Automatic Indexing: An Experimental Inquiry
Open accessAbstract
This inquiry examines a technique for automatically classifying (indexing) documents according to their subject content.The task, in essence, is to have a computing machine read a document and on the basis of the occurrence of selected clue words decide to which of many subject categories the document in question belongs.This paper describes the design, execution and evaluation of a modest experimental study aimed at testing empirically one statistical technique for automatic indexing.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The 1950s witnessed the first applications of the Naïve Bayes classifier in pattern recognition and information retrieval.
“The 1950s also witnessed the first applications of the Naïve Bayes (NB) classifier in the fields of pattern recognition [97] and information retrieval [297].”
From A comprehensive survey on machine learning for networking: Evolution, applications and research opportunities (2018) · cites this paper as [297]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
On Relevance, Probabilistic Indexing and Information Retrieval
Maron, M. E., & Kuhns, J. L. (1960) · Journal of the ACM · doi:10.1145/321033.321035 · Cited by 908 · see citing works
Self-citation
Flagged first, then by citations
Cited by
Sorted by citations
Machine learning in automated text categorization
Sebastiani, F. (2002) · ACM Computing Surveys · doi:10.1145/505282.505283 · Cited by 7,927 · see citing works
Naive (Bayes) at forty: The independence assumption in information retrieval
Lewis, D. D. (1998) · Lecture notes in computer science · doi:10.1007/bfb0026666 · Cited by 2,140 · see citing works
“Its use in the form of Equation 11 was promoted by Robertson and Sparck Jones in a paper [41] that did much to clarify and unify a number of related and partially ad hoc applications of naive Bayes dating back to Maron [37].”
BackgroundMethodA sequential algorithm for training text classifiersPreprint
Lewis, D., & Gale, W. A. (1994) · arXiv (Cornell University) · doi:10.5555/188490.188495 · Cited by 1,838 · see citing works
Content-Based Recommendation Systems
Pazzani, M. J., & Billsus, D. (2007) · Lecture Notes in Computer Science · doi:10.1007/978-3-540-72079-9_10 · Cited by 2,600 · see citing works
“Early work on a probabilistic classifier and its text classification performance was reported by Maron [24].”
BackgroundThe text mining handbook: advanced approaches in analyzing unstructured data
(2007) · Choice Reviews Online · doi:10.5860/choice.44-5684 · Cited by 1,599 · see citing works
Learning and Revising User Profiles: The Identification of Interesting Web Sites
Pazzani, M. J., & Billsus, D. (1997) · Machine Learning · doi:10.1023/a:1007369909943 · Cited by 1,208 · see citing works
“The Bayesian classifier has a long history in text categorization tasks (e.g., Maron, 1961; Lewis 1992).”
BackgroundA Sequential Algorithm for Training Text Classifiers
Lewis, D. D., & Gale, W. A. (1994) · SIGIR ’94 · doi:10.1007/978-1-4471-2099-5_1 · Cited by 1,208 · see citing works
“have been applied to a variety of text classification tasks, including text retrieval [18], text categorization [19, 20], and word sense identification [5].”
BackgroundBoutaba, R., Salahuddin, M. A., Limam, N., Ayoubi, S., Shahriar, N., Estrada-Solano, F., & Caicedo, O. M. (2018) · Journal of Internet Services and Applications · doi:10.1186/s13174-018-0087-2 · Cited by 1,036 · see citing works
“The 1950s also witnessed the first applications of the Naïve Bayes (NB) classifier in the fields of pattern recognition [97] and information retrieval [297].”
BackgroundLarge-Scale Bayesian Logistic Regression for Text Categorization
Genkin, A., Lewis, D. D., & Madigan, D. (2007) · Technometrics · doi:10.1198/004017007000000245 · Cited by 812 · see citing works
“Examples include naive Bayes (Maron 1961; Lewis 1998) and the Rocchio algorithm (Rocchio 1971).”
