The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
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.
Overview
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
Verification
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?
In the citing paper
“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.
Citations
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
Self-citation
Flagged first, then by citations
Cited by
Sorted by citations
Items 101–200 of 570
An association-based method for automatic indexing with a controlled vocabulary
Plaunt, C., & Norgard, B. A. (1998) · Journal of the American Society for Information Science · doi:10.1002/(sici)1097-4571(199808)49:10 · Cited by 38
Chen, H., Nguyen, B.-A., Yan, Y.-X., & Dow, C. (2020) · IEEE Access · doi:10.1109/access.2020.3024102 · Cited by 43
“We assessed various machine learning algorithms for our prediction task: K-nearest neighbors (KNN) [50], naive Bayes (NB) [51], support vector machine (SVM) [52], decision tree (DT) [53], random forest (RF) [54] and AdaBoost (AB) [55].”
MethodSmucny, J., Davidson, I. A. K., & Carter, C. S. (2020) · Human Brain Mapping · doi:10.1002/hbm.25286 · Cited by 36
“Naive Bayes (Maron, 1961) compares the probability of observing an Improver or non-Improver for each test data point according to the equation P ( C i j x ) = ( P ( C i ) * p ( x j C i ) / p ( x ) where P ( C i ) is the prior probability of class C i (e.g., Improver) occurring, p ( x j C i ) is the…”
BackgroundInfluentialMining the Technical Roles of GitHub Users
Montandon, J. E., Valente, M. T., & Silva, L. L. (2020) · Information and Software Technology · doi:10.1016/j.infsof.2020.106485 · Cited by 36
“Machine Learning Classifier: We initially decided to use Random Forest [13] and Naive Bayes [39] classifiers to train and test our models for identifying the technical roles of GitHub users.”
MethodEfficient Bayesian task-level transfer learning
Roy, D. M., & Kaelbling, L. P. (2007) · Cited by 36
Spoiler alert: Machine learning approaches to detect social media posts with revelatory information
Boyd-Graber, J. L., Glasgow, K., & Zajac, J. S. (2013) · Proceedings of the American Society for Information Science and Technology · doi:10.1002/meet.14505001073 · Cited by 36
“Text classification as an approach dates to the 1960s (Maron, 1961), and machine learning techniques provide the mathematical foundation for effective automatic text classification.”
Information Retrieval Based upon Latent Class Analysis
Baker, F. B. (1962) · Journal of the ACM · doi:10.1145/321138.321148 · Cited by 36
The use of titles for automatic document classification
Hamill, K. A., & Zamora, A. (1980) · Journal of the American Society for Information Science · doi:10.1002/asi.4630310603 · Cited by 36
Deng, X., Li, J., Qian, Y., & Liu, J. (2024) · IEEE Transactions on Emerging Topics in Computational Intelligence · doi:10.1109/tetci.2024.3360336 · Cited by 37
“…Fuzzy KNN (GAFuzzyKNN) [55], and Dempster-Shafer KNN (D-SKNN) [56]; 10 classical machine learning classification meth-ods are Naive-Bayes (NB) [57], [58], Linear Discriminant Analysis (LDA) [59], Radial Basis Function Network (RBFN) [60], NU Support Vector Machine (NU-SVM) [61], Sequential Minimal…”
MethodDiscourse Classification into Rhetorical Functions for AWE Feedback
Cotos, E., & Pendar, N. (2016) · CALICO Journal · doi:10.1558/cj.v33i1.27047 · Cited by 36
“Although text categorization work emerged in the early 1960s (Maron, 1961), it became”
BackgroundCombining Machine Learning and Social Network Analysis to Reveal the Organizational Structures
Nurek, M., & Michalski, R. (2020) · Applied Sciences · doi:10.3390/app10051699 · Cited by 38
“In order to fulfill this goal, numerous algorithms have been developed and tuned over last decades, such as logistic regression [18], naive Bayes classifier [19], nearest neighbor [20], Support Vector Machines [21], decision trees [22], random forests [23] or neural networks [24].”
