On Relevance, Probabilistic Indexing and Information Retrieval

By Melvin Earl Maron, J. L. Kuhns. 1960

Subfields: Information Retrieval & Search

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Citations in the corpus

Scrutinizing an algorithmic technique: the Bayes classifier as interested reading of reality (2017, methodology): “This method, named probabilistic indexing , made it possible, through inverse statistical inference via Bayes ’ theorem, to provide an automatically ranked result list for a subject query instead of merely an unordered set of documents (Maron & Kuhns, 1960).”
Query Expansion Techniques for Information Retrieval: a Survey (2017, unknown): “[179] as a technique for literature indexing and searching in a mechanized library system.”
Bandit Algorithms (2020, methodology): “The probabilistic ranking principle was put forward by Maron and Kuhns [1960]. The paper by Robertson [1977] identifies some sufficient conditions under which the principle is valid and also discusses its limitations.”
Naive (Bayes) at Forty: The Independence Assumption in Information Retrieval (1998, background): “Machine learning researchers tend to be aware of the large pattern recognition literature on naive Bayes, but may be less aware of an equally large information retrieval (IR) literature dating back almost forty years [37,38].”
The Probabilistic Relevance Framework: BM25 and Beyond (2009, methodology): “A few of the main references are as follows: [30, 44, 46, 50, 52, 53, 58]; other surveys of a range of probabilistic approaches include [14, 17].”
A survey on the use of relevance feedback for information access systems (2003, methodology): “In the probabilistic model, suggested by Maron and Kuhns (1960), and developed by, amongst others, Robertson and Sparck Jones (1976) and Van Rijsbergen (1979), documents and queries are also viewed as vectors but the vector space similarity measure is replaced by a probabilistic matching function.”
Dense Text Retrieval Based on Pretrained Language Models: A Survey (2022, background): “Unlike sparse retrievers (e.g., BM25), dense retrievers no longer rely on exact term matching for text retrieval, but instead learn semantic matching for capturing the text relevance.”
Text as Data: The Promise and Pitfalls of Automatic Content Analysis Methods for Political Texts (2013, methodology): “Individual Methods: To provide a sense of how individual classification algorithms work, we present in detail a canonical example—the Naı̈ve Bayes classifier (Maron and Kuhns 1960).”
Understanding and improving automated collaborative filtering systems (2000, background): “Indexing is the process of examining content collections and creating software-searchable data structures that contain descriptions of the items available[13,14,24,25,41,45,52,57,71].”
A Survey of Automatic Query Expansion in Information Retrieval (2012, background): “The most critical language issue for retrieval effectiveness is the term mismatch problem: the indexers and the users do often not use the same words.”