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001 on1296624509;

003 OCoLC;

005 20230124155817.0;

006 m o d ;

007 cr unu||||||||;

008 220214s2021 ncua obm 000 0 eng d;

035 a| (Sirsi) o1296624509;

035 a| (OCoLC)1296624509;

049 a| EREE;

090 a| PN171.F56;

100 1 a| Philips, James Patrick,e| author.?| UNAUTHORIZED;

245 10 a| Bibliographic reference analysis in archival data using supervised machine learning and grammatical features /c| by James Patrick Philips.;

264 1 a| [Greenville, N.C.] :b| [East Carolina University],c| 2021.;

300 a| 1 online resource (78 pages) :b| illustrations (chiefly color);

336 a| textb| txt2| rdacontent;

337 a| computerb| c2| rdamedia;

338 a| online resourceb| cr2| rdacarrier;

347 a| text file;

347 b| PDF;

347 c| 2.796 MB;

538 a| System requirements: Adobe Reader.;

538 a| Mode of access: World Wide Web.;

502 b| M.S.c| East Carolina Universityd| 2021;

500 a| Presented to the faculty of the Department of Computer Science;

500 a| Advisor: Nasseh Tabrizi;

500 a| Title from PDF t.p. (viewed November 29, 2022).;

520 3 a| Bibliographic references are integral to scholarly discourse in humanities disciplines. While prior work has focused on reference extraction and parsing, little research has investigated the classification of footnotes containing bibliographic citations and author commentary using supervised machine learning methodologies. For this thesis, we contextualize bibliographic reference analysis within the broader domain of archival document processing through an original literature survey of current techniques, tools, and trends in the field of historical document processing. Next, we review related work on bibliographic citation identification and reference parsing. Finally, using a historiographic dataset drawn from the JSTOR humanities archive, we train and compare the performance of a suite of single and hybrid machine learning classifiers on a novel, previously unexplored bibliographic reference classification task. Moreover, as a part of this analysis, we compare the performance of traditional features and novel, grammatical features drawn from natural language processing. Our work demonstrates the superiority of hybrid models for classification of scholarly footnotes containing historiographic bibliographic references, the transferability of features from reference extraction to this research problem, and the viability of training machine learning models for this task utilizing novel, grammatical features.;

504 a| Includes bibliographical references.;

650 0 a| Bibliographical citations.=| ^A14986;

650 0 a| Machine-readable bibliographic data.=| ^A37547;

653 a| Bibliographic references;

653 a| supervised machine learning;

653 a| grammar;

655 7 a| Academic theses.2| fast0| (OCoLC)fst01726453;

655 7 a| Academic theses.2| lcgft;

655 7 a| Thèses et écrits académiques.2| rvmgf0| (CaQQLa)RVMGF-000001173;

655 2 a| Academic Dissertation.0| (DNLM)D019478?| UNAUTHORIZED;

700 1 a| Tabrizi, M. H. N.,e| degree supervisor.?| UNAUTHORIZED;

710 2 a| East Carolina University.b| Department of Computer Science.?| UNAUTHORIZED;

856 40 u| http://hdl.handle.net/10342/9733z| Access via ScholarShip;

949 o| wjh;

994 a| C0b| ERE;

596 a| 1 4;

998 a| 5792260;

999 a| CLICK ON WEB ADDRESSw| ASISc| 1i| 5792260-1001l| JNETm| JOYNERr| Ys| Yt| JNE3ETDu| 2/14/2022x| ETDz| JERESOURCE;

999 a| CLICK ON WEB ADDRESSw| ASISc| 1i| 5792260-2001l| HSLELECm| HSLr| Ys| Yt| HEETDu| 2/14/2022x| ETDz| HERESOURCE;

Bibliographic reference analysis in archival data using supervised machine learning and grammatical features / by James Patrick Philips.

Author/creator	Philips, James Patrick author.
Other author	Tabrizi, M. H. N., degree supervisor.
Other author	East Carolina University. Department of Computer Science.
Format	Theses and dissertations
Publication	[Greenville, N.C.] : [East Carolina University], 2021.
Description	1 online resource (78 pages) : illustrations (chiefly color)
Supplemental Content	Access via ScholarShip
Subjects	Bibliographical citations. Machine-readable bibliographic data.

Summary	Bibliographic references are integral to scholarly discourse in humanities disciplines. While prior work has focused on reference extraction and parsing, little research has investigated the classification of footnotes containing bibliographic citations and author commentary using supervised machine learning methodologies. For this thesis, we contextualize bibliographic reference analysis within the broader domain of archival document processing through an original literature survey of current techniques, tools, and trends in the field of historical document processing. Next, we review related work on bibliographic citation identification and reference parsing. Finally, using a historiographic dataset drawn from the JSTOR humanities archive, we train and compare the performance of a suite of single and hybrid machine learning classifiers on a novel, previously unexplored bibliographic reference classification task. Moreover, as a part of this analysis, we compare the performance of traditional features and novel, grammatical features drawn from natural language processing. Our work demonstrates the superiority of hybrid models for classification of scholarly footnotes containing historiographic bibliographic references, the transferability of features from reference extraction to this research problem, and the viability of training machine learning models for this task utilizing novel, grammatical features.
General note	Presented to the faculty of the Department of Computer Science
General note	Advisor: Nasseh Tabrizi
General note	Title from PDF t.p. (viewed November 29, 2022).
Dissertation note	M.S. East Carolina University 2021
Bibliography note	Includes bibliographical references.
Technical details	System requirements: Adobe Reader.
Technical details	Mode of access: World Wide Web.
Genre/form	Academic theses.
Genre/form	Academic theses.
Genre/form	Thèses et écrits académiques.

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Bibliographic reference analysis in archival data using supervised machine learning and grammatical features / by James Patrick Philips.

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