MethodProbabilistic learning for selective dissemination of information
Amati, G., & Crestani, F. (1999) · Information Processing & Management · doi:10.1016/s0306-4573(99)00012-6 · Cited by 34
Signature verification using geometrical features and artificial neural network classifier
Jain, A., Singh, S. K., & Singh, K. P. (2020) · Neural Computing and Applications · doi:10.1007/s00521-020-05473-7 · Cited by 34
“, decision tree [27], naiveBayes [29] andeuclideandistance.”
Gomes, J. C., Masood, A. I., Silva, L. H. D. S., Ferreira, J., Freire, A., Rocha, A., Portela, L., da Silva, N. R. C., Fernandes, B. J. T., & dos Santos, W. P. (2021) · Scientific Reports · doi:10.1038/s41598-021-90766-7 · Cited by 41
Automated essay grading: an evaluation of four conceptual models
Williams, R. F. (2001) · eSpace (Curtin University) · Cited by 33
Sasank, J. V. V. S., Sahith, G. R., Abhinav, K., & Belwal, M. (2019) · International Conference on Communication and Electronics Systems (ICCES) · doi:10.1109/icces45898.2019.9002289 · Cited by 33
“2) Naive Bayes: Naive Bayes is a group of probabilistic calculations that exploit likelihood hypothesis and Bayes theorem to anticipate the tag of content [12].”
BackgroundRetractedSturm, V., Efrosinin, D., Öhlschuster, M., Gusterer, E., Drillich, M., & Iwersen, M. (2020) · Sensors · doi:10.3390/s20051484 · Cited by 32
“Having introduced the first main element of our approach in the last sub-section, we continue with shortly describing another two central elements: We utilise Nearest Centroid Classification [32] using Function D1 with G(λ) as above for TSC, and a Naive Bayes Classifier [33] for the feature-based classification.”
MethodRepresentation quality in text classification
Lewis, D. D. (1990) · DARPA Workshop on Speech and Natural Language (HLT) · doi:10.3115/116580.116681 · Cited by 32
“Alternately, the function may be induced by machine learning techniques from large numbers of previously categorized documents [ 17 ,11,2].”
BackgroundText Categorization of Biomedical Data Sets Using Graph Kernels and a Controlled Vocabulary
Bleik, S., Mishra, M., Huan, J., et al. (2013) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2013.16 · Cited by 32
Robust Text Classification in the Presence of Confounding Bias
Landeiro, V., & Culotta, A. (2016) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v30i1.9997 · Cited by 34
“While researchers have investigated automatic text classification algorithms for over fifty years (Maron 1961), they have mostly applied them to topical categorization of documents.”
MethodRieder, B. (2020) · Amsterdam University Press eBooks · doi:10.1515/9789048537419 · Cited by 32
Model-based learning for point pattern data
Vo, B., Dam, N., Phung, D., Tran, Q. N., & Vo, B.-T. (2018) · Pattern Recognition · doi:10.1016/j.patcog.2018.07.008 · Cited by 30
“[4], [5], [6], [7], [12]) and assumes i.”
BackgroundAutomatic Document Classification Part II . Additional Experiments
Borko, H., & Bernick, M. (1964) · Journal of the ACM · doi:10.1145/321217.321219 · Cited by 30
Applications of machine learning in information retrieval
Cunningham, S. J., Witten, I. H., & Littin, J. (1999) · Research Commons (University of Waikato) · Cited by 30
Intersectional Bias in Hate Speech and Abusive Language DatasetsPreprint
Kim, J., Ortiz, C., Nam, S., Santiago, S., & Datta, V. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2005.05921 · Cited by 49
“Then, we trained and tested the least absolute shrinkage and selection operator (Lasso) (Tibshirani 1996), naive Bayes (Maron 1961), and extreme gradient boosting (XGBoost) algorithms (Chen and Guestrin 2016).”
MethodRelevance-based abstraction identification: technique and evaluation
Gacitúa, R., Sawyer, P., & Gervasi, V. (2011) · Requirements Engineering · doi:10.1007/s00766-011-0122-3 · Cited by 29
“automatic indexing [9, 32, 35], and this has had particular value during the last 15 years or so because of”
BackgroundRSHP: an information representation model based on relationships
Lloréns, J., Morato, J., & Génova, G. (2004) · Studies in fuzziness and soft computing · doi:10.1007/978-3-540-44405-3_8 · Cited by 30
Allocco, D., Song, Q., Gibbons, G., Ramoni, M., & Kohane, I. (2007) · BMC Genomics · doi:10.1186/1471-2164-8-68 · Cited by 33
“We wrote our own implementation of the naïve Bayes algorithm [32] in PERL.”
