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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.
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“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]
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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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Computer-aided Document Indexing System
Kolar, M., Vukmirovi�, I., Ba�i�, B. D., et al. (2005) · Journal of Computing and Information Technology · doi:10.2498/cit.2005.04.07 · Cited by 2
“Maron examined a technique for classifying automatically documents according to their subject contents [ 7 ].”
MethodQuality-oriented information retrieval in a health domain
Tang, T. T. (2007) · ANU Open Research (Australian National University) · doi:10.25911/5d5e752abbe23 · Cited by 2
A Study of Machine Learning Models in Predicting the Intention of Adolescents to Smoke CigarettesPreprint
Nam, S. J., Kim, H.-M., Kang, T., et al. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1910.12748 · Cited by 2
“Gaussian Naive Bayes Classifier Naive Bayes (NB) Classifier [24] is a classification method based on probability theory, by which one can classify class labels using (discrete or continuous) inputs.”
MethodMIRAI: A Modifiable, Interpretable, and Rational AI Decision Support System
Mallia, N., Dingli, A., & Haddod, F. (2021) · Studies in computational intelligence · doi:10.1007/978-3-030-61045-6_10 · Cited by 2
Text classification method based on Bi-LSTM+FE-Attention model
Wu, J., & Li, N. (2021) · IEEE Information Technology, Networking, Electronic and Automation Control Conference (ITNEC) · doi:10.1109/itnec52019.2021.9587252 · Cited by 1
“Attention mechanism (1)Encoder-Decoder Model”
BackgroundMethodInfluentialSecureSem: Sensitive Text Classification Based on Semantic Feature Optimization
Qin, K., Liu, R., Yu, M., et al. (2025) · Lecture notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering · doi:10.1007/978-3-031-89363-6_17 · Cited by 1
Improving course evaluation processes in higher education institutions: a modular system approach
Kocaoğlu, İ., & Karataş, E. (2025) · PeerJ Computer Science · doi:10.7717/peerj-cs.3110 · Cited by 2
“It is commonly used in applications such as text classi fi cation and spam fi ltering ( Maron, 1961 ).”
Optimising Image Feature Extraction and Selection: A Comprehensive Review With Spark Case Studies
Figueira-Domínguez, J. G., Remeseiro, B., & Bolón‐Canedo, V. (2026) · Expert Systems · doi:10.1111/exsy.70188 · Cited by 1
“Preliminary experiments were carried out using Logistic Regression (LR) (Hosmer and Lemeshow 2000), Support Vector Classifiers (SVC) (Cristianini and Shawe-Taylor 2000), Decision Trees (Sheppard 2017), Naive Bayes (Nir Friedman and Goldszmidt 1997) and Multilayer Perceptron (MLP) (Vang-Mata 2020) models.”
Leveraging LLM for Enhanced Incident Management in Wireless Networks
Towhid, M. S., Khan, N. S., Shahriar, N., Tornatore, M., Boutaba, R., & Saleh, A. (2026) · IEEE Journal on Selected Areas in Communications · doi:10.1109/jsac.2026.3680419 · Cited by 2
“Conventional supervised learning approaches, including Na¨ıve Bayes, k-Nearest Neighbors, and Support Vector Machines [2], [3], [4], require extensive manual feature engineering and struggle to capture the semantic dependencies between network components, services, and failure modes described in…”
Classifying Digital Resources in a Practical and Coherent Way with Easy-to-Get Features
Chen, C., Yan, H., & Li, X. (2008) · Lecture notes in computer science · doi:10.1007/978-3-540-89447-6_18 · Cited by 1
“argmaxP(Ci|r) [11].”
