| LDR | | 00000nam u2200205 4500 |
| 001 | | 000000432257 |
| 005 | | 20200224115706 |
| 008 | | 200131s2019 ||||||||||||||||| ||eng d |
| 020 | |
▼a 9781088305737 |
| 035 | |
▼a (MiAaPQ)AAI13899048 |
| 040 | |
▼a MiAaPQ
▼c MiAaPQ
▼d 247004 |
| 082 | 0 |
▼a 020 |
| 100 | 1 |
▼a Luan, Yi. |
| 245 | 10 |
▼a Multi-Task Graph-Based Information Extraction with Global Context. |
| 260 | |
▼a [S.l.]:
▼b University of Washington.,
▼c 2019. |
| 260 | 1 |
▼a Ann Arbor:
▼b ProQuest Dissertations & Theses,
▼c 2019. |
| 300 | |
▼a 115 p. |
| 500 | |
▼a Source: Dissertations Abstracts International, Volume: 81-04, Section: A. |
| 500 | |
▼a Advisor: Ostendorf, Mari |
| 502 | 1 |
▼a Thesis (Ph.D.)--University of Washington, 2019. |
| 506 | |
▼a This item must not be sold to any third party vendors. |
| 506 | |
▼a This item must not be added to any third party search indexes. |
| 520 | |
▼a With growing numbers of written documents in the world, it is crucial to leverage automatic language processing so that people can make better use of the information. The main challenge stems from the fact that the information in written text is not as easily used as information in a structured database. Therefore it is very important to understand and automatically extract structured information from large amount of unstructured texts. To tackle this problem, Information Extraction (IE) is the widely studied task of retrieving structured information from text. In this thesis, our goal is to develop a general high performance IE system that can work across many different domains and tasks, but particularly the less well studied domain of scientific literature.Towards achieving this goal, we propose a series of general IE frameworks that addresses the task of entity recognition, relation extraction and coreference resolution. This thesis research addresses challenges common to all such IE systems: 1) how to leverage large unannotated data when annotated training data are limited |
| 590 | |
▼a School code: 0250. |
| 650 | 4 |
▼a Computer science. |
| 650 | 4 |
▼a Artificial intelligence. |
| 650 | 4 |
▼a Information science. |
| 690 | |
▼a 0984 |
| 690 | |
▼a 0800 |
| 690 | |
▼a 0723 |
| 710 | 20 |
▼a University of Washington.
▼b Electrical and Computer Engineering. |
| 773 | 0 |
▼t Dissertations Abstracts International
▼g 81-04A. |
| 773 | |
▼t Dissertation Abstract International |
| 790 | |
▼a 0250 |
| 791 | |
▼a Ph.D. |
| 792 | |
▼a 2019 |
| 793 | |
▼a English |
| 856 | 40 |
▼u http://www.riss.kr/pdu/ddodLink.do?id=T15492012
▼n KERIS
▼z 이 자료의 원문은 한국교육학술정보원에서 제공합니다. |
| 980 | |
▼a 202002
▼f 2020 |
| 990 | |
▼a ***1008102 |
| 991 | |
▼a E-BOOK |