MethodOptimal feature configuration for dynamic malware detection
García, D. E., & DeCastro‐García, N. (2021) · Computers & Security · doi:10.1016/j.cose.2021.102250 · Cited by 32
Shilling-Scrivo, K. V., Mittelstadt, J. K., & Kanold, P. O. (2021) · Journal of Neuroscience · doi:10.1523/jneurosci.0839-21.2021 · Cited by 24
Analyzing sentiments towards E-Levy policy implementation in Ghana using twitter data
Appiahene, P., Afrifa, S., Kyei, E. A., Choudhry, A., Khatri, I., Raj, C., & Prasad, M. (2024) · International Journal of Information Technology · doi:10.1007/s41870-024-01784-3 · Cited by 27
“In machine learning frameworks, we evaluated the performance of Support Vector Machine [29], Naïve Bayes [30], Random Forest [31], Logistic Regression [32] and XGBoost [33] models.”
Classification rules in standardized partition spaces
Garczarek, U. (2002) · Technische Universität Dortmund Eldorado (Technische Universität Dortmund) · doi:10.17877/de290r-14912 · Cited by 27
A systematic evaluation of filter Unsupervised Feature Selection methods
Solorio-Fernández, S., Carrasco-Ochoa, J. A., & Martínez-Trinidad, J. F. (2020) · Expert Systems with Applications · doi:10.1016/j.eswa.2020.113745 · Cited by 28
The Chinese text categorization system with association rule and category priority
Chiang, D.-A., Keh, H., Huang, H. Y., et al. (2007) · Expert Systems with Applications · doi:10.1016/j.eswa.2007.06.019 · Cited by 26
Hierarchical text categorization and its application to bioinformatics
Kiritchenko, S. (2006) · doi:10.20381/ruor-12881 · Cited by 26
Liu, K., & El-Gohary, N. (2020) · Journal of Computing in Civil Engineering · doi:10.1061/(asce)cp.1943-5487.0000921 · Cited by 28
Riera, T. S., Higuera, J. R. B., Higuera, J. B., Martínez-Herráiz, J.-J., & Montalvo, J. A. S. (2020) · Sustainability · doi:10.3390/su12124945 · Cited by 31
An experimental evaluation of OCR text representations for learning document classifiers
Junker, M., & Hoch, R. (1998) · International Journal on Document Analysis and Recognition (IJDAR) · doi:10.1007/s100320050012 · Cited by 25
“Many different learning models have been proposed for the classification of documents including decision trees [18,10], rule learning [1,3,15,8], Bayesian classifiers [13, 9,10,7], nearest-neighbor methods [14], and neural networks [11,17].”
BackgroundChen, D., Chen, Z., Zhang, Y., et al. (2021) · Journal of Advanced Transportation · doi:10.1155/2021/6687378 · Cited by 24
“(ii) NBC [30]—Naive Bayes classifier—is a family of simple “probabilistic classifiers” based on applying Bayes’ theorem with strong independent assumptions between the features.”
MethodSerbouti, I., Raji, M., Hakdaoui, M., et al. (2022) · Remote Sensing · doi:10.3390/rs14215498 · Cited by 24
A Model for Information Retrieval Based on Possibilistic Networks
Brini, A., Boughanem, M., & Didier Dubois, A. (2005) · Lecture notes in computer science · doi:10.1007/11575832_31 · Cited by 23
“Those problems are dealt with, using semantic [21] or a statistical [14][19], [8][23], or probabilistic [ 10 ] point of views.”
BackgroundHaCohen‐Kerner, Y., Mughaz, D., Beck, H., & Yehudai, E. (2008) · Cybernetics & Systems · doi:10.1080/01969720801944299 · Cited by 23
“The beginning of research in TC can be identified with Maron's ( 1961 ) work on probabilistic text classification.”