Algunas Técnicas de Clasificación Automática de Documentos
Figuerola, C. G., Berrocal, J. L. A., Rodríguez, Á. F. Z., et al. (2005) · Complutensian Scientific Journals (Complutense University of Madrid) · doi:10.5209/cdmu.59079 · Cited by 1
Pengelasan Dokumen Web di Bursa Malaysia Menggunakan Pendekatan Support Vector Machine (SVM)
Othman, M. S., Yusuf, L. M., Salim, J., & Shukur, Z. (2009) · Cited by 1
The Chinese Text Categorization System with Category Priorities
Keh, H., Chiang, D.-A., Hsu, C., et al. (2010) · Journal of Software · doi:10.4304/jsw.5.10.1137-1143 · Cited by 1
“For example, Maron [10] took Transaction on Electronic Computer periodicals as a document source, and selected abstracts as document profiles.”
MethodInfluentialMourisse, D., Lefever, E., Nele Verbiest, A., et al. (2012) · Lecture notes in computer science · doi:10.1007/978-3-642-32790-2_30 · Cited by 1
Fast and incremental indexing in effective and efficient XML element retrieval systems
Keyaki, A., Miyazaki, J., Hatano, K., et al. (2012) · International Conference on Information Integration and Web-based Applications & Services (iiWAS) · doi:10.1145/2428736.2428763 · Cited by 1
“To identify informative path expressions, we use Zipf’s law [11] to get the threshold number of middle frequency f , which is computed as follows:”
MethodThe Effect of Term Importance Degree on Text Retrieval
Karbasi, S., & Yaghoubi, M. (2012) · International Journal of Computer Applications · doi:10.5120/4573-6734 · Cited by 1
“In general, term-weighting schemes are based on statistical [13, 9], semantic [15] or probabilistic models [10].”
BackgroundRough Set Techniques for Text Classification and Sentiment Analysis in Social Media
Panda, G. K., Mondal, J., & Vihar, S. (2015) · Cited by 1
Maschinelle Analyse natürlicher Sprachen
Krallmann, D. (1968) · Springer eBooks · doi:10.1007/978-3-7091-8202-4_14 · Cited by 1
Nicola, V., Lauretto, M. D. S., & Delgado, K. V. (2020) · doi:10.5753/eniac.2020.12118 · Cited by 1
“O naı̈ve Bayes teve seu F-Score ótimo na configuração PK sob o conjunto completo de atributos.”
BackgroundInfluentialA 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. (2010) · Advances in data mining and database management book series · doi:10.4018/978-1-60960-067-9.ch011 · Cited by 1
News text classification based on multi-head attention and parallel capsule networks
Wang, D., & Peng, P. (2022) · International Conference on Artificial Intelligence and Intelligent Information Processing (AIIIP) · doi:10.1117/12.2659349 · Cited by 1
“Faced with this exponentially growing amount of news text information, it would be very time-consuming and labor-intensive to sort and classify these data by hand alone, and it is difficult for machine methods represented by plain Bayesian[1], K-nearest neighbor [2] and support vector machines [3] to handle complex te…”
Revathi, B. S., & Meena, K. A. (2022) · i-manager’s Journal on Image Processing · doi:10.26634/jip.9.3.19054 · Cited by 1
Big Data, Good Data, and Residential Floor Plans
Bielik, M., Zhang, L., & Schneider, S. O. (2023) · Communications in computer and information science · doi:10.1007/978-3-031-37189-9_40 · Cited by 1
Mahato, M., Kumar, A., Reddy, V. K. K., & Nabi, J. (2023) · IEEE Women in Technology Conference (WINTECHCON) · doi:10.1109/wintechcon58518.2023.10277260 · Cited by 1
“Examples of these models are Naïve Bayes (NB) [2], K-Nearest Neighbour (KNN) [3], and Support Vector Machine (SVM) [4].”
MethodDeepText: Pioneering the Future of Text Classification with Innovative Deep Learning Techniques
Prathyakshini, & Shetty, J. (2024) · International Conference on Electronics and Sustainable Communication Systems (ICESC) · doi:10.1109/icesc60852.2024.10689751 · Cited by 1
Machine Learning in Automated Text Categorization: a Bibliography
Sebastiani, F., Lockemann, P. C., & Scholl, M. H. (2003) · Cited by 1
Automatic Keyword Annotation System Using Newspapers
Takada, T., Arai, M., & Takagi, T. (2014) · Journal of Advanced Computational Intelligence and Intelligent Informatics · doi:10.20965/jaciii.2014.p0340 · Cited by 1
Kuffò, L., Vaca, C., Izquierdo, E., et al. (2018) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata.2018.8622556 · Cited by 1
“In 1961, Maron’s [8] presented one of the very first definitions of text categorization: Automatically deciding to which of a group of categories a document (i.e. a piece of text) belongs to.”
