MARC details
| 000 -LEADER |
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| fixed length control field |
03444ntm a2200337 i 4500 |
| 003 - CONTROL NUMBER IDENTIFIER |
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| control field |
MY-KuUP |
| 005 - DATE AND TIME OF LATEST TRANSACTION |
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| control field |
20251125110919.0 |
| 006 - FIXED-LENGTH DATA ELEMENTS--ADDITIONAL MATERIAL CHARACTERISTICS |
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| fixed length control field |
t||||fr|||| 000 0 |
| 007 - PHYSICAL DESCRIPTION FIXED FIELD--GENERAL INFORMATION |
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| fixed length control field |
ta |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
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| fixed length control field |
240822t20242024my a|||fr|||| 000 0 eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
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| International Standard Book Number |
THE0009926 (Local) |
| Qualifying information |
Hardback |
| 040 ## - CATALOGING SOURCE |
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| Original cataloging agency |
UMPSA |
| Language of cataloging |
eng |
| Transcribing agency |
UMP |
| Description conventions |
rda |
| 090 ## - LOCALLY ASSIGNED LC-TYPE CALL NUMBER (OCLC); LOCAL CALL NUMBER (RLIN) |
|---|
| Classification number (OCLC) (R) ; Classification number, CALL (RLIN) (NR) |
FKOM .B33 2024 r Thesis |
| 100 1# - MAIN ENTRY--PERSONAL NAME |
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| Personal name |
Mohammad Badrul Alam Miah, |
| Relator term |
author. |
| 245 10 - TITLE STATEMENT |
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| Title |
An extended tree-based keyphrase extraction technique (etket) for academic articles based on syntactic features / |
| Statement of responsibility, etc. |
Mohammad Badrul Alam Miah |
| 264 #1 - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE |
|---|
| Place of production, publication, distribution, manufacture |
Kuantan, Pahang : |
| Name of producer, publisher, distributor, manufacturer |
UMPSA, |
| Date of production, publication, distribution, manufacture, or copyright notice |
2024 |
| 264 #4 - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE |
|---|
| Date of production, publication, distribution, manufacture, or copyright notice |
© 2024 |
| 300 ## - PHYSICAL DESCRIPTION |
|---|
| Extent |
xii, 198 pages : |
| Other physical details |
illustrations (some color) ; |
| Accompanying material |
1 CD-ROM |
| 336 ## - CONTENT TYPE |
|---|
| Source |
rdacontent |
| Content type term |
text |
| 337 ## - MEDIA TYPE |
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| Source |
rdamedia |
| Media type term |
unmediated |
| 338 ## - CARRIER TYPE |
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| Source |
rdacarrier |
| Carrier type term |
volume |
| 347 ## - DIGITAL FILE CHARACTERISTICS |
|---|
| Source |
rda |
| File type |
text file |
| Encoding format |
PDF |
| 500 ## - GENERAL NOTE |
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| General note |
Faculty of Computing |
| 502 ## - DISSERTATION NOTE |
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| Dissertation note |
Thesis (Doctor of Philosophy) -- Universiti Malaysia Pahang – 2024 |
| 504 ## - BIBLIOGRAPHY, ETC. NOTE |
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| Bibliography, etc. note |
Includes bibliographical references |
| 520 3# - SUMMARY, ETC. |
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| Summary, etc. |
Recently, automatic keyphrase extraction (AKE) has faced challenges in extracting highquality keyphrases and summarizing information at a superior level due to technological advancements and the exponential growth of digital sources and textual information. Machine learning including unsupervised AKE depends on feature extraction to extract relevant features and ranking procedure to choose significant keyphrases. However, existing unsupervised AKE has some limitations, including the inability to recognize appropriate features that provide diversity and topical coverage, which are occasionally neglected and misguided ranking procedures. In addition, the existing tree-based technique doesn’t use feature extraction, which is vital for achieving good performance, and uses term frequency (TF) only as a key feature, which misguides the ranking procedure to select the most significant keyphrase because the TF values of irrelevant keyphrases are higher than those of relevant keyphrases. Therefore, this thesis sought to develop an extended tree-based keyphrase extraction technique (ETKET) by proposing new features of keyphrases with new formulas and an extended ranking procedure to select the topmost significant keyphrases from academic articles. The proposed technique consists of five main processes: data collection and preprocessing; candidate keyphrase selection; candidate keyphrase processing to select the final candidate keyphrase using a keyphrase extraction (KePhEx) tree; feature extraction to extract new features such as keyphrase frequency, keyphrase centroid, keyphrase distance, keyphrase concentration area, keyphrase position, and keyphrase positions in different sentences; and finally, an extended ranking procedure to select the topmost significant keyphrases. The proposed technique was evaluated on five widely used benchmark long datasets (SemEval2010, Schutz2008, Nguyen2007, Citeulike180, and Cacic) to measure its performance and effectiveness. The obtained results were then compared with state-of-the-art techniques, showing that the proposed technique outperformed others in terms of precision, recall, and F1-score. Thus, the results proved that the ETKET was able to extract the topmost significant keyphrases. |
| 610 20 - SUBJECT ADDED ENTRY--CORPORATE NAME |
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| Corporate name or jurisdiction name as entry element |
Faculty of Computing |
| General subdivision |
Dissertations |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
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| Topical term or geographic name entry element |
Universities and colleges |
| General subdivision |
Dissertations |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name entry element |
Theses |
| General subdivision |
Dissertations |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) |
|---|
| Source of classification or shelving scheme |
Library of Congress Classification |
| Koha item type |
Thesis |