BackgroundNamed entity recognition in Vietnamese using classifier voting
Thảo, P. T., Tri, T. Q., Đinh, Đ., et al. (2007) · ACM Transactions on Asian Language Information Processing · doi:10.1145/1316457.1316460 · Cited by 23
Mens, G. L., Kovács, B., Hannan, M. T., & Pros, G. (2023) · Sociological Science · doi:10.15195/v10.a3 · Cited by 22
“We also used a standard machine-learning text classifier based on a bag-of-words (BoW) representation of text documents: the naive Bayes classifier (Maron 1961).”
MethodInfluentialDiscretization for Naive-Bayes learning
Yang, Y. (2017) · doi:10.4225/03/59d42df10cb0a · Cited by 22
“Naive-Bayes classifiers 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, Mura- §1.1 Background 3 matsu, and Billsus 1996; Starr, Ackerman, and Pazzani…”
MethodInfluentialHernandez, L., Kim, R. B., Tokcan, N., Derksen, H., Biesterveld, B. E., Croteau, A. J., Williams, A. M., Mathis, M. R., Najarian, K., & Gryak, J. (2021) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2021.102032 · Cited by 22
“These include Random Forest [40], Naive Bayes [41], Support Vector Machine [42], Adaboost [43] and a recently developed kernel-based method called Learning Using Concave and Convex Kernels (LUCCK) [44].”
Evaluating text categorization in the presence of OCR errors
Taghva, K., Nartker, T. A., Borsack, J., et al. (2000) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.410861 · Cited by 22
Hierarchy-Aware and Label Balanced Model for Hierarchical Text Classification
Zhang, J., Li, Y., Shen, F., Xia, C., Tan, H., & He, Y. (2024) · Knowledge-Based Systems · doi:10.1016/j.knosys.2024.112153 · Cited by 23
REVIEW OF The ORIGINS AND DEVELOPMENT OF RESEARCH
Cleverdon, C. W. (1970) · Aslib Journal of Information Management · doi:10.1108/eb050265 · Cited by 21
Biomedical text categorization with concept graph representations using a controlled vocabulary
Mishra, M., Huan, J., Bleik, S., et al. (2012) · doi:10.1145/2350176.2350181 · Cited by 21
Text Document Classification with PCA and One-Class SVM
Kumar, B. S., & Ravi, V. (2017) · Advances in intelligent systems and computing · doi:10.1007/978-981-10-3153-3_11 · Cited by 21
Shankar, V. G., Sisodia, D. S., & Chandrakar, P. (2021) · International Journal of Imaging Systems and Technology · doi:10.1002/ima.22685 · Cited by 21
“NB classifier is defined in general Equation 16.(42,43)”
MethodMeasuring the reliability of subject classification by men and machines
Borko, H. (1964) · American Documentation · doi:10.1002/asi.5090150405 · Cited by 21
Error-Correcting Output Codes for Multi-Label Text Categorization.
Giuliano Armano, B., Chira, C., & Hatami, N. (2012) · Cited by 20
Detection of Gait Abnormalities caused by Neurological Disorders
Goyal, D., Jerripothula, K. R., & Mittal, A. (2020) · IEEE International Workshop on Multimedia Signal Processing (MMSP) · doi:10.1109/mmsp48831.2020.9287163 · Cited by 20
“We use various machine learning algorithms such as Ada Boost (AB) [26], Decision Tree (DT) [27], k-Nearest Neighbors (kNN) [28], Logistic Regression (LR) [29], Naive Bayes (NB) [30], Neural Networks (NN) [31], Random Forests (RF) [32], Stochastic Gradient Descent (SGD) [33], Support Vectors Machine (SVM) [34] to learn…”
MethodAutomated essay grading systems applied to a first year university subject: how can we do it better?
