UUM ETD | Universiti Utara Malaysian Electronic Theses and Dissertation
FAQs | Feedback | Search Tips | Sitemap

Topic identification using filtering and rule generation algorithm for textual document

Nurul Syafidah, Jamil (2015) Topic identification using filtering and rule generation algorithm for textual document. Masters thesis, Universiti Utara Malaysia.

[img] Text
Restricted to Registered users only

Download (3MB)

Download (1MB) | Preview


Information stored digitally in text documents are seldom arranged according to specific topics. The necessity to read whole documents is time-consuming and decreases the interest for searching information. Most existing topic identification methods depend on occurrence of terms in the text. However, not all frequent occurrence terms are relevant. The term extraction phase in topic identification method has resulted in extracted terms that might have similar meaning which is known as synonymy problem. Filtering and rule generation algorithms are introduced in this study to identify topic in textual documents. The proposed filtering algorithm (PFA) will extract the most relevant terms from text and solve synonym roblem amongst the extracted terms. The rule generation algorithm (TopId) is proposed to identify topic for each verse based on the extracted terms. The PFA will process and filter each sentence based on nouns and predefined keywords to produce suitable terms for the topic. Rules are then generated from the extracted terms using the rule-based classifier. An experimental design was performed on 224 English translated Quran verses which are related to female issues. Topics identified by both TopId and Rough Set technique were compared and later verified by experts. PFA has successfully extracted more relevant terms compared to other filtering techniques. TopId has identified topics that are closer to the topics from experts with an accuracy of 70%. The proposed algorithms were able to extract relevant terms without losing important terms and identify topic in the verse.

Item Type: Thesis (Masters)
Uncontrolled Keywords: Topic identification, Filtering algorithm, Rule generation algorithm, Rough Set, Al-Quran verses.
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: Awang Had Salleh Graduate School of Arts & Sciences
Depositing User: Mr. Badrulsaman Hamid
Date Deposited: 03 Jan 2016 06:18
Last Modified: 24 Apr 2016 07:35
URI: http://etd.uum.edu.my/id/eprint/5379

Actions (login required)

View Item View Item