UUM Electronic Theses and Dissertation
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An enhanced sequential exception technique for semantic-based text anomaly detection

Taiye, Mohammed Ahmed (2019) An enhanced sequential exception technique for semantic-based text anomaly detection. Doctoral thesis, Universiti Utara Malaysia.

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The detection of semantic-based text anomaly is an interesting research area which has gained considerable attention from the data mining community. Text anomaly detection identifies deviating information from general information contained in documents. Text data are characterized by having problems related to ambiguity, high dimensionality, sparsity and text representation. If these challenges are not properly resolved, identifying semantic-based text anomaly will be less accurate. This study proposes an Enhanced Sequential Exception Technique (ESET) to detect semantic-based text anomaly by achieving five objectives: (1) to modify Sequential Exception Technique (SET) in processing unstructured text; (2) to optimize Cosine Similarity for identifying similar and dissimilar text data; (3) to hybridize modified SET with Latent Semantic Analysis (LSA); (4) to integrate Lesk and Selectional Preference algorithms for disambiguating senses and identifying text canonical form; and (5) to represent semantic-based text anomaly using First Order Logic (FOL) and Concept Network Graph (CNG). ESET performs text anomaly detection by employing optimized Cosine Similarity, hybridizing LSA with modified SET, and integrating it with Word Sense Disambiguation algorithms specifically Lesk and Selectional Preference. Then, FOL and CNG are proposed to represent the detected semantic-based text anomaly. To demonstrate the feasibility of the technique, four selected datasets namely NIPS data, ENRON, Daily Koss blog, and 20Newsgroups were experimented on. The experimental evaluation revealed that ESET has significantly improved the accuracy of detecting semantic-based text anomaly from documents. When compared with existing measures, the experimental results outperformed benchmarked methods with an improved F1-score from all datasets respectively; NIPS data 0.75, ENRON 0.82, Daily Koss blog 0.93 and 20Newsgroups 0.97. The results generated from ESET has proven to be significant and supported a growing notion of semantic-based text anomaly which is increasingly evident in existing literatures. Practically, this study contributes to topic modelling and concept coherence for the purpose of visualizing information, knowledge sharing and optimized decision making.

Item Type: Thesis (Doctoral)
Supervisor : Kamaruddin, Siti Sakira and Kabir Ahmad, Farzana
Item ID: 8112
Uncontrolled Keywords: Semantic similarity, Semantic-based text anomaly, Word Sense Disambiguation, Enhanced Sequential Exception Technique.
Subjects: T Technology > T Technology (General) > T58.5-58.64 Information technology
Q Science > QA Mathematics > QA273-280 Probabilities. Mathematical statistics
Divisions: Awang Had Salleh Graduate School of Arts & Sciences
Date Deposited: 08 Mar 2021 08:51
Last Modified: 09 May 2022 08:13
Department: Awang Had Salleh Graduate School of Arts & Sciences
Name: Kamaruddin, Siti Sakira and Kabir Ahmad, Farzana
URI: https://etd.uum.edu.my/id/eprint/8112

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