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003 OCoLC;

005 20240430153513.0;

006 m o d ;

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

008 220914s2022 ncua obm 000 0 eng d;

019 a| 1371248783;

035 a| (Sirsi) o1344380204;

035 a| (OCoLC)1344380204z| (OCoLC)1371248783;

049 a| EREE;

090 a| QA76.9.Q4;

100 1 a| Babu, Ganesh,e| author.?| UNAUTHORIZED;

245 10 a| Qa4r :b| a question answering system for r packages /c| by Ganesh Babu.;

246 3 a| Qa4r a question answering system for r packages;

264 1 a| [Greenville, N.C.] :b| [East Carolina University],c| 2022.;

300 a| 1 online resource (49 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| 1.181 MB;

538 a| System requirements: Adobe Reader.;

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

502 b| M.S.c| East Carolina Universityd| 2022;

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

500 a| Advisor: Venkat Gudivada;

500 a| Title from PDF t.p. (viewed January 29, 2024).;

520 3 a| There is a massive amount of data from various sources available today, and querying meaningful information from those datasets would be valuable. Question Answering Systems (QAS) implement information retrieval (IR) and Natural Language Processing (NLP) that can automatically answer the questions posed in a natural language. There are three different types of QAS as Open Domain, Closed Domain, and Restricted Domain. Following are the various types of questions: fact-based, definition, how, why, hypothetical, semantically constrained, and cross-lingual. R is a dynamic programming language widely used for statistical computing that combines functional and object-oriented programming. The R development community maintains thousands of R packages through its Comprehensive R Archive Network CRAN. However, while websites like rdrr.io, rseek.org, and search.r-project.org provide search results for R packages, no intelligent question-answering system is currently available for R.This study examines Question Answering Systems (QAS), current developments and academic research areas in the QAS field, and QAS implementations. In this research, we propose a prototype question answering system for R packages that returns R packages relevant to the user query in natural language. We created a question answering dataset (QAD4R) for R packages using web scraping and developed a question generation model. Pre-trained BERT-based language models were used to create the question-answering system for R. All the code files are available publicly at this GitHub location https://github.com/GanB/QA4R-A-Question-AnsweringSystem-for-R-Packages.;

504 a| Includes bibliographical references.;

650 0 a| Question-answering systems.=| ^A84757;

650 0 a| R (Computer program language)=| ^A523882;

650 0 a| Natural language processing (Computer science)=| ^A235570;

653 a| Question Generation;

653 a| Word Tokenization;

653 a| N-Grams;

653 a| Transformers;

653 a| BERT;

655 7 a| dissertations.2| aat0| (CStmoGRI)aatgf300028029;

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;

700 1 a| Gudivada, Venkat N.,e| degree supervisor.=| ^A1308613;

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

856 40 u| http://hdl.handle.net/10342/11094z| Access via ScholarShip;

949 o| jwjh;

994 a| C0b| ERE;

596 a| 1 4;

998 a| 5909021;

999 a| CLICK ON WEB ADDRESSw| ASISc| 1i| 5909021-1001l| JNETm| JOYNERr| Ys| Yt| JNE3ETDu| 9/14/2022x| ETDz| JERESOURCE;

999 a| CLICK ON WEB ADDRESSw| ASISc| 1i| 5909021-2001l| HSLELECm| HSLr| Ys| Yt| HEETDu| 9/14/2022x| ETDz| HERESOURCE;

Qa4r : a question answering system for r packages / by Ganesh Babu.

Author/creator	Babu, Ganesh author.
Other author	Gudivada, Venkat N., degree supervisor.
Other author	East Carolina University. Department of Computer Science.
Format	Theses and dissertations
Publication	[Greenville, N.C.] : [East Carolina University], 2022.
Description	1 online resource (49 pages) : illustrations (chiefly color)
Supplemental Content	Access via ScholarShip
Subjects	Question-answering systems. R (Computer program language) Natural language processing (Computer science)

Variant title	Qa4r a question answering system for r packages
Summary	There is a massive amount of data from various sources available today, and querying meaningful information from those datasets would be valuable. Question Answering Systems (QAS) implement information retrieval (IR) and Natural Language Processing (NLP) that can automatically answer the questions posed in a natural language. There are three different types of QAS as Open Domain, Closed Domain, and Restricted Domain. Following are the various types of questions: fact-based, definition, how, why, hypothetical, semantically constrained, and cross-lingual. R is a dynamic programming language widely used for statistical computing that combines functional and object-oriented programming. The R development community maintains thousands of R packages through its Comprehensive R Archive Network CRAN. However, while websites like rdrr.io, rseek.org, and search.r-project.org provide search results for R packages, no intelligent question-answering system is currently available for R.This study examines Question Answering Systems (QAS), current developments and academic research areas in the QAS field, and QAS implementations. In this research, we propose a prototype question answering system for R packages that returns R packages relevant to the user query in natural language. We created a question answering dataset (QAD4R) for R packages using web scraping and developed a question generation model. Pre-trained BERT-based language models were used to create the question-answering system for R. All the code files are available publicly at this GitHub location https://github.com/GanB/QA4R-A-Question-AnsweringSystem-for-R-Packages.
General note	Presented to the Faculty of the Department of Computer Science
General note	Advisor: Venkat Gudivada
General note	Title from PDF t.p. (viewed January 29, 2024).
Dissertation note	M.S. East Carolina University 2022
Bibliography note	Includes bibliographical references.
Technical details	System requirements: Adobe Reader.
Technical details	Mode of access: World Wide Web.
Genre/form	dissertations.
Genre/form	Academic theses.
Genre/form	Academic theses.
Genre/form	Thèses et écrits académiques.

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Qa4r : a question answering system for r packages / by Ganesh Babu.

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