Abstract
Information retrieval is a set of methods to provide relevant documents based on the input query. Various steps included in Information retrieval are starting from pre-processing, indexing, and then ranking. The explanation using examples are given in the chapter. The applications of Information retrieval are document summarization, question answer system and many more.
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References
https://www.internetlivestats.com/total-number-of-websites/.
Porter, M.F. 1980. An algorithm for suffix stripping. Program 14: 130–137.
Sonawane, S.S., and P.A. Kulkarni. 2016. Context-based co-reference resolution for text document using graph model (cont-graph). International Journal of Knowledge Engineering and Data Mining 4 (1): 1–17.
Elango, P. 2005. Coreference resolution: A survey. Madison, WI: University of Wisconsin.
Jiang, Ridong, Rafael E. Banchs, and Haizhou Li. 2016. Evaluating and combining name entity recognition systems. In: Proceedings of the sixth named entity workshop.
Nadeau, David, and Satoshi Sekine. 2007. A survey of named entity recognition and classification. Lingvisticae Investigationes 30 (1): 3–26.
Liu, B. 2007. Web data mining: Exploring hyperlinks, contents, and usage data. New York: Springer.
Manning, C., P. Raghavan, and H. Schütze. 2008. Introduction to information retrieval. Cambridge: Cambridge University Press.
Sonawane, S.S. 2014. Graph based information retrieval. IJACKD Journal of Research 3 (1).
Sonawane, Sheetal S., and Parag Kulkarni. 2019. Concept based document similarity using graph model. International Journal of Information Technology 2019: 1–12.
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Sonawane, S.S., Mahalle, P.N., Ghotkar, A.S. (2022). Information Retrieval. In: Information Retrieval and Natural Language Processing. Studies in Big Data, vol 104. Springer, Singapore. https://doi.org/10.1007/978-981-16-9995-5_4
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DOI: https://doi.org/10.1007/978-981-16-9995-5_4
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