Department of Computer Science

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Author: search.filters.author.Dr. Rao Muhammad Adeel NawabSubject: search.filters.subject.Applied mathematics

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    Cross-Lingual Abstractive Text Summarization for English-Urdu Language Pair
    (Library Information Services, COMSATS University Islamabad, Lahore Campus, 2021) Duaa-e-Nadeem; SP20-RCS-001; LHR TP 7600; Dr. Rao Muhammad Adeel Nawab
    Cross-Lingual Abstractive Text Summarization (CLATS) is a process to generate an abstractive summary in Language B (target language) of the given document in Language A (source language). CLATS has the potential to allow cross-lingual information access for millions of people around the world who do not speak or understand languages with a broad online presence and it makes documents originally written in native languages available to a wide audience. The popularity of digital documents has resulted in a rapid increase in the amount of information available online. This massive volume of data, however, cannot be manually analyzed. A summary of a big written document allows individuals to quickly grasp the topic's material without reading the entire document. CLATS plays a vital role in news summarization, literature summarization, timeline/event summarization and summarization of Wikipedia or any information source articles for better understanding. This research aims at exploring the problem of CLATS. Previously, CLATS systems have been developed for different language pair like Chinese-English, German-English, Arabic-English and vice versa but this problem is not explored for English-Urdu language pair, even though Urdu is a widely spoken South Asian language. The twofold aim of this research is to fill the gap: (1) develop a large benchmark corpus CLATS for English-Urdu language pair and (2) develop, apply, evaluate and compare state-of the-art deep learning techniques on proposed corpus for CLATS for English-Urdu language pair. For developing CLATS-EU corpus, News Summary and All the News are used as base corpus. The following steps were taken to create the proposed corpus: (1) automatic translation of base corpus (2) manual inspection and correction of testing data of base corpus (3) corpus standardization After development of CLATS-EU corpus, Deep learning sequence to sequence techniques (RNN, LSTM, GRU, BRNN, BLSTM and BGRU) are developed by using CLATS-EU corpus. Then RNN, LSTM and GRU models are implemented by applying attention mechanism. ROUGE1, ROUGE2 and ROUGEL are used as evaluation x measures to evaluate the performance of the techniques that we have developed. For CLATS, GRU gave the best results on our proposed CLATS-EU corpus
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    Toxic Comment Classification of Roman Urdu Text
    (Library Information Services, COMSATS University Islamabad, Lahore Campus, 2019) Waheed Abbas; FA17-RCS-013; LHR TP 5624; Dr. Rao Muhammad Adeel Nawab
    We are living in an era of technology where most of us have easy access to the inter net. Due to internet bloom, the use of social media and discussion forum, in particular for communication, increase tremendously in recent years. But this advancement also opens doors for trolls who poison these social media and forums by their abusive behav ior toward others. Detection of toxic language online is becoming an important issue in recent years. A toxic comment is defined as rude, unreasonable, or disrespectful com ments which likely to make you leave a conversation. Current methods for handling toxicity online are often heavily dependent on manual moderation, and not scalable enough to handle growing users day by day. In this thesis, we investigated the problem of toxic language detection for Roman Urdu (transliteration of Urdu in English) text because Roman Urdu is widely used to communicate online in Pakistani and Indian community. Various work has been done to detect toxicity in the English language, but due to unavailability of large Roman Urdu corpora, there is no work done so far in Roman Urdu. The aim of this thesis work is two folds: first is to develop large scale corpus for detection of toxic language in Roman Urdu, second is to apply different proven classical machine learning and state-of-the-art deep learning methods on the developed corpus. In the first step, we collected data from the YouTube comment section to develop Roman Urdu corpus. The final corpus consists of 10,863 unique comments where we have 5,887 toxic and 4,976 non-toxic comments. We performed manual annotation of corpus and got it reviewed from another annotator. We applied proven classical ma chine learning algorithms like Logistic Regression, K-Nearest Neighbors, Naive Bayes, Random Forest, Support Vector Machine and Multi-layer Perceptron along with state of-the-art deep learning methods like Long Short-Term Memory, Bidirectional Long Short-Term Memory, and Convolutional Neural Networks. Best results obtained from Random Forest with character 4-gram (Accuracy = 0.964, Precision = 0.980, Recall = 0.953, F1 = 0.966).