MARC details
| 000 -LEADER |
|---|
| fixed length control field |
04409ntm a2200337 i 4500 |
| 003 - CONTROL NUMBER IDENTIFIER |
|---|
| control field |
MY-KuUP |
| 005 - DATE AND TIME OF LATEST TRANSACTION |
|---|
| control field |
20251125110922.0 |
| 006 - FIXED-LENGTH DATA ELEMENTS--ADDITIONAL MATERIAL CHARACTERISTICS |
|---|
| fixed length control field |
t||||fr|||| 000 0 |
| 007 - PHYSICAL DESCRIPTION FIXED FIELD--GENERAL INFORMATION |
|---|
| fixed length control field |
ta |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
|---|
| fixed length control field |
240902s20242024my a|||fr|||| 00| 0 eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
|---|
| International Standard Book Number |
THE0009940 (Local) |
| Qualifying information |
Hardback |
| 040 ## - CATALOGING SOURCE |
|---|
| 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 .A23 2024 r Thesis |
| 100 0# - MAIN ENTRY--PERSONAL NAME |
|---|
| Personal name |
Abbas Saliimi Lokman, |
| Relator term |
author. |
| 245 10 - TITLE STATEMENT |
|---|
| Title |
Modified word representation vector based scalar weight for contextual text classification / |
| Statement of responsibility, etc. |
Abbas Saliimi Lokman |
| 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, 120 pages : |
| Other physical details |
illustration ; |
| Materials specified |
0 cm. +30 cm. + |
| Accompanying material |
1 CD-ROM |
| 336 ## - CONTENT TYPE |
|---|
| Source |
rdacontent |
| Content type term |
text |
| 337 ## - MEDIA TYPE |
|---|
| Source |
rdamedia |
| Media type term |
unmediated |
| 338 ## - CARRIER TYPE |
|---|
| Source |
rdacarrier |
| Carrier type term |
volume |
| 347 ## - DIGITAL FILE CHARACTERISTICS |
|---|
| Source |
rda |
| File type |
text file |
| Encoding format |
PDF |
| 500 ## - GENERAL NOTE |
|---|
| General note |
Faculty of Computing |
| 502 ## - DISSERTATION NOTE |
|---|
| Dissertation note |
Thesis (Master of Science) -- Universiti Malaysia Pahang – 2022 |
| 504 ## - BIBLIOGRAPHY, ETC. NOTE |
|---|
| -- |
ncludes bibliographical referencesIncludes bibliographical references |
| 520 3# - SUMMARY, ETC. |
|---|
| Summary, etc. |
This thesis investigates contextual text classification, which is the process of categorising textual data into different classes or categories based on its meaning within a given context. Central to this process is the representation of words through vectors for computational interpretation. Current practices employ Large Language Models (LLMs) to generate contextualised word representation vectors, achieved through pre-training the LLM on vast corpora that enables it to grasp intricate language patterns and context. For contextual text classification, the pre-trained LLM is further train on classificationspecific labeled data in a process called fine-tuning. Although this approach is currently considered the most optimal in the field, it poses a notable challenge due to the substantial demand for computing resources stemming from the vast number of trainable parameters in LLMs. Furthermore, although pre-trained LLMs can generate contextualised word representation vectors, they lack the flexibility to modify the semantic significance of these vectors outside of the LLM, necessitating fine-tuning for the modification of word vectors. To bridge this gap, a five-phase research methodology is structured to propose and evaluate an algorithm enabling the external modification of LLM-generated word vectors using scalar values as the focus weightage. To validate this algorithm, the modified word vectors are compared with original LLM-generated word vectors to evaluate their reflection of the intended context. In addition, a contextual text classification experiment is conducted using benchmarked datasets to assess the performance of the modified word vectors in the targeted classification task. For this experiment, the modified word vectors serve as input to train a Machine Learning (ML) model for the text classification process, aiming for the developed ML model to have a significantly smaller parameter count. This experiment aims to determine the effectiveness of the modified word vectors in contextual text classification tasks, utilizing a more computationally efficient approach. Based on the acquired results, the experiments reveal that the modified word vectors algorithm can effectively alter original LLM-generated word vectors to reflect intended contexts and can outperform baseline scores in contextual text classification tasks. Evaluation metrics including Accuracy, Precision, Recall, and F1 score are employed in the evaluation process, with Accuracy and F1 score serving as primary metrics. The evaluation showcases significant improvements, with the test ML model achieving a best accuracy score of 0.571, a 46% increase from the baseline, and a best F1 score of 0.727, a 30% increment from the baseline. Overall, this thesis presents five contributions: the proposed modified word vectors algorithm, the new contextual classification dataset named QCoC, the efficient question-type classifier based on the feed-forward neural network algorithm, the potential transferability of the presented work to other domains, and the practical implications of the presented work towards cases where computational resources are limited or costly. |
| 610 20 - SUBJECT ADDED ENTRY--CORPORATE NAME |
|---|
| Corporate name or jurisdiction name as entry element |
Faculty of Computing |
| General subdivision |
Dissertations |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| 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 |