Palmer, J. C., Williams, R. F., & Dreher, H. V. (2002) · eSpace (Curtin University) · Cited by 20
Text categorization: the assignment of subject descriptors to magazine articles
Moens, M., & Dumortier, J. (2000) · Information Processing & Management · doi:10.1016/s0306-4573(00)00012-1 · Cited by 20
“We implemented the model of Maron (1961), which considers the presence of a term in the text to be classi®ed: M.-F. Moens, J. Dumortier / Information Processing and Management 36 (2000) 841±861 847 P Ck 1 j Dm P Ck 1 Y j P wj 1 j Ck 1 P wj 1 2 where P Ck 1 j Dm is the probability that the category…”
MethodChatzilygeroudis, K. I., Hatzilygeroudis, I., & Perikos, I. (2021) · ACM eBooks · doi:10.1145/3447404.3447414 · Cited by 22
MedKPL: A heterogeneous knowledge enhanced prompt learning framework for transferable diagnosis
Lu, Y., Liu, X., Du, Z., Gao, Y., & Wang, G. (2023) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2023.104417 · Cited by 18
Daley, M. S., Gever, D. H., Posada-Quintero, H. F., Kong, Y., Chon, K. H., & Bolkhovsky, J. (2020) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2020.00017 · Cited by 21
“For the state-classification analysis, six general methods were tested: k-nearest neighbor (KNN) (Wu et al., 2002; Zuo et al., 2008; Samworth, 2012), support vector machines (SVM) (Cortes and Vapnik, 1995; Hsu et al., 2003), decision trees (Quinlan, 1986; Breslow and Aha, 1997; Rokach and Maimon, 2008), discriminant an…”
BackgroundInfluentialAnalog Networks for Word Association
Giuliano, V. E. (1963) · IEEE Transactions on Military Electronics · doi:10.1109/tme.1963.4323077 · Cited by 19
WAF-A-MoLE: An adversarial tool for assessing ML-based WAFs
Valenza, A., Demetrio, L., Costa, G., & Lagorio, G. (2019) · SoftwareX · doi:10.1016/j.softx.2019.100367 · Cited by 21
“The learning algorithm used for detecting injections is a Naive Bayes [13].”
MethodOn Dimensionality Reduction for Indexing and Retrieval of Large-Scale Solar Image Data
Banda, J. M., Angryk, R. A., & Martens, P. C. H. (2012) · Solar Physics · doi:10.1007/s11207-012-0027-4 · Cited by 18
“Automatic identification of flares, on the SDO mission, will be performed by an algorithm created by Martens et al. (2011) which works well for noisy and background-affected light curves.”
MethodLee, W. H., Park, C. Y., Diaz, D., et al. (2022) · Water Research · doi:10.1016/j.watres.2022.118977 · Cited by 18
Lettieri, N., Guarino, A., Malandrino, D., & Zaccagnino, R. (2022) · Artificial Intelligence and Law · doi:10.1007/s10506-022-09334-7 · Cited by 19
“We evaluated the effectiveness of such features using the most adopted machine learning models based on supervised learning, i.e., Naive Bayes (Maron 1961), Support Vector Machine (Wang 2005a), Logistic Regression (Mitchell 2005), MultiLayer Perceptron (Rumelhart et al. 1988), KStar (Cleary and Trigg 1995), Random For…”
MethodTopic Classification from Text Using Decision Tree, K-NN and Multinomial Naïve Bayes
Rahman, M. A., & Akter, Y. A. (2019) · International Conference on Advances in Science, Engineering and Robotics Technology (ICASERT) · doi:10.1109/icasert.2019.8934502 · Cited by 18
“The first idea probablyoriginated back in 1961from the seminal work of Maronon probabilistic text classification[1].”
Xiong, P., Long, C., Zhou, H., Battiston, R., Zhang, X., & Shen, X. (2020) · Remote Sensing · doi:10.3390/rs12213643 · Cited by 19
“Since the methods are sensitive to parameter selection, Bayesian hyperparameter tuning is used to choose the parameters that obtain the best performance.”
MethodInfluentialData Augmentation and Feature Selection for Automatic Model Recommendation in Computational Physics
Daniel, T., Casenave, F., Akkari, N., & Ryckelynck, D. (2021) · Mathematical and Computational Applications · doi:10.3390/mca26010017 · Cited by 20
“Decision trees (e.g. CART algorithm [41]) and naive Bayes classifiers [42, 43] are well-known for their interpretability.”
Effective Packet Number for 5G IM WeChat Application at Early Stage Traffic Classification
Shafiq, M., & Yu, X. (2017) · Mobile Information Systems · doi:10.1155/2017/3146868 · Cited by 19
“In this study, we utilized Bayesian network (Bayes Net) [36] machine learning classifier and also Näıve Bayes machine learning classifiers [37, 38].”
