Cited by 570Text and Document Classification TechnologiesAdvanced Text Analysis TechniquesInformation Retrieval and Search Behavior

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Automatic Indexing: An Experimental Inquiry

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Maron, M. E.·1961·Journal of the ACM

DOI 10.1145/321075.321084

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Abstract

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.

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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
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Machine Learning Basics
Skansi, S. (2018) · Undergraduate topics in computer science · doi:10.1007/978-3-319-73004-2_3 · Cited by 5
Corpus-Based Methods for Recognizing the Gender of Anthroponyms
Nazar, R., Renau, I., Acosta, N. N., et al. (2021) · Names · doi:10.5195/names.2021.2238 · Cited by 5
Document-Base Extraction for Single-Label Text Classification
Wang, Y. J., Sanderson, R., Coenen, F., et al. (2008) · Lecture notes in computer science · doi:10.1007/978-3-540-85836-2_34 · Cited by 5
“Early studies of TC can be dated back to the early 1960s (see for instance [13]).”
Background
Automatic Translation to Controlled Medical Vocabularies
Kornai, A., & Stone, L. (2004) · Studies in fuzziness and soft computing · doi:10.1007/978-3-540-39615-4_17 · Cited by 5
“All handcrafted classifiers, based on (combinations of) keywords and rules, fall in the first type, where we also find automatically generated models such as Naive Bayes [38] and linear machines [25,36].”
Method
State of the art of published indexes
Markus, J. (1962) · American Documentation · doi:10.1002/asi.5090130103 · Cited by 5
Classification, Relevance, and Information Retrieval
Jackson, D. M. (1971) · Advances in computers · doi:10.1016/s0065-2458(08)60630-0 · Cited by 5
Structured and collaborative search: an integrated approach to share documents among users
Francq, P., & Delchambre, A. (2003) · Dépôt institutionnel de l'Université libre de Bruxelles (Université Libre de Bruxelles) · Cited by 5
Research in automatic generation of classification systems
Borko, H. (1964) · AFIPS Spring Joint Computer Conference (SJCC) · doi:10.1145/1464122.1464173 · Cited by 5
A distance measure for automatic document classification by sequential analysis
Kar, G., & White, L. J. (1978) · Information Processing & Management · doi:10.1016/0306-4573(78)90063-8 · Cited by 5
Indexing process evaluation
Bernier, C. L. (1965) · American Documentation · doi:10.1002/asi.5090160407 · Cited by 5
Computers in the Library
LYNCH, M. F. (1966) · Nature · doi:10.1038/2121402a0 · Cited by 5
Term association analysis on a large file of bibliographic data, using a highly-controlled indexing vocabulary
Jacquesson, A., & Schieber, W. D. (1973) · Information Processing & Management · doi:10.1016/0020-0271(73)90021-1 · Cited by 5
Learning Complex Users' Preferences for Recommender SystemsPreprint
Yakhchi, S. (2021) · arXiv (Cornell University) · doi:10.25949/21385734 · Cited by 9
A Comprehensive Study of Various Hybrid Deep Learning Models for Leukaemia Classification: Comparative Analysis with Existing Studies
Banerjee, T., Singh, D. P., Dhanabal, S., Chandar, K. P., Bommisetti, R. K., Narayan, Y., Chakole, N., Dutta, C., Bhosale, Y. H., & Singh, R. (2026) · Archives of Computational Methods in Engineering · doi:10.1007/s11831-026-10565-z · Cited by 4
“The early indexing models, while rudimen - tary, provide a foundation for the evolution of automated medical informatics systems [ 44 ].”
Global cost of living patterns: machine learning insights and structural economic interpretation
Çelik, S., ÇİMEN, G., & Yavan, S. (2026) · Quality & Quantity · doi:10.1007/s11135-026-02752-8 · Cited by 5
“Hence, it is called “naive” (naive) (Maron 1961; Zhang 2004).”
