significant performance gains, including an over 40% error reduction over long-standing pre-neural performance levels on PropBank. Our first model, DeepSRL, uses |
| 요약 | To address these limitations, we further introduce a span-based neural model called the Labeled Span Graph Networks (LSGNs). Inspired by a recent state-of-the-art coreference resolution model, LSGNs build contextualized representations for all s |
| 일반주제명 | Computer science. Artificial intelligence. |
| 언어 | 영어 |
| 바로가기 | 
: 이 자료의 원문은 한국교육학술정보원에서 제공합니다. |
