| LDR | | 02407nam u200433 4500 |
| 001 | | 000000417977 |
| 005 | | 20190215162448 |
| 008 | | 181129s2018 |||||||||||||||||c||eng d |
| 020 | |
▼a 9780438174009 |
| 035 | |
▼a (MiAaPQ)AAI10822766 |
| 035 | |
▼a (MiAaPQ)washington:18475 |
| 040 | |
▼a MiAaPQ
▼c MiAaPQ
▼d 247004 |
| 082 | 0 |
▼a 004 |
| 100 | 1 |
▼a He, Luheng. |
| 245 | 10 |
▼a Annotating and Modeling Shallow Semantics Directly from Text. |
| 260 | |
▼a [S.l.]:
▼b University of Washington.,
▼c 2018. |
| 260 | 1 |
▼a Ann Arbor:
▼b ProQuest Dissertations & Theses,
▼c 2018. |
| 300 | |
▼a 103 p. |
| 500 | |
▼a Source: Dissertation Abstracts International, Volume: 79-12(E), Section: B. |
| 500 | |
▼a Adviser: Luke S. Zettlemoyer. |
| 502 | 1 |
▼a Thesis (Ph.D.)--University of Washington, 2018. |
| 520 | |
▼a 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 |
| 520 | |
▼a 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 |
| 520 | |
▼a 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 |
| 520 | |
▼a 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 |
| 590 | |
▼a School code: 0250. |
| 650 | 4 |
▼a Computer science. |
| 650 | 4 |
▼a Artificial intelligence. |
| 690 | |
▼a 0984 |
| 690 | |
▼a 0800 |
| 710 | 20 |
▼a University of Washington.
▼b Computer Science and Engineering. |
| 773 | 0 |
▼t Dissertation Abstracts International
▼g 79-12B(E). |
| 773 | |
▼t Dissertation Abstract International |
| 790 | |
▼a 0250 |
| 791 | |
▼a Ph.D. |
| 792 | |
▼a 2018 |
| 793 | |
▼a English |
| 856 | 40 |
▼u http://www.riss.kr/pdu/ddodLink.do?id=T14998509
▼n KERIS
▼z 이 자료의 원문은 한국교육학술정보원에서 제공합니다. |
| 980 | |
▼a 201812
▼f 2019 |
| 990 | |
▼a ***1012033 |