Unsupervised Feature Generation using Knowledge Repositories for Effective Text Categorization
Prasath, R., & Sarkar, S. (2010) · Frontiers in artificial intelligence and applications · doi:10.3233/978-1-60750-606-5-1101 · Cited by 4
RoBERTa: An Efficient Dating Method of Ancient Chinese Texts
Li, M., Qin, Y., & Huangfu, W. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-28956-9_23 · Cited by 4
Text Categorization Based on Emergency Domain Words: A System Engineering View
Zhao, Y., & Wang, Y. G. (2012) · Systems Engineering Procedia · doi:10.1016/j.sepro.2012.04.002 · Cited by 4
“Related work Since Maron [1] published the first paper about text automatic categorization in 1961, text classification (TC) has gone through several development processes.”
Background
Naive Bayes Modeling with Proper Smoothing for Information Extraction
Gu, Z., & Cercone, N. (2006) · IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) · doi:10.1109/fuzzy.2006.1681742 · Cited by 4
“The naive Bayes model [5] is a popular statistical learning technique and has found many successful applications in practice.”
Background
A Survey on Mathematical Aspects of Machine Learning in GeoPhysics: The Cases of Weather Forecast, Wind Energy, Wave Energy, Oil and Gas Exploration
Kosanić, M., & Milutinović, V. M. (2021) · Mediterranean Conference on Embedded Computing (MECO) · doi:10.1109/meco52532.2021.9460245 · Cited by 4
“Various forms of such mappings f exist, like linear and nonlinear regression ( [21], [22], [23], [24], [25]), logistic regression ( [26], [28], [29]), decision trees ( [31], [32], [32], [33], [34], [35], [36]) , decision forests ( [37], [38], [39], [40], [41]), support vector machines ( [42], [43], [44], [45]), neural…”
Statistical and Visual Analysis of Audio, Text, and Image Features for Multi-Modal Music Genre Recognition
Wilkes, B., Vatolkin, I., & Müller, H. (2021) · Entropy · doi:10.3390/e23111502 · Cited by 4
ICS: Total Freedom in Manual Text Classification Supported by Unobtrusive Machine Learning
Esuli, A. (2022) · IEEE Access · doi:10.1109/access.2022.3184009 · Cited by 5
“This is why the automatic classification of texts is a research topic that has a long history in computer science [1].”
Background
Flexible Android Malware Detection Model based on Generative Adversarial Networks with Code Tensor
Zhao, Y., Deng, F., & Han, L. (2022) · International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery (CyberC) · doi:10.1109/cyberc55534.2022.00015 · Cited by 4
“Naive Bayes (NB) [20] classifier is based on Bayes’ theorem and assumes that the feature conditions are independent of each other.”
Background
Ranking Based Unsupervised Feature Selection Methods: An Empirical Comparative Study in High Dimensional Datasets
Solorio-Fernández, S., Carrasco-Ochoa, J. A., & Martínez-Trinidad, J. F. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-04491-6_16 · Cited by 4
ModHMM: A Modular Supra-Bayesian Genome Segmentation Method
Benner, P., & Vingron, M. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-17083-7_3 · Cited by 4
Combining SentiStrength and Multilayer Perceptron in Twitter Sentiment Classification
Prastowo, E. Y., Endroyono, E., & Yuniarno, E. M. (2019) · International Seminar on Intelligent Technology and Its Applications · doi:10.1109/isitia.2019.8937134 · Cited by 4
“The machine learning algorithm used is Multilayer Perceptron[8] and Naïve Bayes[9] as comparison.”
Method
Automatic Misogyny Detection with Linguistic and Morphological Features in Spanish
Cánovas-García, M., García-Díaz, J. A., & Valencia‐García, R. (2020) · Communications in computer and information science · doi:10.1007/978-3-030-62015-8_3 · Cited by 4
Automatic Classification for the Identification of Relationships in a Meta-Data Repository
Beuster, G., Furbach, U., Groß-Hardt, M., & Thomas, B. (2003) · Lecture notes in computer science · doi:10.1007/978-3-540-39644-4_24 · Cited by 4
“[Maron, 1961] Because of their simplicity, they became kind of a reference method for estimating the difﬁculty of a classiﬁcation problem.”
