Department of Computer Science
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Item A Benchmark Corpus and Methods for the Urdu Qur’anic Question-Answering System(Library Information Services, COMSATS University Islamabad, Lahore Campus, 2025) Abida Ashraf; SP23-RCS-002; Dr. Rao Muhammad Adeel Nawab; LHR TP 9497Question Answering System aims to provide answers to a question posed in natural language. A question can be factoid and non-factoid. With the rapid increase in technologies, the QA task become one of the most challenging and important tasks of Natural Language Processing (NLP). In previous studies, the problem of the QA system for the Holy Quran has been explored for Arabic, English, and Indonesian languages. Urdu has more than 170 million speakers worldwide yet considered low low-resource language. However, the problem of the QA system for the Holy Quran has not been explored for the Urdu language due to the unavailability of the dataset. To overcome this research gap, this study developed a benchmark corpus of the Holy Quran in the Urdu language. We developed the corpus by carefully mapping the QRCD corpus which was originally in Arabic language into Urdu. For the mapping of answers, we take the expertise of data annotators who have a deep knowledge of the Holy Quran in both Arabic and Urdu language. We conducted experiments in a state-of-the-art BERT based LLMs. We evaluate the proposed techniques using Exact Match, partial Reciprocal Rank, and F1 score, and expert judgment.Item Toxicity Detection in Speech(Library Information Services, COMSATS University Islamabad, Lahore Campus, 2021) Mohsin Nazakat , Samiya Ijaz, Ghulam Rasool; FA18 BCS-052, FA18 BCS-143, FA18 BCS-112; Dr. Rao Muhammad Adeel NawabSpeech is a fundamental source of communication. We express our ideas, thoughts, and sentiments through speech. What we say (our speech) has a very significant effect on our personalities and the personalities of our listeners. A positive speech spreads positivity in society, gives peace to its speaker and pleasure to the listeners. On the other hand, negative or toxic speech cause great damage to our society. It not only has a negatively effects the minds of the speaker and listener but also spoils their personalities. Ultimately toxic conversation reshapes human personality in such a way that might leads speakers and listeners to get involved in toxic actions. So, it is momentous to put a stop to these toxic conversations in our society. According to the best of our knowledge, there is no existing system that can classify a digital audio conversation (digital speech) as toxic or normal. To fulfil this gap, we have built an administrative android mobile app that will notify the parent whenever an audio speech is classified as toxic. This will allow parent to guide their children to avoid having such toxic conversations. Using the digital audio data of day-to-day conversations, we have trained a Machine Learning model, and using the REST API call, we will send audio to the model and in response, we get a prediction of whether the audio is toxic or normal. In the case of Toxicity, a notification is dispatched to the parent to notify them that a toxic conversation is detected.Item Developing a Performance Evaluation and Course Recommendation System for Student(Library Information Services, COMSATS University Islamabad, Lahore Campus, 2021) Chaudary Atif Raza Warraich , Asghar Afzal , Muham; SP17-BSE-128 , SP17-BSE-050 , SP17-BSE-059; Dr. Rao Muhammad Adeel Nawab; LHR TP 7120Recommenders systems are widely used in a number of field, especially in e-commerce. In past few years, these systems also occupy the major areas of e-learning systems. We will be carrying out detailed analysis on student performance and course recommendation in this final year project. The input to our system is the student marks and our system will fetch all the relative facts and figures that will lead to check the student performance and on this performance, we will recommend a course to student. We will apply various pre-processing techniques to prepare the data for detailed analytics. An efficient and effective Dashboard will be developed to show student performance and recommended courses to the student.Item Information Retrieval System For Urdu Language(Library Information Services, COMSATS University Islamabad, Lahore Campus, 2021) HASSAN NAWAZ , SAJID DIN MUHAMMAD; SP17-BCS-125 , SP17-BCS-059; Dr. Rao Muhammad Adeel Nawab; LHR TP 7188Internet Searches in the learning environment differs from traditional “Query” or “known objects” research: Students or common person are typically looking for or master a specific topic, and search engines help them to find that information. This report presents IRS (Information Retrieval System), an exploratory search engine for learning resources based on Wikipedia articles: provides set of topics in Urdu language and