BackgroundA review of unsupervised feature selection methods
Solorio-Fernández, S., Carrasco-Ochoa, J. A., & Martínez-Trinidad, J. F. (2019) · Artificial Intelligence Review · doi:10.1007/s10462-019-09682-y · Cited by 658 · see citing works
An evaluation of phrasal and clustered representations on a text categorization task
Lewis, D. D. (1992) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/133160.133172 · Cited by 544 · see citing works
A Survey on Text Classification: From Traditional to Deep Learning
Li, Q., Peng, H. M., Li, J., Xia, C., Yang, R., Sun, L., Yu, P. S., & He, L. (2022) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/3495162 · Cited by 695 · see citing works
“For traditional models, NB [8] is the first model used for the text classification task.”
Method“Fake News” Is Not Simply False Information: A Concept Explication and Taxonomy of Online Content
Molina, M. D., Sundar, S. S., Le, T., & Lee, D. (2021) · American Behavioral Scientist · doi:10.1177/0002764219878224 · Cited by 485 · see citing works
“For example, the different features in our taxonomy can be built into sequences of yes/no questions (see e.g., in Table 9) conditioned on input features to build various supervised learning models such as Naïve Bayes, Linear Regression, or Support Vector Machines (Cortes & Vapnik, 1995; Maron, 1961).”
MethodCombining classifiers in text categorization
Larkey, L. S., & Croft, W. B. (1996) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/243199.243276 · Cited by 416 · see citing works
A theory of term importance in automatic text analysis
Salton, G., Yang, C., & Yu, C. (1975) · Journal of the American Society for Information Science · doi:10.1002/asi.4630260106 · Cited by 389 · see citing works
Shafiq, M., Tian, Z., Sun, Y., Du, X., & Guizani, M. (2020) · Future Generation Computer Systems · doi:10.1016/j.future.2020.02.017 · Cited by 384 · see citing works
The Application of Deep Learning in Cancer Prognosis Prediction
Zhu, W., Xie, L., Han, J., & Guo, X. (2020) · Cancers · doi:10.3390/cancers12030603 · Cited by 384 · see citing works
“Some popular supervised learning algorithms include linear and logistic regression [28], SVM [29], naive bayes [30], gradient boosting [31,32], classification trees, and random forest [33,34].”
MethodA Hybrid User Model for News Story Classification
Billsus, D., & Pazzani, M. J. (1999) · Courses and lectures · doi:10.1007/978-3-7091-2490-1_10 · Cited by 357 · see citing works
“We restrict the evidence used to the presence of words, similar to a naïve Bayes model proposed by Maron (1961).”
MethodMachine Learning for Information Extraction in Informal Domains
Freitag, D. (2000) · Machine Learning · doi:10.1023/a:1007601113994 · Cited by 346 · see citing works
“The idea of a Bayesian method for text classification is almost as old as the idea of automatic text classification itself (Maron, 1961).”
MethodAdapting Feature Selection Algorithms for the Classification of Chinese Texts
Liu, X., Wang, S., Lu, S., Yin, Z., Li, X., Yin, L., Tian, J., & Zheng, W. (2023) · Systems · doi:10.3390/systems11090483 · Cited by 336 · see citing works
Fernandes, M. P., Vieira, S. M., Leite, F., Palos, C., Finkelstein, S. N., & Sousa, J. M. C. (2019) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2019.101762 · Cited by 314 · see citing works
“Naïve Bayes (NB) classifiers [78] are a family of simple “probabil-istic classifiers” based on applying Bayes’ theorem with strong (naive) independence assumptions between the features.”
MethodInformation retrieval and text mining technologies for chemistry
Krallinger, M., Rabal, O., Lourenço, A., Oyarzabal, J., & Valencia, A. (2017) · Chemical Reviews · doi:10.1021/acs.chemrev.6b00851 · Cited by 281 · see citing works
Trotzek, M., Koitka, S., & Friedrich, C. M. (2018) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2018.2885515 · Cited by 299 · see citing works
“The origins of text classification tasks can be found in early research to automatically categorize documents based on statistical analysis of specific clue words in 1961 [29].”
BackgroundAutomatic essay grading using text categorization techniques
Larkey, L. S. (1998) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/290941.290965 · Cited by 277 · see citing works
“Fuhr [3] and Lewis [8] have explored improvements to Maron s model.”
BackgroundInfluentialA survey of the applications of text mining in financial domain
Kumar, B. S., & Ravi, V. (2016) · Knowledge-Based Systems · doi:10.1016/j.knosys.2016.10.003 · Cited by 266 · see citing works
“The beginning of text mining task i.e. document classification was carried out in 1960‟s (Maron, 1961).”
