Department of English
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Item A Comparative Study of DeepSeek R1 (Reasoning) and GPT-4.0 (Non-Reasoning) Agentic AI Models for Irony and Sarcasm Detection in Urdu and English(Library Information Services, COMSATS University Islamabad, Lahore Campus, 2025) Samra Hameed; CIIT/SP24-MEL-009/LHR; Dr. Saima Akhtar; LHR TP 10053Artificial Intelligence (AI) systems often face difficulties in accurately identifying irony and sarcasm because these forms of figurative language convey meanings that differ from their literal expressions. Successful detection of such linguistic phenomena requires AI models to possess contextual understanding, cultural awareness, and the ability to interpret subtle linguistic cues. English is considered a high-resource language with extensive training data available for AI systems, whereas Urdu is a comparatively low-resource language with limited datasets. As a result, AI models generally perform better in English than in Urdu. Detecting sarcasm and irony across both languages remains challenging due to the linguistic and cultural complexities associated with Urdu. Furthermore, limited research has been conducted on comparing the performance of reasoning and non-reasoning AI models in this domain. This study presents a comparative analysis of ChatGPT-4.0, a non-reasoning agentic AI model, and DeepSeek R1, a reasoning-based AI model, to evaluate their effectiveness in detecting irony and sarcasm in English and Urdu texts. A mixed-methods research design was adopted, combining quantitative performance evaluation with qualitative error analysis. The study utilized a total of 10,000 text samples, comprising 5,000 English and 5,000 Urdu instances. The English dataset included sarcastic and non-sarcastic content collected from Twitter and Reddit, while the Urdu dataset was based on the Urdu Sarcastic Tweets (UST) corpus and additional Urdu-language data. By comparing the performance of the two AI models across both languages, the study aims to provide insights into the strengths and limitations of reasoning and non-reasoning approaches in multilingual sarcasm and irony detection. The findings contribute to the growing field of Natural Language Processing (NLP) and highlight the challenges and opportunities associated with developing AI systems for low-resource languages such as Urdu.Item Syntax-driven Sentence Simplification: A Computational linguistics Approach to enhance text Readability for Non-native English Speakers(Library Information Services, COMSATS University Islamabad, Lahore Campus, 2025) Aleena Rashid; CIIT/SP24-MEL-013/LHR; Dr. Saima Akhtar; LHR TP 10057This thesis presents a comprehensive investigation into syntax-driven sentence simplification as a computational linguistics approach to enhance text readability for non-native English speakers. Drawing upon established research in syntactic simplification and text cohesion (Siddharthan, 2006), this study develops a rule-based text simplification system that targets complex syntactic structures, particularly relative clauses and coordinate structures. The research methodology employs dependency parsing using SpaCy v3.5 to identify and transform syntactic patterns that pose comprehension challenges for non-native speakers. The system implements transformation rules based on syntactic simplification principles established by Chandrasekar et al. (1996) and extends the work of Medero and Ostendorf on identifying targets for syntactic simplification. The evaluation framework incorporates SARI (System for Assessing Text Simplification) metrics as proposed by Xu et al. (2016), alongside human evaluation protocols to assess simplification quality, meaning preservation, and grammaticality. Results demonstrate significant improvements in text readability while maintaining semantic coherence through the application of rhetorical structure theory and centering theory principles. The study contributes to the field of computational linguistics by providing a systematic approach to syntactic simplification that addresses the specific needs of non-native English speakers in educational contexts.Item Enhancing Multimodal Sentiment Analysis for Urdu Using Cross Modal Transfer Learning(Library Information Services, COMSATS University Islamabad, Lahore Campus, 2025) Muhammad Uzair Majeed; CIIT/SP24-MEL-008/LHR; Dr. Saima Akhtar; LHR TP 10052Urdu, which is spoken by more than 230 million people around the world, is also a case in point, since it is severely underrepresented in the field of natural language processing research. Most of the existing systems used for Urdu sentiment analysis rely solely on the text, overlooks the rich emotional indications in speech e.g., tone, emphasis and prosody. This gap and the inadequacy of datasets or complex code-switching patterns between Urdu and English combined has restricted the accuracy of sentiment analysis in this low-resource language. This research involved creation of multimodal sentiment analysis framework for Urdu by combining text and audio using cross modal transfer learning. We built a dataset of 10,847 aligned text-audio pairs obtained from Facebook, Twitter and from original recordings and annotated in the positive, negative and neutral categories with high overlap between annotators (Cohen's kappa = 0.78). The framework uses multilingual BERT (mBERT) for the encoding of the text and convolutional neural networks for audio feature extraction. An attention based fusion mechanism is a dynamic mechanism that computes weight for modality contribution dependent upon the signal quality. Cross-modality transfer learning - through contrastive loss and knowledge distillation - allows conducting knowledge transfer across modalities. The proposed framework was able to achieve 84.7% accuracy and 83.2% macro F1-score on the held out test set. And it stood a good 21.8% relative improvement from text only mBERT (79.2%) and also 24.0% improvement over traditional SVM baselines (68.3%). Ablation studies validated that cross-lingual transfer had a positive effect of 8.4 percentage points while multimodal integration had a positive effect of 5.5 points and attention-based fusion +3.4 points over early fusion. The model is quite good at processing code-switched content (83.1% accuracy for instances mixing Urdu and English) and low demographic bias with respect to age, gender and regional variations. These findings show that multimodal strategies with transfer learning can significantly reduce the gap in performance for high-resource and low-resource languages. The dataset and methodology provide zero foundation for imitating Urdu NLP held in future to be engaged within research and practical applications in social media monitoring and customer feedback.Item The Victimization of Women in Patriarchal Societies: a Comparative Case Study of Circe and Beyond the Fields(Library Information Services, COMSATS University Islamabad, Lahore Campus, 2024) Ayesha Amat ur Rasool; CIIT/SP23-REL-009/LHR; Dr. Saima Akhtar; LHR TP 9562Patriarchal societal structures assume that men hold more power than women. In such systems, male power influences every aspect of life, from social dynamics to political decisions, legal frameworks, and moral considerations. Patriarchal cultures are often seen as mechanisms for exploiting women, contributing to issues such as violence, sexual harassment, and victimization. This study seeks to enhance understanding of the parallels and distinctions in women’s victimization across various patriarchal societies, irrespective of cultural origins. Through a comparative case study, it explores common and culturally specific experiences of oppression and victimization, significantly contributing to existing knowledge of patriarchal systems. The research examines the victimization of Circe, the protagonist in Madeline Miller's Circe, and Zara, the protagonist in Aysha Baqir's Beyond the Fields, using Feminist Critical Discourse Analysis (FCDA) by Lazar and Wodak’s Discourse Historical Approach (DHA). It highlights the shared experiences of patriarchal oppression faced by these characters. Despite the vastly different cultural contexts one rooted in the mythological and divine world of Ancient Greece, the other in the socio-cultural dynamics of contemporary South Asia, the victimization of women in both contexts reveals striking similarities. Both novels underscore the pervasive and enduring nature of patriarchal norms, including the objectification of women’s bodies, the normalization of sexual violence, and societal mechanisms that enforce conformity to patriarchal ideals of honor, purity, and submission.