MethodAgricultural loan delinquency prediction using machine learning methods
Chen, J., Katchova, A. L., & Zhou, C. (2021) · The International Food and Agribusiness Management Review · doi:10.22434/ifamr2020.0019 · Cited by 24
A random walk on an ontology: Using thesaurus structure for automatic subject indexing
Willis, C., & Losee, R. M. (2013) · Journal of the American Society for Information Science and Technology · doi:10.1002/asi.22853 · Cited by 17
Bates, M. J. (2004) · Illinois Digital Environment for Access to Learning and Scholarship (University of Illinois at Urbana-Champaign) · Cited by 17
Combining machine learning and hierarchical structures for text categorization
Ruiz, M. E. R., & Srinivasan, P. (2001) · Cited by 17
Klavans, J. L., Sheffield, C. A., Abels, E. G., et al. (2008) · Multimedia Tools and Applications · doi:10.1007/s11042-008-0253-9 · Cited by 17
“Unlike the automatic indexing envisioned by Maron [27] and many later publications, CLiMB presumes that automatically extracted metadata should be reviewed by expert human indexers.”
MethodUnderstanding poetry using natural language processing tools: a survey
Sisto, M. D., Hernández‐Lorenzo, L., de la Rosa, J., Ros, S., & González‐Blanco, E. (2024) · Digital Scholarship in the Humanities · doi:10.1093/llc/fqae001 · Cited by 19
An extended document frequency metric for feature selection in text categorization
Xu, Y., Wang, B., Li, J., et al. (2008) · Asia Information Retrieval Symposium · doi:10.5555/1786374.1786385 · Cited by 16
Classification of Gold-Bearing Particles Using Visual Cues and Cost-Sensitive Machine Learning
Horrocks, T., Wedge, D., Holden, E., Kovesi, P. D., Clarke, N., & Vann, J. (2015) · Mathematical Geosciences · doi:10.1007/s11004-015-9597-7 · Cited by 16
Rabib, M., Sarkar, S., & Rahman, M. S. (2020) · International Journal of Computer Applications · doi:10.5120/ijca2020920107 · Cited by 16
“It was introduced (though not under that name) into the text retrieval community in the early 1960s,[22] and remains a popular (baseline) method for text categorization, the problem of judging documents as belonging to one category or the other (such as spam or legitimate, sports or politics, etc.”
BackgroundImproving the performance of association classifiers by rule prioritization
Chen, C.-H., Chiang, R., Lee, C.-M., et al. (2012) · Knowledge-Based Systems · doi:10.1016/j.knosys.2012.06.004 · Cited by 16
“mutual information [15], information gain [14], and Chi-square test approaches.”
Evaluating OCR and non-OCR text representations for learning document classifiers
Junker, M., & Hoch, R. (2002) · IAPR International Conference on Document Analysis and Recognition (ICDAR) · doi:10.1109/icdar.1997.620671 · Cited by 16
“Classi er construction Many di erent learning models have been proposed for the classi cation of documents including decision trees [20, 11], rule learning [1, 3, 16], Bayesian classi ers [14, 10, 11, 8], nearestneighbor methods [15] and neural networks [12, 19].”
Formative Assessment Visual Feedback in Computer Graded Essays
Williams, R. F., & Dreher, H. V. (2005) · Informing Science and IT Education Conference · doi:10.28945/2843 · Cited by 16
Effective Automatic Indexing Using Term Addition and Deletion
Yu, C., Salton, G., & Siu, M. (1978) · Journal of the ACM · doi:10.1145/322063.322066 · Cited by 16
Learning via variably scaled kernels
Campi, C., Marchetti, F., & Perracchione, E. (2021) · Advances in Computational Mathematics · doi:10.1007/s10444-021-09875-6 · Cited by 15
Li, M. F., Glibert, P. M., & Lyubchich, V. (2021) · Journal of Marine Science and Engineering · doi:10.3390/jmse9090999 · Cited by 15
“The NB classifier is a simple probabilistic classifier based on the Bayes’ rule and requires strong “naïve” independence between the features [66,67].”