BackgroundDiscretization for naive-Bayes learning: managing discretization bias and variance
Yang, Y., & Webb, G. I. (2008) · Machine Learning · doi:10.1007/s10994-008-5083-5 · Cited by 250 · see citing works
“They have long been a core technique in information retrieval (Maron and Kuhns, 1960; Maron, 1961; Lewis, 1992; Guthrie and Walker, 1994; Lewis and Gale, 1994; Kalt, 1996; Larkey and Croft, 1996; Pazzani et al., 1996; Starr et al., 1996a; Joachims, 1997; Koller and Sahami, 1997; Li and Yamanishi, 1997; Mitchell, 1997;…”
MethodEvaluating text categorization
Lewis, D. D. (1991) · DARPA Workshop on Speech and Natural Language (HLT) · doi:10.3115/112405.112471 · Cited by 232 · see citing works
A personal news agent that talks, learns and explains
Billsus, D., & Pazzani, M. J. (1999) · Adaptive Agents and Multi-Agents Systems · doi:10.1145/301136.301208 · Cited by 228 · see citing works
“We restrict the evidence used to the presence of words , similar to a naïve Bayes model proposed by Maron [8].”
MethodInformation extraction as a basis for high-precision text classification
Riloff, E., & Lehnert, W. G. (1994) · ACM Transactions on Information Systems · doi:10.1145/183422.183428 · Cited by 207 · see citing works
“For example, Maron [Maron, 1961] also classi ed texts by estimating the conditional probability of a category.”
Do Switches Dream of Machine Learning?
Xiong, Z., & Zilberman, N. (2019) · ACM Workshop on Hot Topics in Networks (HotNets) · doi:10.1145/3365609.3365864 · Cited by 198 · see citing works
“Our exploration of the supervised Naïve Bayes classifier [30] assumes a Gaussian distribution of independent features [20].”
MethodHarter, S. P. (1975) · Journal of the American Society for Information Science · doi:10.1002/asi.4630260402 · Cited by 195 · see citing works
Automatic Document Classification
Borko, H., & Bernick, M. (1963) · Journal of the ACM · doi:10.1145/321160.321165 · Cited by 188 · see citing works
A systematic literature review of automatic Alzheimer’s disease detection from speech and language
Petti, U., Baker, S., & Korhonen, A. (2020) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocaa174 · Cited by 168 · see citing works
Modeling the Internet and the Web: Probabilistic Method and Algorithms
Baldi, P., Frasconi, P., & Smyth, P. (2003) · Wiley eBooks · Cited by 158 · see citing works
The Born supremacy: quantum advantage and training of an Ising Born machine
Coyle, B., Mills, D., Danos, V., & Kashefi, E. (2020) · npj Quantum Information · doi:10.1038/s41534-020-00288-9 · Cited by 155 · see citing works
Fields, J., Chovanec, K., & Madiraju, P. (2024) · IEEE Access · doi:10.1109/access.2024.3349952 · Cited by 165 · see citing works
“Only in the 1950’s and 1960’s did we begin to utilize computers to aid in the classification of text-based documents, which was a time-consuming and laborious manually task [9].”
MethodXi, Z., Hopkinson, C., Rood, S. B., & Peddle, D. R. (2020) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2020.08.001 · Cited by 146 · see citing works
A model of cluster searching based on classification
Croft, W. B. (1980) · Information Systems · doi:10.1016/0306-4379(80)90010-1 · Cited by 144 · see citing works
Probability models for information retrieval based on divergence from randomness
Amati, G. (2003) · Enlighten: Theses (The University of Glasgow) · Cited by 142 · see citing works
Machine Learning Models to Predict Childhood and Adolescent Obesity: A Review
Colmenarejo, G. (2020) · Nutrients · doi:10.3390/nu12082466 · Cited by 133 · see citing works
“…like Rosenblatt’s perceptron [20] (a basic, 1-layer artificial neural network to perform binary classification), Naïve Bayes [21], Decision Trees [21], and k-Nearest Neighbors date back to the 50s–60s of the 20th century, it was during the last decade of it when the field started to enter into…”
BackgroundMethodVougas, K. N., Sakellaropoulos, T., Kotsinas, A., Foukas, G.-R. P., Ntargaras, A., Koinis, F., Polyzos, A., Myrianthopoulos, V., Zhou, H., Narang, S., et al. (2019) · Pharmacology & Therapeutics · doi:10.1016/j.pharmthera.2019.107395 · Cited by 124 · see citing works
A Tutorial on Automated Text Categorisation
Sebastiani, F. (2000) · Cited by 119 · see citing works
Methods Used in Computer-Aided Diagnosis for Breast Cancer Detection Using Mammograms: A Review
Ramadan, S. Z. (2020) · Journal of Healthcare Engineering · doi:10.1155/2020/9162464 · Cited by 118 · see citing works
“Its performance sometimes out-performs the performance of the more sophisticated classifiers as discussed in [138–141].”