Method
Leveraging collective knowledge
Kon, H., & Hoey, M. (2005) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/1099554.1099702 · Cited by 4
Invited papers---1
Taulbee, O. E. (1965) · ACM National Conference · doi:10.1145/800197.806038 · Cited by 4
A Comparative Study of Associative Classifiers in Mesenchymal Stem Cell Differentiation Analysis
Wang, W., Wang, Y. J., Xin, Q., Bañares‐Alcántara, R., Coenen, F., & Cui, Z. (2012) · IGI Global eBooks · doi:10.4018/978-1-4666-2455-9.ch049 · Cited by 4
Performance Analysis of Statistical Pattern Recognition Methods in KEEL
Ali, Z., Shahzad, S. K., & Shahzad, W. (2017) · Procedia Computer Science · doi:10.1016/j.procs.2017.08.144 · Cited by 4
Bayesian Web Document Classification through Optimizing Association Word
Ko, S. J., Choi, J. H., & Lee, J. H. (2007) · Springer eBooks · doi:10.1007/3-540-45034-3_57 · Cited by 4
Comparing vector space retrieval with the RUBRIC expert system
Gey, F. C., & Chan, W. (1988) · ACM SIGIR Forum · doi:10.1145/1095483.1095484 · Cited by 4
On the number of categories for classification
Hoyle, W. G. (1969) · Information Processing & Management · doi:10.1016/0020-0271(69)90002-3 · Cited by 4
An automatic email distribution by using text mining and reinforcement learning
Ueda, Y., Narita, H., Kato, N., et al. (2006) · Systems and Computers in Japan · doi:10.1002/scj.20387 · Cited by 4
“However, as is well known in research on extracting index words, words that appear infrequently are unnecessary words, and words that appear very frequently are often general words and not feature terms, and thus are unnecessary words [26].”
Background
Model-Based Multiple Instance LearningPreprint
Vo, B., Phung, D., Tran, Q. N., et al. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1703.02155 · Cited by 4
“[4], [5], [6], [7], [12]) and assumes i.”
Background
Pitfalls of machine learning models for protein-protein interactionsPreprint
Lannelongue, L., & Inouye, M. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.02.07.479382 · Cited by 4
“The low dimensionality of the transformed problem explains the success of standard machine learning algorithms; in particular, Naïve Bayes Classifiers [33], decision trees [34] and Random Forests [35] have been the most popular choices [5]–[7], [36].”
Background
COMPARISON OF MACHINE LEARNING MODELS IN HEART FAILURE PREDICTION AND THEIR INTEGRATION INTO CLINICAL DECISION SUPPORT SYSTEMS
Cakir, M. C. (2025) · International Journal of 3D Printing Technologies and Digital Industry · doi:10.46519/ij3dptdi.1724620 · Cited by 3
A review of acoustic systems for insect monitoring: From recording technologies to species recognition performance
Fengler, B., Seyidbayli, C., Reinhardt, A., Anders, M., & Westphal, C. (2026) · Ecological Informatics · doi:10.1016/j.ecoinf.2026.103733 · Cited by 4
BYOC: Personalized Few-Shot Classification with Co-Authored Class Descriptions
Bohra, A., Verkes, G., Harutyunyan, A., Weinberger, P., & Campagna, G. (2023) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2023.findings-emnlp.933 · Cited by 8
A Comprehensive Review of Text Classification Algorithms
Song, Y., Liu, X., & Zhou, Z. (2024) · Journal of Electronics and Information Science · doi:10.23977/jeis.2024.090205 · Cited by 7
“Initially dependent on manually crafted rules like keyword matching, the field has advanced with statistical learning methods such as Naive Bayes [2] and SVMs [3] , enhancing automation and performance.”
Method
Category Mapping of Emergency Supplies Classification Standard Based on BERT-TextCNN
Zhang, Q., Hou, H., Ju, Y., Yuan, J., Zhang, K., Wang, H., & Chen, J. (2024) · Systems · doi:10.3390/systems12090358 · Cited by 3
“NB [12] was the first model used for text classification tasks.”
Identification of Sensitive Unclassified Information
Taghva, K. (2009) · Springer eBooks · doi:10.1007/978-3-642-01141-2_6 · Cited by 3
“Most extant statistical text categorization techniques are based on the probabilistic approach introduced by Maron and others (Maron and Kuhns 1960; Maron 1967; Lewis 1991).”