BackgroundImage-based effective feature generation for protein structural class and ligand binding prediction
Sadique, N., Ahmed, A. A. N., Islam, M. T., Pervage, M. N., & Shatabda, S. (2020) · PeerJ Computer Science · doi:10.7717/peerj-cs.253 · Cited by 1
Chorna, О. V., Lyushenko, L. A., & Rybachok, N. A. (2019) · Control Systems and Computers · doi:10.15407/usim.2019.02.032 · Cited by 1
Forecasting with Multivariate Fuzzy Time Series: A Statistical Approach
Bose, M. M., & Mali, K. (2020) · Advances in intelligent systems and computing · doi:10.1007/978-981-15-1366-4_20 · Cited by 1
Variably Scaled Kernels Improve Classification of Hormonally-Treated Patient-Derived Xenografts
Marchetti, F., Martino, F. D., Shamseddin, M., et al. (2020) · IEEE Conference on Evolving and Adaptive Intelligent Systems (EAIS) · doi:10.1109/eais48028.2020.9122767 · Cited by 1
“In our experiments, we compute these probabilities by means of three different classification methods, which are a Naive Bayes (NB) classifier [16], a K-Nearest Neighbors (KNN) algorithm [1] and a Random Forest (RF) [4].”
MethodData-Driven Approach To Patient Flow Management And Resource Utilization In Urban Medical Facilities
Prokofyeva, E. S., Maltseva, S., Fomichev, N. Y., et al. (2020) · IEEE Conference on Business Informatics · doi:10.1109/cbi49978.2020.10060 · Cited by 1
Online Sequential Extreme Learning Machine based Instinct Plasticity for Classification
Liu, Z., & Pasupa, K. (2020) · doi:10.1109/icitee49829.2020.9271686 · Cited by 1
“Many classical algorithms, such as Logistic Regression [1], Na¨ıve Bayes [2], Fisher’s Discriminant Analysis [3], Support Vector Machine [4], k - nearest neighbor [5], were designed to solve the binary and multi-class classification problems.”
MethodJ-Net: An Adaptive System for Computer-Aided Diagnosis in Lung Nodule Characterization
Buscema, M., Passariello, R., Grossi, E., et al. (2012) · Springer eBooks · doi:10.1007/978-1-4614-4223-3_2 · Cited by 1
“3All the experimentations with Naive Bayes algorithm were executed by Massimiliano Marciano (software engineer at CSI Research and Innovation, via Cesare Pavese 305, Rome, Italy), using Rapid Miner ver.”
InfluentialDictionary Requirements for Text Classification: A Comparison of Three Domains
Rfloff, E. (2002) · Cited by 1
On the expected gain from adjusting matched term retrieval systems
Shumway, R. H. (1967) · Communications of the ACM · doi:10.1145/363790.363821 · Cited by 1
A Feature Selection and Classification Technique for Text Categorization
Girgis, M. R., & Aly, A. A. (2003) · International Journal of Cooperative Information Systems · doi:10.1142/s0218843003000826 · Cited by 1
Roy, D. M. (2006) · DSpace@MIT (Massachusetts Institute of Technology) · Cited by 1
Automatic processing of Japanese text data based on the occurrence frequency distributions of Kanji.
