Natural language question-answering systems: 1969

Author:

Simmons Robert F.1

Affiliation:

1. Univ. of Texas at Austin, Austin

Abstract

Recent experiments in programming natural language question-answering systems are reviewed to summarize the methods that have been developed for syntactic, semantic, and logical analysis of English strings. It is concluded that at least minimally effective techniques have been devised for answering questions from natural language subsets in small scale experimental systems and that a useful paradigm has evolved to guide research efforts in the field. Current approaches to semantic analysis and logical inference are seen to be effective beginnings but of questionable generality with respect either to subtle aspects of meaning or to applications over large subsets of English. Generalizing from current small-scale experiments to language-processing systems based on dictionaries with thousands of entries—with correspondingly large grammars and semantic systems—may entail a new order of complexity and require the invention and development of entirely different approaches to semantic analysis and question answering.

Publisher

Association for Computing Machinery (ACM)

Subject

General Computer Science

Cited by 94 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. The power and potentials of Flexible Query Answering Systems: A critical and comprehensive analysis;Data & Knowledge Engineering;2024-01

2. Research on Question Answering over Knowledge Graph of Chronic Diseases;2022 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT);2022-11

3. The Influence of AI-Assisted Learning on CAL;Computer-Assisted Learning for Engaging Varying Aptitudes;2022-09-30

4. A User Study on Clarifying Comparative Questions;ACM SIGIR Conference on Human Information Interaction and Retrieval;2022-03-14

5. Controllable Generation from Pre-trained Language Models via Inverse Prompting;Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining;2021-08-14

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3