Method
WeChat traffic classification using machine learning algorithms and comparative analysis of datasets
Shafiq, M., Yu, X., & Laghari, A. A. (2018) · International Journal of Information and Computer Security · doi:10.1504/ijics.2018.10012574 · Cited by 11
Application of a Novel Subject Classification Scheme for a Bibliographic Database Using a Data-Driven Correspondence
Kurakawa, K., Sun, Y., & Ando, S. (2020) · Frontiers in Big Data · doi:10.3389/fdata.2019.00048 · Cited by 3
A Movie Trailer Recommendation System Based on Pre-trained Vector of Relationship and Scenario Content Discovered from Plot Summaries and Social Media
Chien, C.-Y., Qiu, G.-H., & Lu, W. (2019) · Conference on Technologies and Applications of Artificial Intelligence (TAAI) · doi:10.1109/taai48200.2019.8959918 · Cited by 3
MACHINE LEARNING FOR TEXT CLASSIFICATION IN BUILDING MANAGEMENT SYSTEMS
Mesa-Jiménez, J. J., Stokes, L., Yang, Q., & Livina, V. (2022) · Journal of Civil Engineering and Management · doi:10.3846/jcem.2022.16012 · Cited by 3
Text Classification In The Wild: A Large-Scale Long-Tailed Name Normalization Dataset
Qi, J., Li, S., Guo, Z., Huang, Y., Zhou, C., Zhang, W., Wang, X., & Lin, Z. (2023) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp49357.2023.10096769 · Cited by 2
“The baselines used include Naive Bayes [10], sCool [11], CompanyDepot V1(CDv1) [12], and BERT [13].”
Method
Text Classification Based on the Heterogeneous Graph Considering the Relationships between Documents
Nakajima, H., & Sasaki, M. (2023) · Big Data and Cognitive Computing · doi:10.3390/bdcc7040181 · Cited by 4
BRsyn-Caps: Chinese Text Classification Using Capsule Network Based on Bert and Dependency Syntax
Luo, J., He, C., & Hongwei, L. U. O. (2024) · IEICE Transactions on Information and Systems · doi:10.1587/transinf.2023edp7119 · Cited by 3
GEML: a graph-enhanced pre-trained language model framework for text classification via mutual learning
Yu, T., Song, R., Pinto, S., Gomes, T., Tavares, A., & Xu, H. (2024) · Applied Intelligence · doi:10.1007/s10489-024-05831-1 · Cited by 3
“These features are then used in statistics-based classiﬁers like Naïve Bayes [30] and Support Vector Machine [13].”
A comparison of several classifiers for eye detection on emotion expressing faces
Boia, R., Dogaru, R., & Florea, L. (2013) · doi:10.1109/iseee.2013.6674313 · Cited by 3
“The Bayes classifiers were introduced [9], [10] as a simplification of the Bayes theorem.”
Method
Enhancing Educational Data Mining based ICT Competency among e-Learning Tutors using Statistical Classifier
Barik, L., AbdulQadir, A., & Difulah, Y. (2020) · International Journal of Advanced Computer Science and Applications · doi:10.14569/ijacsa.2020.0110371 · Cited by 3
“ICT skills are taken into account for the skillful usage of hardware and software, computer system association configuration [20].”
A Supervised Clustering Method for Text Classification
Pappuswamy, U., Bhembe, D., Jordan, P., et al. (2005) · Lecture notes in computer science · doi:10.1007/978-3-540-30586-6_78 · Cited by 3
Improving Persian Text Classification Using Persian Thesaurus
Parvïn, H., Bidgoli, B. M., & Dahbashi, A. (2011) · Lecture notes in computer science · doi:10.1007/978-3-642-25085-9_46 · Cited by 3
Adoption and evaluation issues of automatic and computer aided indexing systems
Ruiz, M. E., Aronson, A. R., & Hlava, M. M. K. (2008) · Proceedings of the American Society for Information Science and Technology · doi:10.1002/meet.2008.1450450143 · Cited by 3
“This area of research started has evolved since the 1960’s with Maron’s seminal work (Maron, 1961), through the 1970’s and 80’s development of rule based systems constructed by using knowledge engineering, to the use of machine learning in the 1990s and strong emphasis on creating standard test…”