relationship between Wikipedia articles provides suggestion to Urdu native speakers to deepen research in a specific field. IRS indexes several hundred thousand Wikipedia articles. This report also contains the results of evaluation of IRS show that the exploratory research paradigm using Wikipedia knowledge is a promising method that can be used to create information retrieval systems for learning environments.Item Developing a Gold Standard Benchmark Corpus and Methods for Multi-label Emotion Classification for Urdu Language(Library Information Services, COMSATS University Islamabad, Lahore Campus, 2023) Azqa Abdul Ghani; SP22-RCS-025; LHR TP 8685; Dr. Rao Muhammad Adeel NawabEmotion Classification (EC) involves detecting the emotional tone conveyed by text, while Multi-Label Emotion Classification (MLEC) aims to assign relevant emotion labels that accurately reflect the author's state of mind. This research focuses on MLEC in Urdu text. In previous studies, only one work has been done for the problem of MLEC, including the development of a dataset based on Urdu Tweets using six emotions [5]. However, the problem of MLEC has not been explored for 12 emotions and diverse genres. To fulfill this research gap, this research study has developed a large benchmark corpus of 16,011 Urdu reviews, manually annotated with 12 emotions and gathered from 9 different genres. Furthermore, this research study has Developed, Applied, Evaluated, and Compared Classical Machine Learning, Deep Learning and Transfer Learning based methods. Whereas in Transfer Learning methods, we have implied 6 distinct sentence transformers (xlm-r-100langs-bert-base-nli-stsb-mean-tokens, xlm-r-100langs-bert-base nli-mean-tokens, gtr-t5-large, all-mpnet-base-v2, distiluse-base-multilingual-cased-v2, and LaBSE), Also the Combined Feature-based approach is applied which averages the features of all the columns of the sentence transformer model and then the Machine Learning models are applied. The proposed transformers-based approach outperforms with an F1 score of 0.4562 using the ‘xlm-r-100langs-bert-base-nli-stsb-mean-tokens’ sentence transformerItem Urdu Text Reuse Detection (UTRD) at Phrasal Level using Transformer-Based Approached(Library Information Services, COMSATS University Islamabad, Lahore Campus, 2022) Gull Mehak; SP21-RCS-006; LHR TP 8058; Dr. Rao Muhammad Adeel NawabText reuse is a process of creating new text(s) from pre-existing text(s). In recent years, Urdu Text Reuse Detection (UTRD) has gained the attention of researchers because the text is readily available in digital format all over the internet and can be copied or paraphrased from another source without proper attribution, which makes it easier to reuse but hard to detect. In previous studies, the problem of UTRD has been explored at the sentence level [1], sentence/passage level [2], and document level [3], along with benchmark corpora and methods. However, the problem of UTRD has not been explored at the Phrasal level with respect to corpora and methods. To fulfill this research gap, this research study has developed a large benchmark manually annotated corpus of 25,001 text pairs at two levels of rewrite: 1) Derived = 15,105 and 2) Non Derived = 9,896. Secondly, this research study has developed, applied, evaluated, and compared baseline approaches (N-gram Overlap and Word Embedding-based approaches) with proposed transformer based approaches on the proposed UTRD-Phr-22 corpus. The proposed transformers-based approach (using a combination of 8 different Sentence Transformers including: paraphrase- multilingual-mpnet-base-v2, distiluse-base-multilingual-cased-v, paraphrase multilingual-MiniLM-L12-v2, LaBSE, xlm-r-distilroberta-base-paraphrase-v1, xlm-r 100langs-bert-base-nli-mean-tokens, xlm-r-bert- base-nli-stsb-mean-tokens, and xlm-r 100langs-bert-base-nli-stsb-mean-tokens) outperforms with an F1 score of 0.63 compared to the best results obtained uisng N-gram Overlap approach (F1 = 0.53)Item Image Caption Generation for Urdu Language – Data and Methods(Library Information Services, COMSATS University Islamabad, Lahore Campus, 2021) Nimra Shafiq; SP20-RCS-010; LHR TP 7602; Dr. Rao Muhammad Adeel NawabImage Captioning (IC) generates a suitable caption for the given query image. The focus of IC is understanding the contextual meaning of visual contents in an image and then describing them in semantically and syntactically correct description in the natural language. The most salient part of an image is described in a sentence to increase the visual understanding of people. It has many applications like navigation guidance for blind persons, image-based optimized search engines, visual context understanding, early babyhood education, image description in an e-commerce site, and image indexing. Previously most of the work done on Image Captioning was in English and other high resource languages like