Hosono, K., Harada, T., UMEDA, S., et al. (1986) · Journal of Information Processing and Management · doi:10.1241/johokanri.29.410 · Cited by 1
Abstract of the Master's Thesis
Virhiä, T., Hämmäinen, H., & Hämäläinen, M. (2010) · Cited by 1
Latent-Variable Generative Models for Data-Efficient Text ClassificationPreprint
Ding, X., & Gimpel, K. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1910.00382 · Cited by 1
“…models have traditionally been used in supervised settings for many NLP tasks, including naive Bayes and other models for text classi-fication (Maron, 1961; Yogatama et al., 2017), Markov models for sequence labeling (Church, 1988; Bikel et al., 1999; Brants, 2000; Zhou and Su, 2002), and…”
MethodComparing the utility of user-level and kernel-level data for dynamic malware analysis
Nunes, M. (2019) · ORCA Online Research @Cardiff (Cardiff University) · Cited by 1
Cross-project Classification of Security-related RequirementsPreprint
Mohamad, M., Steghöfer, J.-P., & Scandariato, R. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2003.14134 · Cited by 1
“(Linear SVM) [29], and Naive Bayes (NB) [30].”
Discussing Privacy and Surveillance on Twitter: A Case Study of COVID-19Preprint
Dev, J. (2020) · arXiv (Cornell University) · doi:10.13140/rg.2.2.14162.38083 · Cited by 1
“In order to answer the second research question, I performed a sentiment analysis of tweets using Python’s using a naive Bayes classification approach (previously known as automatic indexing [8]).”
MethodSocial Media Driven Public Health Informatics: Applications in Regulatory Science
Zhan, Y. (2019) · UA Campus Repository (The University of Arizona) · Cited by 1
Bai, N., Luo, R., Nourian, P., & Roders, A. P. (2021) · arXiv (Cornell University) · Cited by 1
Amati, G. (2009) · Encyclopedia of Database Systems · doi:10.1007/978-0-387-39940-9_920 · Cited by 1
Automated classification of medical text reports
Wilcox, A. B. (2000) · Cited by 1
Sentiment-Driven Topic Analysis Of Song Lyrics
Sharma, G. K. (2015)
What Theories of Political Participation Can Teach Us about the Blogosphere, and Vice Versa.
Gong, W. A. (2013) · Deep Blue (University of Michigan)
Citationfinder: a knowledge-based system for web pages of citations classification
Neves, M. L., & Barros, F. D. A. (2002) · Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics · doi:10.1109/icsmc.2001.969852
“The term selection was accomplished with three different techniques: the mutual information [13], the coefficient correlation [14] and the 9 [14].”
MethodUse of machine learning techniques for SNP based prediction of ancestry
Allocco, D. J. (2006) · DSpace@MIT (Massachusetts Institute of Technology)
Hybrid DIAAF/RS: Statistical Textual Feature Selection for Language-Independent Text Classification
Wang, Y. J., Li, F., Coenen, F., et al. (2010) · Lecture notes in computer science · doi:10.1007/978-3-642-14400-4_18
“TC, as a well established research filed, has been studied for almost half a century; early work on TC can be dated back to the 1960s (see for instance [ 21 ]).”
BackgroundData Mining and the Text Categorization Framework
Cerchiello, P. (2009) · IGI Global eBooks · doi:10.4018/978-1-60566-010-3.ch062
Machine learning approaches to text representation using unlabeled data
Mikaela Keller, A. (2008) · Infoscience (Ecole Polytechnique Fédérale de Lausanne) · doi:10.5075/epfl-thesis-3676
Un clasificador de texto por aprendizaje
Pelaez, J. D., Martínez, D. L. L. R., & Sánchez, P. (2002) · INTELIGENCIA ARTIFICIAL · doi:10.4114/ia.v6i15.756
“A continuación se muestran los filtros utilizados para realizar la reducción de dimensionalidad.”
