Scott Deerwester
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Scott Craig Deerwester is an American computer scientist and computer engineer who created the mathematical and natural language processing (NLP) technique known as Latent Semantic Analysis (LSA).[1][2]
Early life
[edit]Deerwester was born in Rossville, Indiana, United States. He is the son of Kenneth F. Deerwester (July 8, 1927 – March 3, 2013) and Donna Stone.[citation needed]
Scientific career
[edit]Deerwester began his academic career in the United States, contributing to the development of LSA during his tenure at Colgate University and the University of Chicago.[3] Deerwester published his first research paper, The Retrieval Expert Model of Information Retrieval, at Purdue University in 1984.[4]
Publications and research work
[edit]Deerwester co-authored a research paper on LSA in 1988.[5] This paper helped improve how information retrieval systems process textual information by finding latent associations between keywords in documents, even when they lack common words. This method aimed to address issues related to polysemy (words with multiple meanings) and synonymy (different words with similar meanings).[6]
According to Deerwester's seminal 1988 work, Latent Semantic Analysis (LSA) enabled search engines to retrieve relevant documents even when they did not contain the exact keywords, which led to a more user-friendly and contextual retrieval mechanism.[1] His work impacted the advancement of subsequent technologies, such as Latent Dirichlet Allocation (LDA), probabilistic models, and its uses in topic modeling and semantic similarity in texts.[2]
LSA is a technique for natural language processing applications, ranging from chatbots to automatic translation services, and has the ability to emulate some human traits such as word sorting and category assessment.[7] Deerwester's work has found applications in data mining, recommender systems, and business intelligence tools.[2]
References
[edit]- ^ a b Deerwester, Scott; Dumais, Susan T.; Furnas, George W.; Landauer, Thomas K.; Harshman, Richard (September 1990). "Indexing by latent semantic analysis". Journal of the American Society for Information Science. 41 (6): 391–407. doi:10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9. ISSN 0002-8231.
- ^ a b c Dumais, S. T.; Furnas, G. W.; Landauer, T. K.; Deerwester, S.; Harshman, R. (1988-05-01). "Using latent semantic analysis to improve access to textual information". Proceedings of the SIGCHI conference on Human factors in computing systems – CHI '88. New York, NY, USA: Association for Computing Machinery. pp. 281–285. doi:10.1145/57167.57214. ISBN 978-0-201-14237-2.
- ^ Scott, Deerwester. "Scott Deerwester | LinkedIn". LinkedIn.
- ^ Deerwester, Scott (1984). "The retrieval expert model of information retrieval". Google Scholar. Retrieved 18 October 2024.
- ^ Dumais, S. T.; Furnas, G. W.; Landauer, T. K.; Deerwester, S.; Harshman, R. (1988). "Using latent semantic analysis to improve access to textual information". Proceedings of the SIGCHI conference on Human factors in computing systems - CHI '88. Washington, D.C., United States: ACM Press. pp. 281–285. doi:10.1145/57167.57214. ISBN 978-0-201-14237-2.
- ^ Hurtado, Jose L.; Agarwal, Ankur; Zhu, Xingquan (14 April 2016). "Topic discovery and future trend forecasting for texts". Journal of Big Data. 3. doi:10.1186/s40537-016-0039-2.
- ^ Foltz, Peter W. (1996-06-01). "Latent semantic analysis for text-based research". Behavior Research Methods, Instruments, & Computers. 28 (2): 197–202. doi:10.3758/BF03204765. ISSN 1532-5970.