V.Durga Mahesh, P.Durga Shashinadh, B.Esha Tanmai, K. Eshwar Sai Pranay, Fardeena Thoufiya, SD. Fouziya Thabassum
The aim of this project is to create a robust and accurate system that can automatically categorize conversations into predefined domains. First, a diverse dataset of textual conversations spanning different domains will be collected and preprocessed. This preprocessing phase will involve tasks such as text cleaning, tokenization, and the removal of stop words to ensure high-quality input data. Next, relevant features will be extracted from the preprocessed conversations. These features may include ngrams, word embeddings, and syntactic features. The heart of the project lies in the selection and training of machine learning models. Various algorithms, including Support Vector Machines (SVM), Random Forests, and Recurrent Neural Networks (RNN), will be considered and compared in terms of their classification accuracy, precision, recall, and F1-score. The aim is to identify the most suitable model or combination of models for accurate domain classification. Throughout the project, challenges such as data imbalance, domain ambiguity, and scalability will be addressed. Strategies such as data augmentation, ensemble methods, and transfer learning will be explored to enhance the system's ability to handle these challenges effectively.
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Unique Paper ID: 162036

Publication Volume & Issue: Volume 10, Issue 7

Page(s): 273 - 277
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