M.Phil / MS

Permanent URI for this collectionhttps://repository.cuilahore.edu.pk/handle/123456789/36

This collection archives the complete set of theses produced by students of the COMSATS University Islamabad, Lahore Campus.

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Textual Cyberbullying Detection on Social Networks using Machine Learning and Deep Learning Models
(Library Information Services, COMSATS University Islamabad, Lahore Campus, 2022) Gull Bano Anwar; FA20-RCS-011; LHR TP 8359; Dr. Muhammad Waqas Anwar
Nowadays Cyberbullying on social media has become a major problem. Cyberbullying may cause many serious and negative mental, emotional and physical impacts on a person's life. However, Cyberbullying leaves a record that can demonstrate value and give proof to help stop digital abuse. The early detection of Cyberbullying on social media becomes crucial to moving the effect on the social media user. In this direction, many studies are conducted to detect Cyberbullying content automatically. The major concern and gap in Cyberbullying detection strategies is the lack of linguistic resources, especially for newly evolved languages. Roman Urdu is a newly emerged and widely used language on social network sites in Asian countries. The greatest strategy to prevent Cyberbullying is to use Machine Learning or Deep Learning with Natural Language Processing (NLP) tools to detect it automatically. The current research proposed an efficient framework to detect Cyberbullying, using NLP tools with Machine Learning and Deep Learning models. Using different preprocessing techniques, the proposed study is validated on a roman-Urdu-abusive-comment detector (RUACD) dataset. Data Preprocessing steps were followed that included text cleaning, tokenization, lemmatization, and removal of stop words. For experimental purposes, five machine learning models Support Vector Machine (SVM), Naïve Bayes (NB), Logistic Regression (LR), Random Forest (RF), and Decision Tree (DT) and 4 deep learning models are evaluated on the RUACD dataset. From experiments of machine learning models, current study finds that the SVM, LR, and DT outperformed and achieved promising results as SVM, LR and DT achieve 96.2%, 94.91, and 94.01 of test accuracy and from experiments of deep learning models, current study find that the DNN, LSTM, and RNN outperformed and achieved promising results as DNN, LSTM, and RNN achieves 90.4%, 86.5, and 85.4 of test accuracy. Ensemble of these outperformed models is formed separately and achieved 95.92% of test accuracy with machine learning ensemble name EN-SLD, and achieved 90.92% of test accuracy with deep learning ensemble name EN-DLR. At last the ensemble of both EN-SLD and EN DLR is formed and achieved 93.92% of test accuracy
Neural Abstractive Text Summarization for Urdu Language
(Library Information Services, COMSATS University Islamabad, Lahore Campus, 2022) Adnan Sanaullah; FA18-RCS-012; LHR TP 8343; Dr. Muhammad Waqas Anwar
Text summarization achieved a lot of popularity in natural language processing because of the large amount of literature available on internet, especially for English language. Nowadays, most used technique is abstractive text summarization in which generated summaries are quite related to the human-written summaries. In this research, to create the summaries for the Urdu language the abstractive text summarization technique is used. In this technique, the Attention based sequence to sequence encoder decoder model are used to create the summaries. For the training of model for Urdu Language, two dataset which are BBC Urdu Dataset and Urdu News 1M are used. In order to evaluate the model, ROUGE metrics are used in which the model generated summary and human-written summary are compared and then performance of the model is measured. After training the model on both datasets, there is the quit the difference between the results of both the datasets which is due to the size of dataset. Although the model got 42.85 rouge-1 score on BBC Urdu Dataset and 66.67 on Urdu News 1M Dataset. Our model shows promising results on both the datasets but if the size of dataset increases the model performs better. We also discussed the problem faced during the completion of research and results of the model in this research work
Fake News Detection and Corpus Development for Urdu News
(Library Information Services, COMSATS University Islamabad, Lahore Campus, 2021) Muhammad Ismail Suri; SP17-RCS-009; LHR TP 7287; Dr. Muhammad Waqas Anwar
