| LDR | | 01982nam u200421 4500 |
| 001 | | 000000422855 |
| 005 | | 20190215170449 |
| 008 | | 181129s2017 |||||||||||||||||c||eng d |
| 020 | |
▼a 9780355627701 |
| 035 | |
▼a (MiAaPQ)AAI10614929 |
| 035 | |
▼a (MiAaPQ)umd:18335 |
| 040 | |
▼a MiAaPQ
▼c MiAaPQ
▼d 247004 |
| 082 | 0 |
▼a 004 |
| 100 | 1 |
▼a Guha, Anupam. |
| 245 | 10 |
▼a Data and Methods for Reference Resolution in Different Modalities. |
| 260 | |
▼a [S.l.]:
▼b University of Maryland, College Park.,
▼c 2017. |
| 260 | 1 |
▼a Ann Arbor:
▼b ProQuest Dissertations & Theses,
▼c 2017. |
| 300 | |
▼a 143 p. |
| 500 | |
▼a Source: Dissertation Abstracts International, Volume: 79-07(E), Section: B. |
| 500 | |
▼a Advisers: Yiannis Aloimonos |
| 502 | 1 |
▼a Thesis (Ph.D.)--University of Maryland, College Park, 2017. |
| 506 | |
▼a This item is not available from ProQuest Dissertations & Theses. |
| 520 | |
▼a One foundational goal of artificial intelligence is to build intelligent agents which interact with humans, and to do so, they must have the capacity to infer from human communication what concept is being referred to in a span of symbols. They |
| 520 | |
▼a A central theme throughout this thesis is the paucity of data in solving hard problems of reference, which it addresses by designing several datasets. To investigate hard text coreference this dissertation analyses a domain of coreference heavy |
| 590 | |
▼a School code: 0117. |
| 650 | 4 |
▼a Computer science. |
| 650 | 4 |
▼a Artificial intelligence. |
| 690 | |
▼a 0984 |
| 690 | |
▼a 0800 |
| 710 | 20 |
▼a University of Maryland, College Park.
▼b Computer Science. |
| 773 | 0 |
▼t Dissertation Abstracts International
▼g 79-07B(E). |
| 773 | |
▼t Dissertation Abstract International |
| 790 | |
▼a 0117 |
| 791 | |
▼a Ph.D. |
| 792 | |
▼a 2017 |
| 793 | |
▼a English |
| 856 | 40 |
▼u http://www.riss.kr/pdu/ddodLink.do?id=T14996628
▼n KERIS
▼z 이 자료의 원문은 한국교육학술정보원에서 제공합니다. |
| 980 | |
▼a 201812
▼f 2019 |
| 990 | |
▼a ***1012033 |