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Table of Contents
ABSTRACT
INTRODUCTION
PROBLEM DEFINITION
NLP BACKGROUND
TRAINING MODELS
EVALUATING ACCURACY OF ANSWERS
Summary
In recent years, neural networks have shown impressive performance gains on long-standing AI problems, particularly in answering queries from natural language text. This paper presents NeuralDB, a database system without a pre-defined schema, allowing updates and queries in natural language. The system leverages NLP techniques and transformer models to process queries. The architecture includes Neural SPJ operators for query processing and aggregation. Experimental validation shows high accuracy in answering queries over thousands of sentences. Training the neural network with examples is crucial for performance. Evaluation metrics like Exact Match and F1 score are used to measure correctness of answers. NeuralDB offers benefits in handling unstructured data and diverse linguistic queries, suitable for applications like personal assistants and political claim modeling.