Natural Language Processing (NLP), a facet of Artificial Intelligence (AI), is pivotal in deciphering diverse forms of human-generated data, encompassing text, image, video, and audio. Functioning as a bridge between computers and human language, NLP serves as a tool for real-time data understanding and analysis. Its applications span machine translation, information extraction, text summarization, and question answering, with the latter being a extensively researched challenge within the NLP domain.
Question Answering (QA) systems, akin to information retrieval systems, rely on NLP methods to process user queries and extract relevant answers from a given dataset. Neural networks, integral to training QA systems, are algorithmic models designed to emulate the intricacies of the human brain, discerning relationships within datasets. TensorFlow (TF), a powerful framework, facilitates efficient neural network training by automatically computing gradients through a graph-based approach. Leveraging large datasets, our system utilizes TF to discern the similarity between questions and answers in order to respond to factoid questions embedded in paragraphs.
To bolster the credibility of the retrieved answers, our system employs reasoning—a logical data analysis process crucial for making informed decisions. Within QA systems, reasoning emerges as a vital component, significantly contributing to answer extraction accuracy. This research underscores the synergy of neural networks, TensorFlow, and reasoning in enhancing the efficacy of factoid question answering over paragraphs.