BackgroundClapp, L. C. (1962) · ACM National Meeting · doi:10.1145/800198.806140
The computer-stored thesaurus and its use in concept processing
Shepherd, C. A. (1963) · AFIPS Fall Joint Computer Conference (FJCC) · doi:10.1145/1463822.1463863
A New Method of Class Centriod Vectors Classification Based on the Feedback
Li, W., Xing, C., Tiejun, Z., et al. (2012) · International Conference on Computer Distributed Control and Intelligent Environmental Monitoring · doi:10.1109/cdciem.2012.20
Memorizzazione e ricerca delle informazioni
Sozzi, V. (1966) · CALCOLO · doi:10.1007/bf02575693
On-line index term predictions using bigram-term associations
Rickman, J. T., & Gardner, H. W. (1973) · ACM Annual Conference · doi:10.1145/800192.805715
A Web Text Classification Rules Extraction Algorithm
Liu, H., Liu, D., & Shi, X. (2008) · International Conference on Natural Computation (ICNC) · doi:10.1109/icnc.2008.231
“Text classification technology has developed in the early 1960s by Maron [3].”
BackgroundA Link Structure Based Website Topic Hierarchy Extracting Approach
Xu, Z., Chen, Q., & Guo, H. (2008) · Joint Conference on Information Sciences (JCIS) · doi:10.2991/jcis.2008.98
“[9] M.”
Tensor Decompositions for Probabilistic Classification
van Gerven, M. A. J. (2007)
A Predictive Model for the Parallel Processing of Digital Libraries
Thompson, J. M. (2015) · Research Commons (University of Waikato)
Rapid pre-indexing by machine.
Kampe, W. R. (1968)
Automatic subject classification of textual documents using limited or no training data
Joorabchi, A. (2010) · University of Limerick Institutional Repository (University of Limerick) · doi:10.34961/4103
Classification of travertine plates with image processing
Metlek, S., Çetişli, B., & Senel, A. F. (2015) · Signal Processing and Communications Applications Conference (SIU) · doi:10.1109/siu.2015.7130129
Some Practical Randomized Algorithms and Data Structures
Upfal, E. (2014) · Chapman & Hall/CRC eBooks · doi:10.1201/b16812-14
Thesaurus-Based Automatic Indexing
de Campos, L. M. (2009) · IGI Global eBooks · doi:10.4018/978-1-59904-990-8.ch020
Development of a Content Based Recommender Using Dynamic Artificial Neural Network
Islam, M. Z., Naznin, F., & Zaman, A. (2015) · IJARCCE · doi:10.17148/ijarcce.2015.41153
“Maron first introduced the topic [2].”
Rzepka, R., Amaya, Y., Yatsu, M., & Araki, K. (2015) · Hokkaido University Collection of Scholarly and Academic Papers (Hokkaido University)
Towards the Improvement of Automated Scientific Document Categorization by Deep LearningPreprint
Krause, T. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1706.05719
“3 Structure of Text Classification Algorithms According to Sebastiani (2002) the automated text categorization was first described by Maron (1961). This first attempt of text categorization used a probabilistic model and a so called bag-of-words approach that is still very popular today.”
MethodInfluentialAutomatic Evaluation of Employee Satisfaction
Piersanti, M., Brandetti, G., & Failla, P. (2017) · Accademia University Press eBooks · doi:10.4000/books.aaccademia.2444
“The automated categorization of texts, although dating back to the early ’60s (Maron, 1961; Borko and Bernick, 1963), went through a booming interest in the last twenty years, due to the explosion of the amount of documents available in digital form and the impelling need to organize them.”
BackgroundSimões, M. D. G., Machado, L. M. O., Souza, R. R., et al. (2017) · Ciência da Informação · doi:10.18225/ci.inf.v46i1.4020
California at Berkeley in the 1960s: A Memoir of Student Days
Bates, M. J. (2004)
Shino, N., Yamanishi, R., & Nishihara, Y. (2018) · Journal of Japan Society for Fuzzy Theory and Intelligent Informatics · doi:10.3156/jsoft.30.6_779
Modelling text meta-properties in automated text scoring for non-native English writing
Zhang, M. (2018) · Apollo (University of Cambridge) · doi:10.17863/cam.41820
“Bayes classifier (Maron, 1961) to predict whether an incoming essay was good or bad, and the k-nearest-neighbour classifier (Altman, 1992) to gauge the similarity between the incoming essay and all the low-quality and well-written essays in the training set.”