Chinese, Arabic, and Hindi, etc. To the best of our knowledge, the Urdu language has not been explored previously for Urdu Image Captioning (UIC). The primary reason for this is the unavailability of benchmark corpora for UIC. So, the foremost aim of this research is to develop a benchmark Image Captioning corpus for the Urdu language (known as UIC-21 corpus). The proposed corpus is based on the Fliker-8k English corpus. It is developed by translating the English image caption into Urdu image captions using a semi-automatic translation approach. In this approach, automated translation tools are used to automatically translate the image captions in Urdu language and then a manual inspection and correction approach is used to improve the quality of the captions. To demonstrate how UIC systems can be developed and evaluated on our proposed UIC-21 corpus, we applied deep learning techniques (CNN for images and RNN, LSTM, and GRU for Urdu textual data) and state-of-the-art transfer learning techniques (InceptionV3 with ImageNet weights and VGG16 with ImageNet weights for images, and pre-trained word2vec urduvec and W2V models for textual data) for UIC systems, which are further evaluated using BELU-1, BLEU-2, BLEU-3, and BLEU-4. Their experimental results show that the transfer learning InceptionV3-GRU with the pretrained word embedding method achieved the highest BLEU-1 score of 0.60 on our proposed UIC-21 corpusItem Cross-Lingual Paraphrase Detection for English Urdu Language Pa(Library Information Services, COMSATS University Islamabad, Lahore Campus, 2021) Nida Waheed; SP19-RCS-025; LHR TP 7598; Dr. Rao Muhammad Adeel NawabThe main aim of the Cross-Lingual Paraphrase Detection (CLPD) task is to identify whether a text pair (text 01 in language A and text 02 in language B) are paraphrased of one another or not. With the massive increase in technological advancements in digital media and internet facilities, the availability of digital content across multiple languages has become free as well as easy to access. Besides paraphrasing, there has been a rise in cross-lingual paraphrasing since the development of advanced text processing tools. Moreover, the facility of translating existing texts from the original language into any other desired language using freely available translation tools has worsened the problems and triggered an increase in cross-lingual paraphrasing. Previously, there has been plenty of work done for the cross-lingual paraphrase detection in different languages but the downside is that under-resourced languages are still neglected. Urdu is a widely spoken language, despite the fact, the work done for the Urdu language in the CLPD domain is notably less because of the complexity and challenges of the Urdu language. It still comes in the category of low-resource languages. The research in the text analysis domain is mostly focused on well-resourced languages such as English and other European languages. The CLPD problem has formerly been explored for English-Czech, English German, English-Spanish, English Chinese, etc. language pairs. However, the CLPD problem has not yet been studied and reported for the English-Urdu language pair. Although, Cross-Lingual Paraphrasing is now being used enormously in various domains such as content writing, blog posts, and academics, especially in research works related to different languages. To overcome this research gap, a benchmark corpus and approaches are presented through this study for CLPD. The first objective of this research work is to develop a large benchmark Cross-Lingual Paraphrase Detection Corpus (CLPD-EU) for English-Urdu language pair at the sentence level by using a semi-automatic translation approach. For the creation of the CLPD-EU corpus, an existing corpus named Microsoft Research Paraphrase x Corpus (MRPC) is used. The second chief objective of this research work is to make a thorough comparison of classical machine learning (Bilingual Dictionary Based Approaches), Cross-lingual Word Embedding Based Approaches, Transfer Learning (Sentence Transformers Based Approaches) Approaches, and Feature Fusion Approaches on our proposed corpus. The results exhibited that our proposed Feature Fusion Approach 1 has shown notable performance for CLPD. The evaluation of all these approaches is carried out using the weighted average of Precision, Recall, and F1 scores.Item Urdu Short Text Reuse Detection Using Sentence Transformers(Library Information Services, COMSATS University Islamabad, Lahore Campus, 2021) Hamza Hafeez; SP19-RCS-014; LHR TP 7589; Dr. Rao Muhammad Adeel NawabThe process of creating new text(s) using an existing one is called Text Reuse. The goal of text reuse detection is to identify whether a new text is created using a source text or not. A source text can be reused at different rewrite levels to generate either partially (Paraphrased) or fully (Verbatim) reused