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008			240822t20242024my a|||fr|||| 000 0 eng d
020			_aTHE0009926 (Local) _qHardback
040			_aUMPSA _beng _cUMP _erda
090			_aFKOM .B33 2024 r Thesis
100	1		_aMohammad Badrul Alam Miah, _eauthor.
245	1	0	_aAn extended tree-based keyphrase extraction technique (etket) for academic articles based on syntactic features / _cMohammad Badrul Alam Miah
264		1	_aKuantan, Pahang : _bUMPSA, _c2024
264		4	_c© 2024
300			_axii, 198 pages : _billustrations (some color) ; _e1 CD-ROM
336			_2rdacontent _atext
337			_2rdamedia _aunmediated
338			_2rdacarrier _avolume
347			_2rda _atext file _bPDF
500			_aFaculty of Computing
502			_aThesis (Doctor of Philosophy) -- Universiti Malaysia Pahang – 2024
504			_aIncludes bibliographical references
520	3		_aRecently, 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	2	0	_aFaculty of Computing _xDissertations
650		0	_aUniversities and colleges _xDissertations
650		0	_aTheses _xDissertations
942			_2lcc _cTHESIS