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Annotating and Modeling Shallow Semantics Directly from Text
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| 자료유형 | E-Book |
|---|---|
| 개인저자 | He, Luheng. |
| 단체저자명 | University of Washington. Computer Science and Engineering. |
| 서명/저자사항 | Annotating and Modeling Shallow Semantics Directly from Text. |
| 발행사항 | [S.l.] : University of Washington., 2018 |
| 발행사항 | Ann Arbor : ProQuest Dissertations & Theses, 2018 |
| 형태사항 | 103 p. |
| 소장본 주기 | School code: 0250. |
| ISBN | 9780438174009 |
| 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 |
| 기본자료 저록 | Dissertation Abstracts International79-12B(E). Dissertation Abstract International |
| 대출바로가기 | http://www.riss.kr/pdu/ddodLink.do?id=T14998509 |
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| No. | 등록번호 | 청구기호 | 소장처 | 도서상태 | 반납예정일 | 예약 | 서비스 | 매체정보 |
|---|---|---|---|---|---|---|---|---|
| 1 | WE00024083 | 004 | 가야대학교// | 대출가능 |
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