texts pairs. The Paraphrased Text Reuse shows the relationship between two text pairs conveying the same idea differently. Paraphrased text reuse text is difficult to detect since different edit operations can be used to generate it which varies considerably according to the context where it is being used. In literature, the Paraphrase Text Reuse Detection problem has been explored for English as well as some other European languages, but it has not been investigated for the Urdu language because of the non-availability of the Urdu paraphrase text reuse detection corpus. Therefore, to fulfill this gap this study aims to develop a large-scale gold standard benchmark Urdu Paraphrase Text Reuse Corpus (UPTRC) for the Urdu text reuse detection with special emphasizes on paraphrase text reuse detection for Urdu language. In addition, we have applied the baseline approaches (N-Gram Based Approaches) and proposed other approaches including Word Embedding Based Approaches, Sentence Transformers Based Approaches, and other Feature Fusion Approaches for Urdu Paraphrase Text Reuse Detection (UPTRD). However, Urdu Short Text Reuse Detection (USTRD) has been explored at the sentence level, passage level and document level along with different approaches that have been previously applied on Urdu Short Text Reuse Corpus (USTRC). Nonetheless, the USTRD task has only been explored with lexical and semantic approaches on USTRC but has not been explored with state-of-the-art Sentence Transformers Based Approaches and Word Embedding Based Approaches. The Sentence Transformers Based Approaches are used in a variety of applications and natural language processing tasks, especially for semantic text similarity and duplicate pair detection. But the Sentence Transformers has not been explored for Urdu text reuse detection. So, there is a need to explore the Sentence Transformers Based Approaches on the USTRC corpus for the task of USTRD. In this study, along with the corpus construction, we have applied the N-Gram Based Approaches as baseline and proposed new approaches including Word Embedding Based Approaches, Sentence Transformers Based Approaches, and Feature Fusion Approaches for our proposed UPTRC corpus. We have proposed applied compared and evaluated same approaches for USTRC corpus as well as our proposed corpus to investigate the performance of our proposed approaches. The best results obtained were the weighted average of F1 = 0.855607 by our proposed Feature Fusion Approaches for the task of UPTRD on our proposed UPTRC corpus. In the case of the USTRC corpus, our proposed Feature Fusion Approaches still outperformed the baseline approaches for ternary classification with F1 = 0.715164. The performance of the proposed approaches for both corpora is evaluated and reported using the weighted average of Precision (P), Recall (R), and F1 evaluation measures.Item Smart Reply Suggestion for an Email in Multiple Languages – Data and Metho(Library Information Services, COMSATS University Islamabad, Lahore Campus, 2021) Nazia Jehan; FA19-RCS-002; LHR TP 7295; Dr. Rao Muhammad Adeel NawabSmart Reply Suggestion (SRS) is a novel end-to-end system that refers toward the method of suggesting the three short email responses. These responses are themselves complete short email replies, suggested in diverse form for (monolingual) email i.e. English. In literature, mostly the model is specifically designed for monolingual emails like English. There is a paucity of state-of-the-art techniques that handle both monolingual and multilingual email at a time. Moreover, there is a paucity of benchmark corpus in multiple languages-based emails. This research has tried to overcome the limitations of an earlier study of the SRS system by proposing a novel method for the SRS system which handle both the monolingual and multilingual email at a time. In this research, we treat this problem as a multilabel text classification problem. we developed the " (ML-SRS) Email corpus collected from academia, consisting of three different languages including English, Roman Urdu and a combination of Roman Urdu and English emails (text) and developed the list of smart response prepared to annotate that dataset manually the inter-annotator agreement calculated and then standardized the dataset in .CSV format. For our proposed SRS system, we considered two approaches. In the first approach, we considered content-based n-gram approach at word and character level with the combination of multi-label classifier i.e. One Vs Rest, Label Powerset, with eight classical machine learning classifiers i.e. Linear SVC, Logistic Regression etc. In the second approach, we consider LSTM, CNN, GRU, Bi GRU, Bi-LSTM models. Both approaches are evaluated on Micro Precision, Micro Recall, Micro F1 score and Hamming loss. However, Label Powerset with the combination of Linear SVC performed well as compared to others