MethodAnnual Review of Information Science and Technology
Soergel, D. (2003) · Library & Information Science Research · doi:10.1016/s0740-8188(02)00179-2 · Cited by 111 · see citing works
A Survey on Text Classification: From Shallow to Deep LearningPreprint
Li, Q., Peng, H. M., Li, J., Xia, C., Yang, R., Sun, L., Yu, P. S., & He, L. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2008.00364 · Cited by 110 · see citing works
“For traditional models, NB [8] is the first model used for the text classification task.”
MethodSalton, G. (1970) · Science · doi:10.1126/science.168.3929.335 · Cited by 97 · see citing works
Alyari, F., & Navimipour, N. J. (2018) · Kybernetes · doi:10.1108/k-06-2017-0196 · Cited by 96 · see citing works
MATHEMATICS AND INFORMATION RETRIEVAL
Salton, G. (1979) · Journal of Documentation · doi:10.1108/eb026671 · Cited by 94 · see citing works
Towards advancing the earthquake forecasting by machine learning of satellite data
Xiong, P., Tong, L., Zhang, K., Shen, X., Battiston, R., Ouzounov, D., Iuppa, R., Crookes, D., Long, C., & Zhou, H. (2021) · The Science of The Total Environment · doi:10.1016/j.scitotenv.2021.145256 · Cited by 91 · see citing works
“…Karim 1991), Gradient Boosting Machines (GBM) (Friedman 2001), Deep Neural Network (DNN) (LeCun et al. 2015), Random Forests (RF) (Geurts et al. 2006), Convolutional Neural Network (CNN) (Krizhevsky et al. 2012), Logistic Regression (LR) (Walker and Duncan 1967) and Naive Bayes (NB) (Maron 1961).”
MethodDong, L., Li, X., & Xie, G. (2014) · Abstract and Applied Analysis · doi:10.1155/2014/459137 · Cited by 85 · see citing works
“Despite this unrealistic assumption, empirical studies demonstrate that this assumption does not need to significantly compromise the accuracy of the prediction, and NBCs are used in a variety of applications, including document classification [28], medical diagnosis [29, 30], systems performance management [31], prob…”
BackgroundIndexing and abstracting by association
Doyle, L. B. (1962) · American Documentation · doi:10.1002/asi.5090130404 · Cited by 84 · see citing works
Automated text classification using a dynamic artificial neural network model
Ghiassi, M., Olschimke, M., Moon, B., et al. (2012) · Expert Systems with Applications · doi:10.1016/j.eswa.2012.03.027 · Cited by 82 · see citing works
“Text categorization research has its roots in the 1960s (Maron, 1961).”
BackgroundText Classification for Data Loss Prevention
Hart, M., Manadhata, P. K., & Johnson, R. (2011) · Lecture notes in computer science · doi:10.1007/978-3-642-22263-4_2 · Cited by 81 · see citing works
“Research in the document classification field dates back to 1960s [36, 6].”
BackgroundHandbook of Research on Text and Web Mining Technologies
Song, M., & Wu, Y.-F. B. (2009) · IGI Global eBooks · doi:10.4018/978-1-59904-990-8 · Cited by 79 · see citing works
“Classical methods such as Naïve Bayes (Maron, 1961) are sometimes used for this purpose.”