Background
EVALUATION OF MACHINE LEARNING TECHNIQUES IN VINE LEAVES DISEASE DETECTION: A PRELIMINARY CASE STUDY ON FLAVESCENCE DORÉE
Hruška, J., Adão, T. O., Pádua, L., et al. (2019) · The international archives of the photogrammetry, remote sensing and spatial information sciences/International archives of the photogrammetry, remote sensing and spatial information sciences · doi:10.5194/isprs-archives-xlii-3-w8-151-2019 · Cited by 3
“Agriculture industry worldwide (Martinelli et al. 2015). In 1990s, new discipline called Precision viticulture was developed. It aims to adjust vineyard management to the spatial variability that is present in the field in order to make its economic and environmental sustainability more efficient (Santesteban 2019). D…”
Background
Classification of Medical Sensitive Data based on Text Classification
Jiang, H., Chen, C., Wu, S., et al. (2019) · IEEE International Conference on Consumer Electronics - Taiwan (ICCE-Taiwan) · doi:10.1109/icce-tw46550.2019.8991726 · Cited by 3
Improving measurements of similarity judgments with machine-learning algorithms
Stevens, J. R., Saltzman, A., Rasmussen, T., & Soh, L. (2021) · Journal of Computational Social Science · doi:10.1007/s42001-020-00098-1 · Cited by 2
GLAUDIA: A predicative system for glaucoma diagnosis in mass scanning
Omar, Y. M. K., Elsheikh, M., & Hodhod, R. (2021) · Health Informatics Journal · doi:10.1177/14604582211009276 · Cited by 3
Makine Öğrenmesi Yaklaşımlarının Spam-Mail Sınıflandırma Probleminde Karşılaştırmalı Analizi
BAKTIR, N., & Atay, Y. (2022) · Bilişim Teknolojileri Dergisi · doi:10.17671/gazibtd.1014764 · Cited by 3
Performance Assessment of Various Machine Learning Classification Methods for Classifying the Landcover Using Landsat 8 OLI
Sajan, A., & Manayath, D. (2023) · International Conference on Intelligent Technologies (CONIT) · doi:10.1109/conit59222.2023.10205822 · Cited by 3
“The NBC calculates the probability of each training sample that it is likely to belong to each category and assigns it to the category with the greatest probability [26] [27].”
Background
Discovering Travelers’ Purchasing Behavior from Public Transport Data
Branda, F., Marozzo, F., & Talia, D. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-64583-0_63 · Cited by 3
“The accuracy of our approach is evaluated comparing the most relevant machine learning algorithms used in the literature (Na¨ıve Bayes [10], Logistic Regression [16], Decision Tree [13], Random Forest [3]).”
Method
Integrating human and machine coding to measure political issues in ethnic newspaper articles
Kim, J. Y. (2021) · Journal of Computational Social Science · doi:10.1007/s42001-020-00097-2 · Cited by 3
ENVIRONMENT CHANGES DETECTION: A PROACTIVE SYSTEM TO MONITOR MOVING OBJECTS
Albuquerque, F. D. C. (2012) · doi:10.17771/pucrio.acad.29127 · Cited by 3
A BERT-Based model: Improving Crime News Documents Classification through Adopting Pre-trained Language ModelsPreprint
Ali, A., Noah, S. A. M., & Zakaria, L. Q. (2023) · Research Square · doi:10.21203/rs.3.rs-2582775/v1 · Cited by 7
An automatic text mining framework for knowledge discovery on the web
Chung, W., & Chen, H. (2004) · UA Campus Repository (The University of Arizona) · Cited by 3
Interpretable Machine Learning-Based Fraud Detection Model and Knowledge Discovery in Financial Transactions
Xu, W. (2025) · Learning and analytics in intelligent systems · doi:10.1007/978-3-031-99477-7_28 · Cited by 3
Textual, Non-Textual, and Hybrid Feature Engineering for SMS Spam Classification
-, A. R. K. V., & Sadana, S. (2025) · IEEE Access · doi:10.1109/access.2025.3620751 · Cited by 3
“For a binary feature vector x ∈ { 0 , 1 } d and class label y ∈ { 0 , 1 } (ham = 0, spam = 1), the model assumes conditional independence of the features given the class [45], [46].”
