question-answering-systems

Designing and developing question answering systems requires a diverse skill set and the use of specialized tools. Here are the key skills and tools needed: Skills Required: Natural Language Processing (NLP): Understanding of how machines can comprehend and generate human language is essential. Machine Learning: Knowledge of ML algorithms and techniques is crucial for training models to understand and answer questions. Software Development: Proficiency in programming languages like Python and Java is necessary for implementing the system. Domain Knowledge: Understanding the specific domain in which the question answering system will operate is important for accurate answers. Tools Used: Python: A versatile programming language commonly used for NLP and ML tasks. TensorFlow: An open-source ML framework that enables the development and training of neural networks. BERT (Bidirectional Encoder Representations from Transformers): A pre-trained NLP model widely used for QA tasks due to its effectiveness. SpaCy: A popular NLP library for tasks like tokenization, parsing, and named entity recognition.