BackgroundMethodEEG-Based Eye Movement Recognition Using Brain–Computer Interface and Random Forests
Antoniou, E., Bozios, P., Christou, V., Tzimourta, K. D., Kalafatakis, K., Tsipouras, M. G., Γιαννακέας, Ν., & Tzallas, A. T. (2021) · Sensors · doi:10.3390/s21072339 · Cited by 77 · see citing works
“Naïve Bayes proposed by Maron [20] makes use of Bayes’ theorem and uses probability theory to classify data.”
MethodShiraishi, T., Motohka, T., Thapa, R. B., et al. (2014) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2014.2313572 · Cited by 73 · see citing works
Scrutinizing an algorithmic technique: the Bayes classifier as interested reading of reality
Rieder, B. (2016) · Information Communication & Society · doi:10.1080/1369118x.2016.1181195 · Cited by 71 · see citing works
“…of the probabilistic approach proposed by Maron, meant that text documents were to be sorted into user-speci fi ed subject categories: ‘ [b]ased on some more or less clear notion of the category, we must decide whether or not an arbitrary document should be assigned to it ’ (Maron, 1961, p. 404).”
BackgroundMethodInfluentialWhy-oriented end-user debugging of naive Bayes text classification
Kulesza, T., Stumpf, S., Wong, W., Burnett, M., Perona, S., Ko, A. J., & Oberst, I. (2011) · ACM Transactions on Interactive Intelligent Systems · doi:10.1145/2030365.2030367 · Cited by 69 · see citing works
Does Physical Activity Predict Obesity—A Machine Learning and Statistical Method-Based Analysis
Cheng, X., Lin, S.-Y., Liu, J., Liu, S., Zhang, J., Nie, P., Fuemmeler, B. F., Wang, Y., & Xue, H. (2021) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph18083966 · Cited by 70 · see citing works
“The naïve Bayes classifier is based on Bayes’ theorem [30].”
MethodA comparative study on text representation schemes in text categorization
Song, F., Liu, S., & Yang, J. (2005) · Pattern Analysis and Applications · doi:10.1007/s10044-005-0256-3 · Cited by 70 · see citing works
“Text categorization, the assignment of free text documents to one or more predefined categories based on their content, goes back at least to the early 1960s and to Maron’s seminal work [1].”
BackgroundMeasuring urban landscapes for urban function classification using spatial metrics
Xing, H., & Meng, Y. (2019) · Ecological Indicators · doi:10.1016/j.ecolind.2019.105722 · Cited by 69 · see citing works
Combining Internal- and External-Training-Loads to Predict Non-Contact Injuries in Soccer
Vallance, E., Sutton-Charani, N., Imoussaten, A., et al. (2020) · Applied Sciences · doi:10.3390/app10155261 · Cited by 69 · see citing works
“sam ples = 29 value = [2 , 27] class = no injur y”
BackgroundMethodInfluentialData and Information Leakage Prevention Within the Scope of Information Security
Hauer, B. (2015) · IEEE Access · doi:10.1109/access.2015.2506185 · Cited by 67 · see citing works
“As long ago as 1961 an article entitled ‘‘Automatic Indexing: An Experimental Inquiry’’ was published which proposed a ‘‘technique for automatically classifying (indexing) documents according to their subject content’’ [6].”
BackgroundRecognition of Urdu Handwritten Characters Using Convolutional Neural Network
Husnain, M., Missen, M. M. S., Mumtaz, S., Jhanidr, M. Z., Coustaty, M., Luqman, M. M., Ogier, J.-M., & Choi, G. S. (2019) · Applied Sciences · doi:10.3390/app9132758 · Cited by 67 · see citing works
“It is observed that several machine learning models like SVM (support vector machines) [12], NB (naive Bayes) [13], ANN (artificial neural network) [14,15], etc., were applied in the analysis of Urdu handwritten text in the literature survey.”
MethodGroce, A., Kulesza, T., Zhang, C., Shamasunder, S., Burnett, M., Wong, W., Stumpf, S., Das, S., Shinsel, A., Bice, F., et al. (2014) · IEEE Transactions on Software Engineering · doi:10.1109/tse.2013.59 · Cited by 66 · see citing works
“To produce multiple classifiers C to test, we trained a Naive Bayes (NB) classifier [40] and a support vector machine [12] on each training set.”