BackgroundUso de técnicas de machine learning para realizar mapping en robótica móvil
Cebollada, S., Román, V., Payá, L., Tenza, M. F., Jiménez, L. M., & Reinoso, Ó. (2019) · Jornadas de Automática · doi:10.17979/spudc.9788497497169.686
“[8] M. E. Maron.”
BackgroundInfluentialKim, H.-Y., Choi, B.-H., Oh, T., & Kang, B.-C. (2019) · Journal of Agriculture & Life Science · doi:10.14397/jals.2019.53.4.93
Combining Free Collaborative and Controlled Annotation in Social Media
Marquez, I. G., Nazarenko, A., & Lévy, F. (2019) · International Conference on Social Networks Analysis, Management and Security (SNAMS) · doi:10.1109/snams.2019.8931844
“We tested Support vector machines (SVM) with a linear kernel [1], Multinomial Naive Bayes (NB) [2], K nearest neighbour with K=5 (5NN) [3],”
MethodA Hybrid Approach for Large-Scale Product Categorization Based on Weighted KNN and LSTM-BPV
Hu, H. (2019) · YorkSpace (York University)
Projeto de um Sistema Web a Classificação de Fake News
Monteiro, R., & Nogueira, R. R. (2019) · Congresso Latino-Americano de Software Livre e Tecnologias Abertas (Latinoware) · doi:10.5753/latinoware.2019.10343
Quantitative Analysis of Post-War Japanese Prime Ministers' Diet Speeches
Kawase, A., & YOSHIHARA, H. (2020) · Joho Chishiki Gakkaishi · doi:10.2964/jsik_2020_019
An Idea of a Rough Set Theory Based Document Classification System
Kurematsu, M. (2020) · Journal of Japan Society for Fuzzy Theory and Intelligent Informatics · doi:10.3156/jsoft.32.4_778
“また,文書分類課題の代表的な手法であるナイーブベイズ分類 器 [8](以後,NBCと略記)との比較を行う.”
Predictive Technique Of Task Scheduling For BigData In Cloud
Sarraiu, S., & Bien, F. D. (2020) · International Conference on Emerging Smart Computing and Informatics (ESCI) · doi:10.1109/esci48226.2020.9167590
A Distance-Weighted Class-Homogeneous Neighbourhood Ratio for Algorithm Selection
Chen, H., Ya, L., Ahuja, J. K., & Ler, D. (2020) · Asian Conference on Machine Learning
Corpus-Based Methods for Recognizing the Gender of Anthroponyms
Nazar, R., Renau, I., Acosta, N., et al. (2020) · Names · doi:10.1080/00277738.2020.1841467
Rieder, B. (2021) · Routledge eBooks · doi:10.5117/9789462986190_ch05
Machine learning approach for the binary classification of biomedical literaturePreprint
Price, A., Mort, M. E., Cooper, D. N., et al. (2020) · doi:10.21203/rs.3.rs-16326/v1
Atribuição autoral com uso de classificadores de perfis
Deutsch, C. (2020) · doi:10.11606/d.100.2020.tde-04112020-152132 · Cited by 1
Revathi, B. S., & Kowshalya, A. M. (2021) · Research Square · doi:10.21203/rs.3.rs-618868/v1
Product Classification Using Partially Abbreviated Product Names, Brands and Dimensions
Allweyer, O., Schorr, C., Mohr, A., & Krieger, R. (2021) · Communications in computer and information science · doi:10.1007/978-3-030-83014-4_11
Nicholson, J. M., Mordaunt, M., Lopez, P., et al. (2021) · Publons reviews and discussion · doi:10.1162/qss_a_00146/v2/response1
Histopathology Image Analysis and NLP for Digital Pathology
Allada, A. A. (2021) · UWSpace (University of Waterloo)
Gadasina, L. V., Veklenko, V., & Luukka, P. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-91434-9_30
Rieder, B. (2020) · Amsterdam University Press eBooks · doi:10.1515/9789048537419-008
“An index where each retained word received a relevance value for each category was then generated, determining ‘certain probability relationships between individual content-bearing words and subject categories’ (Maron, 1961, p. 405).”
Influential
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.
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