# Design and Development of Rule-based open-domain Question-Answering System on SQuAD v2.0 Dataset

Source: [Design and Development of Rule-based open-domain Question-Answering System on SQuAD v2.0 Dataset](<https://www.sota2.com/research/paper/68fa6c541e209131cea334f6>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-26T00:07:48.255Z

Authors: Pragya Katyayan, Nisheeth Joshi

Published: 2022-03-27T07:51:18.000Z

## Abstract

Human mind is the palace of curious questions that seek answers. Computational resolution of this challenge is possible through Natural Language Processing techniques. Statistical techniques like machine learning and deep learning require a lot of data to train and despite that they fail to tap into the nuances of language. Such systems usually perform best on close-domain datasets. We have proposed development of a rule-based open-domain question-answering system which is capable of answering questions of any domain from a corresponding context passage. We have used 1000 questions from SQuAD 2.0 dataset for testing the developed system and it gives satisfactory results. In this paper, we have described the structure of the developed system and have analyzed the performance.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2204.09659>)

[PDF](<https://arxiv.org/pdf/2204.09659.pdf>)