M.Phil / MS

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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
Cross Domain Sentiment Classification for Urdu Language
(Library Information Services, COMSATS University Islamabad, Lahore Campus, 2020) Sana Hassan; FA18-RCS-010; LHR TP 6410; Dr. Muhammad Waqas Anwar
An enormous amount of information is produced daily on the internet about different prod ucts and objects. People like to express their feelings, thoughts in their native language on different social sites. This bulk data needs to be interpreted. So, Sentiment Analysis (SA) is required which extracts people’s opinions, feelings, and thoughts. However, it is a highly domain-dependent task. Due to this, the issue of domain-transfer arises. If a classifier is tested with any different domain, other than on which it is trained, its performance is affected. Many tasks and frameworks are created in mostly English and western languages. Tasks intended for the English language cannot be applied for other languages, hence there is a need to work on different dialects. In this research, we performed a cross-domain sentiment Analysis on data set of Urdu language comprising of 9000 sentences from sports tweets in which there are two domains (Hockey and Cricket). Furthermore, preparing corpus for specific domains in the Urdu language we applied ma chine learning and deep learning approach. After this, we evaluated results using standard evaluation measures and a confusion matrix, Gated Recurrent Units (GRU) gives the highest accuracy of (77%)
Neural Architecture Based Urdu Named Entity Recognition
(Library Information Services COMSATS University Islamaabad Lahore Campus, 2020) Muhammad Ehsan; FA18-RCS-018; LHR TP 6413; Dr. Muhammad Waqas Anwar
In natural language processing, named entity recognition (NER) is the issue of figuring out and arrange named elements referenced in unstructured content into pre-characterized classifications. To compare, evaluate and develop a NER system, we need standard and high-quality benchmark resources. But unfortunately, corpora in a huge amount are available for many other languages but not for the Urdu language. Even though Urdu is a rich language in all aspects and has more than 300 million speakers all over the world. As state-of-the-art sequence labeling systems are heavily based on large amounts of domain specific knowledge and hand-crafted feature for efficient learning from small data. When the dataset is in a large amount and high quality then the Named Entity Recognition (NER) task can be solved in a better way and provides better results on a large amount of labeled data, but the availability of labeled data in most of the languages is limited for training purpose. One way is there are scarcely any limitations on identifiable words. Hence, concluded the little amount of exampled data is troublesome. Subsequently, deliberately built orthographic features and language-explicit information assets (e.g., gazetteers) are broadly utilized for understanding this undertaking. Tragically, for new languages and new domains, the task of identifying explicit language sources and feature extraction is mainly an expensive process. As a result, it makes efficient NER system development adaptation a challenge. Another way to makes an efficient NER system is the use of deep neural network models that have achieved highest performance in the domain of natural language processing (NLP). However, this performance highly depends on the availability of huge amount of annotated data. The other way to get a better generalization from a small amount of data is to deploy semi-supervised models that performs on small set of annotated examples and a large set of unannotated data. Due to limited annotated data for many Natural Language Processing (NLP) problems like NER for the Urdu language, we will solve the NER problem using deep neural algorithms. In deep learning methods, we perform 44 experiments by using recurrent neural network (RNN), long short-term memory (LSTM), Bidirectional LSTM, gated recurrent unit (GRU), Bidirectional GRU and GRUCell in a supervised learning setting to compare with ix the previous models. Our model outperformed the previous results with F1-score of 91.43%. We also use multi-layer perceptron in a semi-supervised setting. This model gives the results with F1-score of 51.00%. We use standard evaluation measures to check the performance of the NER system. However, deep learning results with semi-supervised settings unable to beat the results of deep learning results with supervised settings.

M.Phil / MS

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