MethodValidation of the repertory grid for use in modeling knowledge
Latta, G. F., & Swigger, K. (1992) · Journal of the American Society for Information Science · doi:10.1002/(sici)1097-4571(199203)43:2 · Cited by 56 · see citing works
Modeling the Internet and the Web
Baldi, P., Frasconi, P., & Smyth, P. (2003) · Cited by 65 · see citing works
Naive Bayesian Classifier for Incident Duration Prediction
Boyles, S. D., Fajardo, D., & Waller, S. T. (2007) · Cited by 64 · see citing works
Wang, F., Chen, Z., & Song, G. (2020) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2020.107186 · Cited by 63 · see citing works
How Well Sentence Embeddings Capture Meaning
White, L., Togneri, R. B., Liu, W., et al. (2015) · Australasian Document Computing Symposium · doi:10.1145/2838931.2838932 · Cited by 62 · see citing works
“Early work in the area goes back to [11] and [1].”
BackgroundText Mining in Social Networks
Aggarwal, C. C., & Wang, H. (2011) · Springer eBooks · doi:10.1007/978-1-4419-8462-3_13 · Cited by 62 · see citing works
“Some common algorithms which are often used for content-based text classi cation are the Naive Bayes classi er [36], TFIDF classi er [30] and Probabilistic Indexing classi er [20] respectively.”
MethodA General Mathematical Model for Information Retrieval Systems
Cooper, W. S. (1976) · The Library Quarterly · doi:10.1086/620501 · Cited by 62 · see citing works
“Under some methods words selected from a natural language are designated as "content bearing" or "informing" words because of their syntactic or other properties, and if such a word occurs at least once in a document, that word is used as an index term for that document [17].”
MethodIIsy: Hybrid In-Network Classification Using Programmable Switches
Zheng, C., Xiong, Z., Bui, T. T., Kaupmees, S., Bensoussane, R., Bernabeu, A., Vargaftik, S., Ben-Itzhak, Y., & Zilberman, N. (2024) · IEEE/ACM Transactions on Networking · doi:10.1109/tnet.2024.3364757 · Cited by 107 · see citing works
“For a Naïve Bayes classifier [44], we assume a Gaussian distribution of independent features [45].”
MethodDemetrio, L., Valenza, A., Costa, G., & Lagorio, G. (2020) · ACM Symposium on Applied Computing · doi:10.1145/3341105.3373962 · Cited by 57 · see citing works
“The learning algorithm used for detecting injections is a Naive Bayes [30].”
MethodErkan, U., Toktaş, A., & Üstün, D. (2022) · Journal of Ambient Intelligence and Humanized Computing · doi:10.1007/s12652-021-03631-w · Cited by 56 · see citing works
“…vector machine (SVM) (Cortes and Vap-nik 1995), K-nearest neighbors (KNN) (Dudani 1976), Random Forest (RF) (Breiman 2001), Naïve Bayes (NB) (Maron 1960) and fuzzy system (Mohammadzadeh and Zhang 2019; Balootaki et al. 2020; Mohammadzadeh et al. 2020; Mittal et al. 2020) are the well-known…”
Classification of Text, Automatic
Sebastiani, F. (2006) · Elsevier eBooks · doi:10.1016/b0-08-044854-2/00964-0 · Cited by 54 · see citing works
“Maron’s seminal paper (Maron, 1961) is usually taken to mark the official birthdate of TC, which at the time was called automatic indexing ; this name reflected the fact that the main (or only) application that was then envisaged for TC was automatically indexing (i.”
BackgroundEffective packet number for early stage internet traffic identification
Peng, L., Yang, B., & Chen, Y. (2015) · Neurocomputing · doi:10.1016/j.neucom.2014.12.053 · Cited by 52 · see citing works
“In this study, we choose Naive Bayes classifier [11], [32] and Bayesian network (BayesNet) [18] as Bayes classifiers.”