MethodYang, M., Chen, X., Tan, L., Lan, X., & Luo, Y. (2022) · Information Processing & Management · doi:10.1016/j.ipm.2022.103186 · Cited by 17
Predicting long-term product ratings based on few early ratings and user base analysis
Khopkar, S. S., & Nikolaev, A. G. (2016) · Electronic Commerce Research and Applications · doi:10.1016/j.elerap.2016.12.002 · Cited by 15
“…Neighbor Methods” (Cohen and Hirsh, 1998; Yang, 1999; Allan, 1998; Billsus et al., 2000), ‘‘Relevance Feedback and Rocchios Algorithm” (Rocchio, 1971; Ittner et al., 1995), ‘‘Linear Classifiers” (Lewis et al., 1996), ‘‘Probabilistic Methods and Naive Bayes” (Maron, 1961; Duda and Hart, 1973).”
Shilling-Scrivo, K. V., Mittelstadt, J. K., & Kanold, P. O. (2022) · Journal of Neuroscience · doi:10.1523/jneurosci.0955-22.2022 · Cited by 15
Xu, H., Zhang, Y., & Xu, Y. (2023) · IEEE Access · doi:10.1109/access.2023.3275085 · Cited by 16
Automated Essay Grading System Applied to a First Year University Subject - How Can We Do It Better?
Palmer, J. C., Williams, R. F., & Dreher, H. V. (2002) · Informing Science and IT Education Conference · doi:10.28945/2553 · Cited by 15
Exploitation of Social Network Data for Forecasting Garment Sales
Giri, C., Thomassey, S., & Zeng, X. (2019) · International Journal of Computational Intelligence Systems · doi:10.2991/ijcis.d.191109.001 · Cited by 15
“For the tweet classification task, a probabilistic classifier “NB” algorithm [25] was used as it is the effective classification model for text classification.”
Service Discovery in Mobile Ad-Hoc Environments: A Solution Space Analysis
Bose, R. (2020) · International Journal of Emerging Trends in Engineering Research · doi:10.30534/ijeter/2020/129872020 · Cited by 15
Qiu, M., Zhang, Y., Ma, T., et al. (2020) · Sensors and Materials · doi:10.18494/sam.2020.2794 · Cited by 15
Forty Years of Classification Online: Final Chapter or Future Unlimited?
Markey, K. (2006) · Cataloging & Classification Quarterly · doi:10.1300/j104v42n03_01 · Cited by 15
Mining Brain Tumors and Tracking their Growth Rates
Elamy, A.-H. H., & Hu, M. (2007) · Conference proceedings - Canadian Conference on Electrical and Computer Engineering · doi:10.1109/ccece.2007.222 · Cited by 15
Statistical and Information-Theoretic Methods for Data-Analysis
Roos, T. (2007) · Työväentutkimus Vuosikirja · Cited by 15
Optimum procedures for economic information retrieval
Heaps, H. S., & Thiel, L. H. (1970) · Information Processing & Management · doi:10.1016/0020-0271(70)90057-4 · Cited by 15
Klasifikasi Untuk Prediksi Cuaca Menggunakan Esemble Learning
Siregar, A. M. (2020) · Petir · doi:10.33322/petir.v13i2.998 · Cited by 32
“Dalam istilah sederhana, sebuah classifier Naive Bayes mengasumsikan bahwa kehadiran fitur tertentu dalam kelas tidak terkait dengan kehadiran fitur lainnya [9],[10].”
BackgroundA novel model to label delirium in an intensive care unit from clinician actions
Coombes, C. E., Coombes, K. R., & Fareed, N. (2021) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-021-01461-6 · Cited by 15
When and Why Is a Pioneer: History and Heritage in Library and Information Science
Rayward, W. B. (2004) · Illinois Digital Environment for Access to Learning and Scholarship (University of Illinois at Urbana-Champaign) · Cited by 14
An Extended Document Frequency Metric for Feature Selection in Text Categorization
Xu, Y., Wang, B., Li, J., et al. (2008) · Springer eBooks · doi:10.1007/978-3-540-68636-1_8 · Cited by 14
“Due to its simplicity and effectiveness, DF is adopted in more and more experiments[7][4][2][6].”
Method
Metadata
- 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
