Enhancing Question Answering Systems using Textual Entailment A Comprehensive Study and Performance Evaluation

Abstract

Question answering comprises a subdomain within the discipline of natural language processing, focusing newlineon the automated generation of responses to queries stated in natural language. Commonly, question newlineanswering systems use strategies that include information retrieval, recognizing the complexity of natural newlinelanguage, and utilizing machine learning. These approaches work together to find the critical information newlineinside a particular document or knowledge repository, obtaining the necessary response. newlineTwo basic types of question answering systems evolve: community question answering and noncommunity newlinequestion answering systems. Community question answering systems revolve around queries newlinebeing handled by a collective of users, frequently congregating on platforms such as question and answer newlineforums. In contrast to non-community question answering systems, these platforms are often more newlineexhaustive and informative. This arises from their capacity to draw into the aggregate knowledge and newlinecapabilities of the user community, rendering them highly informative. Moreover, the real-time nature newlineof these systems enables for immediate user-generated responses. However, managing such systems can newlinebe more difficult, demanding a sizable and dedicated user population. Additionally, they are more prone newlineto biases, as the viewpoints of community members could impact responses. newlineConversely, non-community question answering systems comprise requests being handled by automated newlineentities like search engines or question answering software. These systems frequently display newlinesuperior scalability compared to their community counterparts, adeptly managing a larger amount of newlineinquiries continuously. Their advantage stays in their impartiality, as responses remain untouched by newlinecommunity sentiments. Nonetheless, they may deliver significantly less thorough and informative solutions newlinewhen compared with community question answering systems. This is primarily due to the absence newlineof the diversity of knowledge and experience necessarily present within a user