Automated extraction of scientific statements for integrated assessment of animal welfare using language models
Zhang, C., Torres, R. D. S., Gansel, L. C., & Bracke, M. B. M. (2026) · Smart Agricultural Technology · doi:10.1016/j.atech.2026.101814 · Cited by 2
Assessing the impact of news on oil prices - A text mining approach
Rad, A. F. (2009) · Cited by 2
Analysis of Turkish Sentiment Expressions About Touristic Sites Using Machine Learning
Aksu, M., & Karaman, E. (2021) · Journal of Intelligent Systems Theory and Applications · doi:10.38016/jista.854250 · Cited by 3
“It has been used in text classification studies since the early 1960s (Maron, 1961).”
Method
The Explanatory Gap in Algorithmic News Curation
Heuer, H. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-87031-7_1 · Cited by 2
“The Gaussian Na¨ıve Bayes classi-ﬁer is a supervised ML algorithm that applies Bayes’ theorem while assuming conditional independence between words [29].”
Method
Environment modeling and localization from datasets of omnidirectional scenes using machine learning techniques
Cebollada, S., Payá, L., Peidró, A., Mayol-Cuevas, W. W., & Reinoso, Ó. (2023) · Neural Computing and Applications · doi:10.1007/s00521-023-08515-y · Cited by 3
“This classification was introduced by Maron [32] as a method for text categorization to solve the problem of judging documents (such as spam or legitimate, sports or politics, etc.”
Method
Brain Computer Interface for Eye Movement Recognition Using Random Forest
Shilaskar, S., Chaudhari, N., Bhatlawande, S. S., Agharkar, S., & Jalnekar, R. M. (2023) · International Conference on Computational Intelligence and Sustainable Engineering Solutions (CISES) · doi:10.1109/cises58720.2023.10183409 · Cited by 2
“A strategy uses [9] Nave Bayes model and probability theory to categories data.”
Method
Development and prospects of machine learning methods in geographic elements classification
Juanle, W. A. N. G., Li, K., Xinrong, Y. A. N., Li, Z., & Xuehua, H. A. N. (2023) · National Remote Sensing Bulletin · doi:10.11834/jrs.20232299 · Cited by 2
Predicting Heart Disease: a Comprehensive Approach to Data Analysis and Machine Learning Model Development
Bojanić, U., & Bjelica, M. (2024) · IEEE International Conference on Bioinformatics and Bioengineering (BIBE) · doi:10.1109/bibe63649.2024.10820449 · Cited by 3
“• Na¨ıve Bayes (NB) : A probabilistic classifier based on Bayes’ theorem, assuming full feature independence [22].”
A Framework for Interrogating Social Media Images to Reveal an Emergent Archive of War
Pleska, A., Hoskins, A., & Renaud, K. (2019) · International Journal of Humanities and Arts Computing · doi:10.3366/ijhac.2019.0236 · Cited by 3
Методы машинного обучения в социологическом исследовании: предсказание частичного неответа с использованием наивного байесовского классификатора
Александрова, М. Ю. (2021) · Monitoring obŝestvennogo mneniâ: èkonomičeskie i socialʹnye peremeny · doi:10.14515/monitoring.2021.1.1756 · Cited by 2
Uma arquitetura hibrida para a indexação de documentos do Diário Oficial do Município de Cachoeiro de Itapemirim
Xavier, B. M., da Silva, A. D., & Gomes, G. R. R. (2015) · Transinformação · doi:10.1590/0103-37862015000100008 · Cited by 2
A Framework of Centroid-Based Methods for Text Categorization
Wang, D., Chen, Q., & Wang, X. (2014) · IEICE Transactions on Information and Systems · doi:10.1587/transinf.e97.d.245 · Cited by 2
“The study of text categorization should be traced back to the early 1960s [25].”