MethodStatistical Comparisons of the Top 10 Algorithms in Data Mining for Classification Task
Settouti, N., Bechar, M. E. A., & Chikh, M. A. (2016) · International Journal of Interactive Multimedia and Artificial Intelligence · doi:10.9781/ijimai.2016.419 · Cited by 51 · see citing works
Towards graphical models for text processing
Aggarwal, C. C., & Zhao, P. (2012) · Knowledge and Information Systems · doi:10.1007/s10115-012-0552-3 · Cited by 50 · see citing works
Liu, Y., Chen, R., Chen, Y., et al. (2012) · ACM International Conference on Web Search and Data Mining (WSDM) · doi:10.1145/2124295.2124356 · Cited by 50 · see citing works
“Figure 6: Distribution of Motivation Categories In Consentient Hand-Coded Sample (with normalization) We then train a binary classi.er for each category using Naïve Bayes [29], maximum entropy [31], and support vector machines (SVM) [11].”
MethodInfluentialEffectiveness of Statistical Features for Early Stage Internet Traffic Identification
Peng, L., Yang, B., Chen, Y., & Chen, Z. (2015) · International Journal of Parallel Programming · doi:10.1007/s10766-014-0337-2 · Cited by 49 · see citing works
“In this study, we choose Naive Bayes classifier [7,22] and Bayesian network (BayesNet) [12] as Bayes classifiers.”
MethodA survey on multi-modal social event detection
Zhou, H., Yin, H., Zheng, H., & Li, Y. (2020) · Knowledge-Based Systems · doi:10.1016/j.knosys.2020.105695 · Cited by 49 · see citing works
Walston, C. E. (1966) · Advances in computers · doi:10.1016/s0065-2458(08)60417-9 · Cited by 49 · see citing works
Automatic categorization of case law
Thompson, P. (2001) · International Conference on Artificial Intelligence and Law · doi:10.1145/383535.383543 · Cited by 47 · see citing works
“Proceedings of the 19th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR ‘96) Konstanz, Germany: Hartung-Gorre Verlag, 307-315 [24] Maron, M. E. Automatic indexing: an experimental inquiry Journal of the Association for Computing Machinery.”
BackgroundSequential patterns for text categorization
Jaillet, S., Laurent, A., & Teisseire, M. (2006) · Intelligent Data Analysis · doi:10.3233/ida-2006-10302 · Cited by 46 · see citing works
“Automatic text classification goes back at least to the 1960s [24].”
BackgroundSSEM: A Novel Self-Adaptive Stacking Ensemble Model for Classification
Jiang, W., Chen, Z., Xiang, Y., Shao, D., Ma, L., & Zhang, J. (2019) · IEEE Access · doi:10.1109/access.2019.2933262 · Cited by 46 · see citing works
“RESULTS In order to show the performance of the SSEM method, we compare it with other 11 state-of-art classification algorithms including NB [51], ERT [52], Logistic [53], RF [54], CART [55], Back Propagation Network (BPN) [56], support vector machines (SVM) [57], AdaBoost [58], Bagging [59], Convolutional Neural Netw…”
MethodAggregating Customer Review Attributes for Online Reputation Generation
Benlahbib, A., & Nfaoui, E. H. (2020) · IEEE Access · doi:10.1109/access.2020.2996805 · Cited by 50 · see citing works
“Three machine learning classifiers (Support Vector Machines (SVMs) [23], Naïve Bayes classifier [24] and Maximum Entropy classifier [25]) were trained with movie reviews labeled by sentiment (positive/negative).”
MethodLiu, X., Tang, T., & Ding, N. (2021) · Egyptian Informatics Journal · doi:10.1016/j.eij.2021.04.003 · Cited by 45 · see citing works
The Maximum-Margin Approach to Learning Text Classifiers: Methods, Theory, and Algorithms
Joachims, T. (2001) · Ausgezeichnete Informatikdissertationen · Cited by 45 · see citing works
Poly, T. N., Islam, M. M., Muhtar, M. S., Yang, H., Nguyen, P., & Li, Y. (2020) · JMIR Medical Informatics · doi:10.2196/19489 · Cited by 44 · see citing works
“In 1960, the NB model was first introduced for text classification by the text retriever community [27].”
MethodPredicting the spatiotemporal legality of on-street parking using open data and machine learning
Gao, S., Li, M., Liang, Y., et al. (2019) · Annals of GIS · doi:10.1080/19475683.2019.1679882 · Cited by 43 · see citing works
“Naive Bayes : Naive Bayes is a probabilistic classi fi er that makes classi fi cations using the Maximum A Posteriori decision rule in a Bayesian setting (Maron 1961).”
