Browsing by Author "Waqar Ashiq"

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    Urdu Word Sense Disambiguation using Siamese Neural Networks
    (Library Information Services, COMSATS University Islamabad, Lahore Campus, 2020) Waqar Ashiq; SP19-RCS-013; LHR TP 6419; Dr. Rao Muhammad Adeel Nawab
    The process of Word Sense Disambiguation (WSD) is to recognize the exact sense of the targeted polysemous terms in the given context. Word sense ambiguities exist in all human languages, and humans can easily solve this ambiguity but it’s a tough task for machines to solve automatically. WSD is a significant long-established task in the area of natural language processing (NLP) with a remarkable contribution in numerous applications. The task of WSD has been categorized into (1) Lexical Sample, and (2) All-Words WSD. In literature, the problem of Urdu WSD has been mainly addressed using feature-based machine learning methods (based on manual feature engineering). A major limitation of manual feature engineering is that it requires a lot of time and endeavors to develop machine learning methods in Urdu WSD. To overcome the limitations of manual feature engineering, deep learning approaches are proposed, and they have produced promising results for various NLP tasks. To fulfill the gap in WSD research, we developed and apply state-of-the-art Siamese Neural Network (SNN) based approaches for both tasks for the Urdu Language. We developed the SNN based approaches for both WSD tasks using six deep learning models (CNN, RNN, LSTM, BiLSTM, GRU, and BiGRU) with the combination of three distance measure functions including Euclidean distance, Manhattan distance, and Cosine similarity. These Siamese network features of all deep learning models are combined for WSD classification using eight machine learning classifiers with two well-known feature selection methods (RFECV and Chi-Square). Additionally, for the All-words task SNN features are combined with n-gram (unigram and bigram) features. The developed SNN based approaches are evaluated on well-known benchmark corpora which include (1) UAW-WSD-18 Corpus for All-words WSD and (2) ULS WSD-18-Corpus for Lexical sample WSD task. After extensive experimentation, the proposed approach produced state-of-the-art results (Accuracy = 64%) in All-words WSD and outperform the baseline results (Accuracy = 57.71%). The results of Lexical sample WSD are not improved because of very few instances of each ambiguous word in corpora for deep learning approaches.