With the growing technological advancements in the present era, the curiosity to remain updated about the daily happenings in the world has opened up various opportunities for news dissemination. Access to information with ease has made the propagation of news much efficient and cost effective. However, online news is a double-edged sword that creates a room for spreading fake news which intends to mislead and exaggerate the reality. This issue is a rising problem leading towards several real world sociopolitical and cultural impacts. To curb the menace of fake news different organizations are working to handle it, but majority of the work is being done in English, unfortunately the language of Urdu in this dimension remains untouched. This research is focused on detecting fake news in Urdu language. The demand is to have a detecting mechanism that can predict between fake and real news. In South Asian languages, unavailability of datasets is the major hindrance of evaluating fake news. Therefore, my contribution is the induction of a dataset influenced by English fake news dataset for Urdu news; Corpus of Urdu News for Fake News Detection (COUNFND) which has been derived from digital journalism. It contains 480 documents with 120 claims processed by means of conducting traditional machine learning algorithms and text preprocessing techniques. The corpus holds great importance for the evaluation of fake news detection system specifically for Urdu Language. This thesis discusses different techniques that have been applied on the Urdu language datasets and furthermore achieving classification accuracy of about 73 percent effectively. Later on, a new dataset of fake news in Urdu Language has been introduced by Maaz Amjad (Bend the Truth), which has articles divided into fake and real classification, I have applied different supervised learning models and have achieved an accuracy of 0.89 on random forest using four grams techniques which is greater than the baseline accuracy provided with the dataset.
Structural based Sentiment Mining for Roman Urdu
(Library Information Services, COMSATS University Islamabad, Lahore Campus, 2020) Mubashir Ubaid Ullah; SP18-RCS-013; LHR TP 5986; Dr. Muhammad Waqas Anwar
Web-based data is increasing day by day and plays a vital role in developing people’s opinions. Sentiment mining/analysis is the natural language processing task that helps to identify, classify these opinions. Usually research focus is on resource-rich language for sentiment mining. In this thesis, we performed classification of various sentiments using feature selection techniques for a resource-poor language i.e. Roman Urdu. These classification techniques include chi-square, mutual information and select from model which are implemented on the Roman Urdu Dataset of 11k reviews. Well-known machine learning algorithms are applied for experimental analysis that includes Logistic Regression (LR), Support Vector Machine (SVM), Random Forest (RF), Decision Tree (DT), Multinomial Naïve Bayes (MNB) and Multi-Layer Perceptron (MLP). These are applied for character-level & word-level features for n-gram variations that are bigram to 7-gram for character-level classification and Uni, Bi, Uni Bi gram, Uni-Bi-Tri gram & Uni-Bi-Tri-Four gram in terms of word-level classification. Results are being evaluated using accuracy, precision, recall & f1-score. The Highest accuracies for both word-level and character-level achieved are 83.93% and 83.72% which improves the baseline score that was 82.46% on feature union whereas F1-score is 90.51% & 90.42% respectively. Some renowned Neural Network techniques are also applied in this thesis which include CNN, LSTM, & Bi-LSTTM. We achieved maximum results by Bi-LSTM which gives 91.8% accuracy and 91.7% F1-score
Urdu to English Based Unsupervised Machine Translation
(Library Information Services, COMSATS University Islamabad, Lahore Campus, 2020) Ahmed Raza; SP18-RCS-005; LHR TP 5983; Dr. Muhammad Waqas Anwar
The process of automatically converting the text from one language to another natural language is Machine Translation. Machine Translation is a subfield of computational linguistic. There are two state-of-the-art machine translation techniques i,e Neural Machine Translation (NMT), and Statistical Machine Translation (SMT). In both techniques, a large corpus is required for the training of the translation model. Urdu counts in low resource languages due to the fewer resources available for computational work. To build a good translation system available resources are not enough. Many languages present in the world have a different structure. Like in Urdu and English, Urdu structure is based on Subject Object-Verb (SOV) and the English structure is based Subject-Verb-Object (SVO). In this study, we presented Urdu to English unsupervised translation model and the practical challenges faced during the work. We try to partially remove the need for parallel corpora and proposed a method to train a Machine Translation System in an unsupervised manner. The proposed system is aimed to provide Urdu to English translation through an unsupervised manner. For this propose, we use Artetxe Author developed a toolkit that is based on Unsupervised Neural Machine Translation (UNMT). This approach tested the models of UNMT which include denoising and on-the-fly back-translation. From denoising model obtain the BLEU score 4.14 and 5.11 for two language pairs UR-EN and EN-UR. From back translation obtain the BLEU score of 5.21 and 6.28 which are better than from the previous score. Back-translation results difference from denoising technique gains +1.07 and +1.17 for two language pairs Urdu to English and English to Urdu. We also faced many challenges during work and effects on pre-processing techniques. Our approach shows promising results in translation of Urdu text into English which is mostly neglected due to its complexities