MethodRieder, B. (2020) · Amsterdam University Press eBooks · doi:10.2307/j.ctv12sdvf1 · Cited by 40 · see citing works
Extraction-Based Text Categorization: Generating Domain-Specific Role Relationships Automatically
Riloff, E., & Lorenzen, J. (1999) · Text, speech and language technology · doi:10.1007/978-94-017-2388-6_7 · Cited by 40 · see citing works
SciKGraph: A knowledge graph approach to structure a scientific field
Tosi, M. D. L., & dos Reis, J. C. (2020) · Journal of Informetrics · doi:10.1016/j.joi.2020.101109 · Cited by 39 · see citing works
Applying an existing machine learning algorithm to text categorization
Moulinier, I., & Ganascia, J. (1996) · Lecture notes in computer science · doi:10.1007/3-540-60925-3_58 · Cited by 39 · see citing works
“These methods include Bayesian classi cation [6], neural networks [7], decision tree construction [4, 8], memory-based learning [9] and optimization techniques [5].”
MethodHuang, X., Liu, Z. Y., Zhang, M., 桂, 韬., 韩, 先., 户, 保., 黄, 民., 黄, 萱., 刘, 康., 刘, 挺., et al. (2023) · Scientia Sinica Informationis · doi:10.1360/ssi-2023-0113 · Cited by 40 · see citing works
Feature generation for textual information retrieval using world knowledge
Gabrilovich, E. (2007) · ACM SIGIR Forum · doi:10.1145/1328964.1328988 · Cited by 38 · see citing works
“Research in automatic text categorization started in the 1960s, while most articles cite Maron’s work on probabilistic indexing (Maron, 1961) as the first major work in the field.”
BackgroundPredicting particle accelerator failures using binary classifiers
Reščič, M., Seviour, R., & Blokland, W. (2019) · Nuclear Instruments and Methods in Physics Research Section A Accelerators Spectrometers Detectors and Associated Equipment · doi:10.1016/j.nima.2019.163240 · Cited by 38 · see citing works
“Naive Bayes classifiers [74] are a set of supervised learning algorithms based on Bayes’ theorem with the “naive” assumption of conditional independence between every pair of sample features, given the value of the class variable.”
MethodTicket Sales Prediction and Dynamic Pricing Strategies in Public Transport
Branda, F., Marozzo, F., & Talia, D. (2020) · Big Data and Cognitive Computing · doi:10.3390/bdcc4040036 · Cited by 38 · see citing works
“In the following sub-sections we provide a description of the event logs we analyzed and how the different steps of the methodology were applied to them.”
MethodLearning to Use a Learned Model: A Two-Stage Approach to Classification
Antonie, M.-L., Zai͏̈ane, O. R., & Holte, R. C. (2006) · Proceedings · doi:10.1109/icdm.2006.97 · Cited by 38 · see citing works
“Classifiers based on probabilistic models have been proposed starting with the first presented in literature by Maron [20] and continuing with naı̈ve-Bayes [17] that proved to perform well.”
Method
- Authors
- Maron, M. E.
- Affiliations
- RAND Corporation (US)
- Corresponding author
- Maron, M. E.
- Venue
- Journal of the ACM
- Published
- 1961-07
- Volume
- 8
- Issue
- 3
- Pages
- 404-417
- ISSN
- 0004-5411 (ISSN-L)
- 1557-735X
- OpenAlex
- W2009190245
- Cited by
- 570
- References
- 1
- Citations by year
- 2026: 18 · 2025: 16 · 2024: 22 · 2023: 27 · 2022: 29 · 2021: 57 · 2020: 82 · 2019: 44 · 2018: 14 · 2017: 11 · 2016: 9 · 2015: 12 · 2014: 7 · 2013: 10 · 2012: 22
- FWCI
- 9.89
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Journal of the ACM (publishedVersion)
- Defense Technical Information Center (DTIC) (submittedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Text and Document Classification Technologies
- Physical Sciences › Computer Science › Artificial Intelligence › Advanced Text Analysis Techniques
- Physical Sciences › Computer Science › Information Systems › Information Retrieval and Search Behavior
- Keywords
- automatic indexing, document classification, subject classification, clue words, statistical classification, subject categories, information retrieval
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
