| LDR | | 00000nam u2200205 4500 |
| 001 | | 000000434643 |
| 005 | | 20200226161805 |
| 008 | | 200131s2018 ||||||||||||||||| ||eng d |
| 020 | |
▼a 9781687938145 |
| 035 | |
▼a (MiAaPQ)AAI22621448 |
| 040 | |
▼a MiAaPQ
▼c MiAaPQ
▼d 247004 |
| 082 | 0 |
▼a 001 |
| 100 | 1 |
▼a Roemmele, Melissa. |
| 245 | 10 |
▼a Neural Networks for Narrative Continuation. |
| 260 | |
▼a [S.l.]:
▼b University of Southern California.,
▼c 2018. |
| 260 | 1 |
▼a Ann Arbor:
▼b ProQuest Dissertations & Theses,
▼c 2018. |
| 300 | |
▼a 171 p. |
| 500 | |
▼a Source: Dissertations Abstracts International, Volume: 81-04, Section: B. |
| 500 | |
▼a Advisor: Gordon, Andrew S. |
| 502 | 1 |
▼a Thesis (Ph.D.)--University of Southern California, 2018. |
| 506 | |
▼a This item must not be sold to any third party vendors. |
| 520 | |
▼a The field of artificial intelligence has long envisioned using computers to automatically write stories. With the advent of machine learning, in particular neural networks, researchers have found new opportunities to automatically acquire narrative knowledge directly from text corpora. There is now interest in developing systems that interface with human-authored stories in order to dynamically predict 'what happens next' in a story. In this thesis, I apply a set of neural network approaches to this narrative continuation task. I examine the task within two frameworks. In the first (closed-choice prediction), the system is presented with a story and must choose the best continuing sentence from a set of provided candidates. In the second (free-text generation), there are no candidates given for the next sentence, and the system must generate a new continuation. I demonstrate some evaluation approaches and applications associated with each framework. I discuss the observed successes and challenges of these neural network techniques in order to motivate future work in this up-and-coming research domain. |
| 590 | |
▼a School code: 0208. |
| 650 | 4 |
▼a Artificial intelligence. |
| 690 | |
▼a 0800 |
| 710 | 20 |
▼a University of Southern California.
▼b Computer Science. |
| 773 | 0 |
▼t Dissertations Abstracts International
▼g 81-04B. |
| 773 | |
▼t Dissertation Abstract International |
| 790 | |
▼a 0208 |
| 791 | |
▼a Ph.D. |
| 792 | |
▼a 2018 |
| 793 | |
▼a English |
| 856 | 40 |
▼u http://www.riss.kr/pdu/ddodLink.do?id=T15493812
▼n KERIS
▼z 이 자료의 원문은 한국교육학술정보원에서 제공합니다. |
| 980 | |
▼a 202002
▼f 2020 |
| 990 | |
▼a ***1008102 |
| 991 | |
▼a E-BOOK |