An Empirical Study of Urdu Noun and Verb Phrase Chunking
(Library Information Services, COMSATS University Islamabad, Lahore Campus, 2020) Maryam Khurshid; FA17-RCS-025; LHR TP 5979; Dr. Muhammad Waqas Anwar
Urdu is a language which is a morphologically rich and weak resourced language. The distinguishing features such as free word order, context-sensitive orthography, flexible grammar rules and complex morphology makes the representation of the Urdu language a difficult problem area. In Urdu's hand-written text the words are written without any space among them. A computer needs a text file that needs a separator when a word ends with a non-joiner character. Without these separators, the words will join with one another that will not be understandable for language native speakers. Chunking is a basic technique used for entity detection that labels and segments the sequence of Multi tokens. Chunking technique helps in the progress of many Natural Processing Applications. Chunking is a mature field while dealing with other languages like Hindi, English, Chinese and Turkish but it still requires the attention of researchers in the Urdu language. The Native speakers of Urdu language are more than 70 Million. The study is about the noun and verb phrase chunking in the Urdu language. The intention of this work is to explore the corpus accuracy based on the Noun and verb phrase chunking of the Urdu language. Chunking is an NLP (natural language processing) function that focuses on splitting a text into syntactically linked non-overlapping and non-exhaustive word-groups i.e. a word could only be a part of one chunk but not all words are in chunks. Different experiments are conducted on this work by using a tag set of different input and output schemes with the same Methodology. Firstly, the corpus is selected then preprocessing is performed on that corpus. After that part of speech tagging and IOB tags are assigned to that corpus then Noun and verb phrases are detected by neural networks and machine learning techniques. After this Noun and Verb phrases are detected from a corpus. At last, evaluation will be done by using different Parameters like F-call, recall and Precision.
Urdu-English Machine Transliteration using Neural Networks
(Library Information Services, COMSATS University Islamabad, Lahore Campus, 2019) Usman Mohy ud Din; FA16-RCS-007; LHR TP 5776; Dr. Muhammad Waqas Anwar
Machine translation has gained much attention in recent years. It is a sub-field of computational linguistic which focus on translating text from one language to other language. Among different translation techniques, neural network currently leading the domain with its capabilities of providing a single large neural network with attention mechanism, sequence-to-sequence and long-short term modelling. Despite significant progress in domain of machine translation, translation of out-of-vocabulary words(OOV) which include technical terms, named-entities, foreign words are still a challenge for current state-of-art translation systems, and this situation becomes even worse while translating between low resource languages or languages having different structures. Due to morphological richness of a language, a word may have different meninges in different context. In such scenarios, translation of word is not only enough in order provide the correct/quality translation. Transliteration is a way to consider the context of word/sentence during translation. For low resource language like Urdu, it is very difficult to have/find parallel corpus for transliteration which is large enough to train the system. In this work, we presented transliteration technique based on Expectation Maximization (EM) which is un-supervised and language independent. Systems learns the pattern and out-of-vocabulary (OOV) words from parallel corpus and there is no need to train it on transliteration corpus explicitly. This approach is tested on three models of statistical machine translation (SMT) which include phrase based, hierarchical phrase-based and factor based models and two models of neural machine translation which include LSTM and transformer model. On SMT models, there is gain of 0.63 to 0.91 in BLEU score while on NMT models, there is gain of 1.28 to 2.05 in BLEU which are better than previous baseline scores. Our approach shows promising results in translation of Urdu text into English which is mostly neglected due to its complexities. We also discussed the results, different challenges faced during this work and effect of right pre-processing techniques

M.Phil / MS

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