The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Analysis of TF-IDF Model and its Variant for Document Retrieval
Abstract
An Information Retrieval System is a system that is capable of storage, retrieval, and maintenance of an Information. In this context Information can be composed of text (including numeric and date data), images, audio, video and other multi-media objects. The TF-IDF weight is a statistical measure used to evaluate how important a word is to a document in a collection or corpus. There exist various models for weighting terms of corpus documents and query terms. This work is carried out to analyze and evaluate the retrieval effectiveness of vector -- space model while using the new data set of FIRE 2011. The experiments were performed with TF-IDF and its variants. For all experiments and evaluation the open search engine, Terrier 3.5 was used. Our result shows that TF-IDF model gives the highest precision values with the new corpus dataset.
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“The selected analyser in the framework performs the tasks of tokenisation and normalisation of a text corpus along with stemming and lemmatization.33,34”
From Automated Knowledge Organization AI ML based Subject Indexing System for Libraries (2023) · cites this paper as [34]
Citations
Cites
Introduction to Information Retrieval
Manning, C. D., Raghavan, P., & Schütze, H. (2008) · doi:10.1017/cbo9780511809071 · Cited by 11,288
Research works printed but not linked: 6
- ho chung, 2008, Interpreting tf-idf term weights as making relevance decisions, ACM Transactions on Information Systems (TOIS), 26, 13
- juan, 2003, Using tf-idf to determine word relevance in document queries, Proceedings of the First Instructional Conference on Machine Learning
- djoerd, 2000, A probabilistic justification for using tf-idf term weighting in information retrieval, International Journal on Digital Libraries, 3, 131, 10.1007/s007999900025 · DOI
- 10.1137/1037127 · DOI
- 10.1016/0306-4573(88)90021-0 · DOI
- akiko, 2003, An information-theoretic perspective of tf -idf measures, Information Processing & Management, 39, 45, 10.1016/S0306-4573(02)00021-3 · DOI · Paper page
Cited by
Automated Knowledge Organization AI ML based Subject Indexing System for Libraries
Ahmed, M., Mukhopadhyay, M., & Mukhopadhyay, P. (2023) · DESIDOC Journal of Library & Information Technology · doi:10.14429/djlit.43.01.18619 · Cited by 20
Metadata
- Authors
- Mishra, A., & Vishwakarma, S.
- ORCID
- Vishwakarma, Santosh
- Venue
- 2015 International Conference on Computational Intelligence and Communication Networks (CICN)
- Publisher
- IEEE
- Published
- 2015-12
- Pages
- 772-776
- OpenAlex
- W2513519930
- Cited by
- 65
- References
- 1
- Citations by year
- 2026: 7 · 2025: 10 · 2024: 5 · 2023: 8 · 2022: 7 · 2021: 5 · 2020: 7 · 2019: 8 · 2018: 4 · 2017: 3 · 2016: 1
- FWCI
- 2.32
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- International Conference on Computational Intelligence and Communication Networks (CICN) (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Information Systems › Information Retrieval and Search Behavior
- Physical Sciences › Mathematics › Theoretical Computer Science › Algorithms and Data Compression
- Physical Sciences › Computer Science › Artificial Intelligence › Advanced Text Analysis Techniques
- Keywords
- TF-IDF, term weighting, vector space model, document retrieval, retrieval effectiveness, precision evaluation
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
