| LDR | | 02666nam u200421 4500 |
| 001 | | 000000421183 |
| 005 | | 20190215165116 |
| 008 | | 181129s2018 |||||||||||||||||c||eng d |
| 020 | |
▼a 9780438145832 |
| 035 | |
▼a (MiAaPQ)AAI10792301 |
| 035 | |
▼a (MiAaPQ)umd:19016 |
| 040 | |
▼a MiAaPQ
▼c MiAaPQ
▼d 247004 |
| 082 | 0 |
▼a 001.5 |
| 100 | 1 |
▼a Vinjumur, Jyothi K. |
| 245 | 10 |
▼a Predictive Coding Techniques With Manual Review to Identify Privileged Documents in E-Discovery. |
| 260 | |
▼a [S.l.]:
▼b University of Maryland, College Park.,
▼c 2018. |
| 260 | 1 |
▼a Ann Arbor:
▼b ProQuest Dissertations & Theses,
▼c 2018. |
| 300 | |
▼a 147 p. |
| 500 | |
▼a Source: Dissertation Abstracts International, Volume: 79-12(E), Section: B. |
| 500 | |
▼a Adviser: Douglas W. Oard. |
| 502 | 1 |
▼a Thesis (Ph.D.)--University of Maryland, College Park, 2018. |
| 520 | |
▼a In twenty-first century civil litigation, discovery focuses on the retrieval of electronically stored information. Lawsuits may be won or lost because of incorrect production of electronic evidence. Organizations may generate fewer paper documen |
| 520 | |
▼a Two main questions that are central to building a privilege classifier are addressed. The first question seeks to determine which set of annotations can serve as a reliable basis for evaluation. The second question seeks to determine which of th |
| 520 | |
▼a A research prototype is built to perform a user study. Privilege judgments are gathered from multiple lawyers using two user interfaces. One of the two interfaces includes automatically generated features to aid the review process. The goal is t |
| 520 | |
▼a As cost is proportional to time during review, as the final step, this work introduces a semi-automated framework that aims to optimize the cost of the manual review process. The framework calls for litigants to make some rational choices about |
| 520 | |
▼a Although the work in this dissertation is applied to e-discovery, similar approaches could be applied to any case in which retrieval systems have to withhold a set of confidential documents despite their relevance to the request. |
| 590 | |
▼a School code: 0117. |
| 650 | 4 |
▼a Artificial intelligence. |
| 690 | |
▼a 0800 |
| 710 | 20 |
▼a University of Maryland, College Park.
▼b Library & Information Services. |
| 773 | 0 |
▼t Dissertation Abstracts International
▼g 79-12B(E). |
| 773 | |
▼t Dissertation Abstract International |
| 790 | |
▼a 0117 |
| 791 | |
▼a Ph.D. |
| 792 | |
▼a 2018 |
| 793 | |
▼a English |
| 856 | 40 |
▼u http://www.riss.kr/pdu/ddodLink.do?id=T14997694
▼n KERIS
▼z 이 자료의 원문은 한국교육학술정보원에서 제공합니다. |
| 980 | |
▼a 201812
▼f 2019 |
| 990 | |
▼a ***1012033 |