On the construction of effective vocabularies for information retrieval

Author:

Salton G.1,Yu Clement T.1

Affiliation:

1. Cornell University, Ithaca, New York

Abstract

Natural language query formulations exhibit advantages over artificial language statements since they permit the user to approach the retrieval environment without prior training and without using intermediaries. To obtain adequate retrieval output, it is however necessary to emphasize the good terms and to deemphasize the bad ones. The usefulness of the terms in a natural language vocabulary is first characterized in terms of their frequency distribution over the documents of a collection. The construction of "good" natural language vocabularies is then described, and methods are given for improving the vocabulary by transforming terms that operate poorly for retrieval purposes into better ones.

Publisher

Association for Computing Machinery (ACM)

Subject

Hardware and Architecture,Management Information Systems

Cited by 13 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. ANDetect: A Third-party Ad Network Libraries Detection Framework for Android Applications;Annual Computer Security Applications Conference;2023-12-04

2. Comprehensive Evaluation of Tourist Attractions Based on Text Reviews Using Topic Modeling and Sentiment Analysis;Proceedings of the 2023 3rd Guangdong-Hong Kong-Macao Greater Bay Area Artificial Intelligence and Big Data Forum;2023-09-22

3. A Machine-Learning Based Approach to Validating Learning Materials;Lecture Notes in Networks and Systems;2023

4. Second Language Teaching with a Focus on Different Learner Cultures for Sustainable Learner Development: The Case of Sino-Korean Vocabulary;Sustainability;2022-06-30

5. A method based on SVD-PCA microblog hot event prediction;International Conference on Signal Processing and Communication Technology (SPCT 2021);2022-04-14

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