Jul 2026· International Journal for Research in Applied Science and Engineering Technology· Vol 14, pp. 946-953· 0 citations
TL;DR
The goal of a question answering system is to provide a precise response in natural language to the user's question, and the closed domain question answering system provides more precise and accurate responses.
Abstract
Users can use search engines such as Google and Yahoo! to search for documents on the World Wide Web. It takes
time, but the user of a search engine must go through each document to find an answer that is relevant to the question. The
Query Answering (QA) method reduces the amount of time spent searching for the exact answer to a question. The study of
question-answering systems is an important aspect of the field of information retrieval. The year 1960 saw the start of research
into question-answering systems, and since then, a plethora of different question-answering systems have been developed. The
Question Answering system combines research from several fields, including Natural Language Processing, Artificial
Intelligence, Information Retrieval, and Information Extraction. The goal of a question answering system is to provide a precise
response in natural language to the user's question. The availability of various resources for responses is used to differentiate
between different types of question answering systems. In comparison to the open domain question answering system, the closed
domain question answering system provides more precise and accurate responses.
This study provides an AI- Based document analyzer with a question-answer system that makes use of Natural Language Processing approaches that is affordable, scalable, and suitable for business, education, and research.
Radhika Sharma, Devraj Gautam· Revolutionary Advances in Co...· 0 citations
Past work in the fields of NLG and UM is looked at, outlining what was identified as important for graceful human machine interactions, and future research directions that I believe are important are discussed.
Cécile Paris· Annual International ACM SIG...· 0 citations
This review aims to systematically sort out the technical framework of automatic question answering system, analyze its performance bottlenecks, and explore innovative solutions based on large language model and multimodal fusion.
Xuxin Peng· Proceedings of the 3rd Inter...· 2 citations
Web tables are an important source of structured data for question-answering systems; however, retrieval methods differ substantially in their retrieval units, structure modeling strategies, and mechanisms for matching queries with table representations. The purpose of this article is to systematize methodological directions in structure-aware retrieval of web table fragments and to identify limitations that motivate the use of multivector and late-interaction retrieval schemes. The study is conducted as a scoping review. The search was performed in Scopus, OpenAlex, eLIBRARY/RSCI, and through citation analysis; from 767 identified records, 42 studies were included in the final synthesis after duplicate removal, screening, and full-text assessment. The article proposes a unified formal framework for comparing retrieval methods, develops a taxonomy of approaches, and analyzes retrieval granularity, vector interaction types, relevance aggregation mechanisms, structural signals, and computational limitations. The results show that dense and structure-aware whole-table methods dominate, whereas scalable retrieval of rows, cells, subtables, and multi-table evidence remains insufficiently developed. The article concludes that further research should focus on multivector methods that preserve local table structure while maintaining acceptable computational complexity.
Ali Deeb Khaled, Yuriy E. Gapanyuk, Gennady I. Afanasyev· Computational nanotechnology· 0 citations
The results show that English retrieval is already strong under BM25, whereas translated isiZulu queries benefit more clearly from multilingual semantic retrieval, especially at Top-10, and suggest that multilingual semantic retrieval can partly reduce translated-query barriers in open repository access.
Ya-Min Lu, Zhi-Qiang Huang, Tang Cheng· Information Development· 0 citations
This work hypothesizes that using models trained only on generic question answering data (e.g. SQuAD) is a good starting point for domain specific entity extraction, and explores whether the addition of small amounts of training data can help lift model performance.
Corey A. Harper, R. Daniel, Paul Groth· 0 citations
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