| LDR | | 00000nmm u2200205 4500 |
| 001 | | 000000329780 |
| 005 | | 20241016151409 |
| 008 | | 181129s2018 ||| | | | eng d |
| 020 | |
▼a 9780438380646 |
| 035 | |
▼a (MiAaPQ)AAI10823022 |
| 035 | |
▼a (MiAaPQ)rochester:11666 |
| 040 | |
▼a MiAaPQ
▼c MiAaPQ
▼d 248032 |
| 049 | 1 |
▼f DP |
| 082 | 0 |
▼a 004 |
| 100 | 1 |
▼a Peng, Xiaochang. |
| 245 | 10 |
▼a Mapping Natural Language Sentences to Semantic Graphs. |
| 260 | |
▼a [S.l.] :
▼b University of Rochester.,
▼c 2018 |
| 260 | 1 |
▼a Ann Arbor :
▼b ProQuest Dissertations & Theses,
▼c 2018 |
| 300 | |
▼a 136 p. |
| 500 | |
▼a Source: Dissertation Abstracts International, Volume: 80-02(E), Section: B. |
| 500 | |
▼a Adviser: Daniel Gildea. |
| 502 | 1 |
▼a Thesis (Ph.D.)--University of Rochester, 2018. |
| 520 | |
▼a 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 |
| 520 | |
▼a 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 |
| 590 | |
▼a School code: 0188. |
| 650 | 4 |
▼a Computer science. |
| 690 | |
▼a 0984 |
| 710 | 20 |
▼a University of Rochester.
▼b Engineering and Applied Sciences. |
| 773 | 0 |
▼t Dissertation Abstracts International
▼g 80-02B(E). |
| 773 | |
▼t Dissertation Abstract International |
| 790 | |
▼a 0188 |
| 791 | |
▼a Ph.D. |
| 792 | |
▼a 2018 |
| 793 | |
▼a English |
| 856 | 40 |
▼u http://www.riss.kr/pdu/ddodLink.do?id=T14998534
▼n KERIS |
| 980 | |
▼a 201812
▼f 2019 |
| 990 | |
▼a 관리자
▼b 관리자 |