SCHOOL OF GRADUATE STUDIES DEVELOPING AUTOMATIC CONSTITUENCY PARSER FOR SILTIGNA LANGUAGE USING DEEP LEARNING APPROACH

Abstract

In our study, we focused on developing automatic constituency parsing for the Silting language using deep learning approaches. The Siltigna language is experiencing increased speaker numbers, and our goal was to address the language's issues and enhance its content globally. To achieve this, we employed a deep learning technique known as the transition method and the main architecture we used was a seq-to-seq auto encoder-decoder model. This model has been widely used in natural language processing tasks. We conducted experiments using various deep learning models, including LSTM, BiLSTM, LSTM with attention, GRU, and transformer models. To train and evaluate these models, collected a dataset of approximately 2000 sentences and labeled them with corresponding parse trees. Before parsing the sentences into sequences, we applied preprocessing techniques such as data cleaning and tokenization and split the dataset into training and testing sets using an 80-20 split. Subsequently, we trained and tested theLSTM, Bi-LSTM, LSTM with attention, GRU, and Transformer models on the labeled parse tree data. Among these models, the transformer model achieved the best performance with 84.38% accuracy, 0.137 losses, and LAS of 0.83. This indicates that the transformer model was most effective in accurately parsing the Silting language. Our study highlights the importance of natural language processing with interconnected global community. By developing automatic constituency parsing for the Siltigna language, we aimed to bridge language barriers and enable effective communication across borders.