Background
Fast incremental indexing with effective and efficient searching in XML element retrieval
Keyaki, A., Miyazaki, J., Hatano, K., et al. (2013) · International Journal of Web Information Systems · doi:10.1108/ijwis-04-2013-0008 · Cited by 2
“We therefore use Zipf’s law [15] to obtain the threshold of median frequency f , which is computed as follows:”
Method
Prioritising Hyperlinks for Topic-Focused Web Crawling using Lexical and Terminological Profiling By
Greenwood, M. A. (2009) · Cited by 2
An adaptive document classifier inspired by T-cell cross-regulation in the immune system
Abi-Haidar, A. (2011) · Cited by 2
AN INTEGRATED KNOWLEDGE-BASED SYSTEM FOR REAL-TIME ESTIMATION OF INCIDENT DURATIONS AND NONRECURRENT CONGESTION DELAY FOR FREEWAY NETWORKS
Brown, A. G. A., Porcari, J. D., & Pedersen, N. J. (2009) · Cited by 2
An Approach for Extraction of Keywords and Weighting Words for Improvement Farsi Documents Classification
Rezaie, V., Mohammadpour, M., Parvïn, H., & Nejatian, S. (2018) · Signal and Data Processing · doi:10.29252/jsdp.14.4.55 · Cited by 2
Multinomial Naïve Bayes using similarity based conditional probability
Santhi, B., & R, B. G. (2019) · Journal of Intelligent & Fuzzy Systems · doi:10.3233/jifs-181009 · Cited by 2
Natural Language Processing for Company Financial Communication Style
Askerov, R., Kwon, E., Song, L., Weber, D., Schaer, O., Dadgostari, F., & Adams, S. (2020) · Systems and Information Engineering Design Symposium · doi:10.1109/sieds49339.2020.9106636 · Cited by 2
“The models that were used are Naïve Bayes Classifier and Support Vector Machines (SVM) with tuned hyperparameters [18, 6].”
Method
Machine Learning Methods in Medicine Diagnostics Problem
Strilets, V., Bakumenko, N., Donets, V., Chernysh, S., Ugryumov, M., & Goncharova, T. (2020) · International Conference on ICT in Education, Research and Industrial Applications: Integration, Harmonization and Knowledge Transfer (ICTERI) · doi:10.55056/ceur-ws.org/vol-2732/20200089.pdf · Cited by 2
“Naïve Bayes method developments started in the distant 1960s and are still a popular method of text categorization (e.g. a scientific text, a fiction literature text, spam etc.) [1].”
Method
Engines of Order
Rieder, B. (2021) · Routledge eBooks · doi:10.5117/9789462986190_ch01 · Cited by 2
Advanced Parametric Mixture Model for Multi-Label Text Categorization
Kao, T.-H. (2006) · Cited by 2
“Co-occurrence Relation” and “Ingredient Category” Recommend Alternative-Ingredients
Shino, N., Yamanishi, R., & Fukumoto, J. (2018) · Studies in computational intelligence · doi:10.1007/978-3-319-70636-8_1 · Cited by 2
Binary Credal Classification Under Sparsity Constraints
Basu, T., Troffaes, M. C. M., & Einbeck, J. (2020) · Communications in computer and information science · doi:10.1007/978-3-030-50143-3_7 · Cited by 2
A Proposed Model for Dimensionality Reduction to Improve the Classification Capability of Intrusion Protection Systems
Elkassabi, H., Ashour, M. M., & Zaki, F. W. (2020) · International Journal of Network Security & Its Applications · doi:10.5121/ijnsa.2020.12402 · Cited by 2
“Naïve Bayes classifier considered a supervised machine learning algorithm works on the principle of conditional probability as given by the Bayes theorem [28].”
Method
Big data analytics and process safety
Valeev, S., & Kondratyeva, N. (2021) · Elsevier eBooks · doi:10.1016/b978-0-12-822066-5.00001-7 · Cited by 2
Ramp Closure Strategies for Incident Management
Waller, S. T., Boyles, S. D., Fajardo, D., et al. (2007) · Cited by 2
Automatic role recognition
Salamin, H. (2013) · Enlighten: Theses (The University of Glasgow) · Cited by 2
MECHANIZED DOCUMENT CONTROL
Lancaster, F. W. (1964) · Aslib Journal of Information Management · doi:10.1108/eb049960 · Cited by 2
Analyse des sentiments : système autonome d'exploration des opinions exprimées dans les critiques cinématographiques
Dziczkowski, G. (2008) · HAL · Cited by 2

Authors: Maron, M. E.
Affiliations: RAND Corporation (US)
Corresponding author: Maron, M. E.
Venue: Journal of the ACM
Publisher: Association for Computing Machinery (ACM)
Published: 1961-07
Volume: 8
Issue: 3
Pages: 404-417
ISSN: 0004-5411 (ISSN-L)
1557-735X
DOI: 10.1145/321075.321084
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
Licence: www.acm.org/publications/policies/copyright_policy#Background (vor)
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

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Automatic Indexing: An Experimental Inquiry

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