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Mapping Natural Language Sentences to Semantic Graphs
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| 자료유형 | 학위논문 |
|---|---|
| 서명/저자사항 | Mapping Natural Language Sentences to Semantic Graphs. |
| 개인저자 | Peng, Xiaochang. |
| 단체저자명 | University of Rochester. Engineering and Applied Sciences. |
| 발행사항 | [S.l.]: University of Rochester., 2018. |
| 발행사항 | Ann Arbor: ProQuest Dissertations & Theses, 2018. |
| 형태사항 | 136 p. |
| 기본자료 저록 | Dissertation Abstracts International 80-02B(E). Dissertation Abstract International |
| ISBN | 9780438380646 |
| 학위논문주기 | Thesis (Ph.D.)--University of Rochester, 2018. |
| 일반주기 |
Source: Dissertation Abstracts International, Volume: 80-02(E), Section: B.
Adviser: Daniel Gildea. |
| 요약 | In recent years, there has been growing interest in graph representations of semantics as a deeper understanding of natural language is increasingly important for user applications such as information extraction, question answering and dialogue |
| 요약 | More specifically, we present different modeling frameworks that take as input a sentence, and produce a semantic graph representation encoding meaning of the sentence as the output. First, we present a neural sequence-to-sequence model for sema |
| 일반주제명 | Computer science. |
| 언어 | 영어 |
| 바로가기 | 
: 이 자료의 원문은 한국교육학술정보원에서 제공합니다. |
