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WordNet: a lexical database for English
Open accessAbstract
Because meaningful sentences are composed of meaningful words, any system that hopes to process natural languages as people do must have information about words and their meanings. This information is traditionally provided through dictionaries, and machine-readable dictionaries are now widely available. But dictionary entries evolved for the convenience of human readers, not for machines. WordNet 1 provides a more effective combination of traditional lexicographic information and modern computing. WordNet is an online lexical database designed for use under program control. English nouns, verbs, adjectives, and adverbs are organized into sets of synonyms, each representing a lexicalized concept. Semantic relations link the synonym sets [4].
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“Hierarchical relations are prevalent and indispensable for organizing human knowledge captured by a knowledge graph (KG).”
From Modeling Heterogeneous Hierarchies with Relation-specific Hyperbolic Cones (2021) · cites this paper as [8]
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“In fact, knowledge about hierarchical relations (i.e., transitive closure) is explicitly available in the definition of the relation in KGs such as ConceptNet [28], WordNet [8] and Gene Ontology [9].”
From Modeling Heterogeneous Hierarchies with Relation-specific Hyperbolic Cones (2021) · cites this paper as [8]
The cited source says what the claim attributes to it.
In the citing paper
“WN18RR is a subset of WordNet [8], which features lexical relationships between word senses.”
From Modeling Heterogeneous Hierarchies with Relation-specific Hyperbolic Cones (2021) · cites this paper as [8]
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Citations
Cites
Introduction to WordNet: An On-line Lexical Database *
Miller, G. A., Beckwith, R., Fellbaum, C., Gross, D., & Miller, K. J. (1990) · International Journal of Lexicography · doi:10.1093/ijl/3.4.235 · Cited by 4,197
Self-citationFREQUENCY ANALYSIS OF ENGLISH USAGE: LEXICON AND GRAMMAR
Francis, W. N., Kučera, H., & Mackie, A. W. (1983) · Medical Entomology and Zoology · Cited by 2,310
Contextual correlates of semantic similarity
Miller, G. A., & Charles, W. G. (1991) · Language and Cognitive Processes · doi:10.1080/01690969108406936 · Cited by 1,634 · cited 1×
“Miller and Charles [ 5 ] proposed that a contextual representation associated with each sense characterizes sentential contexts in which a given word can be used to express that sense.”
BackgroundSelf-citationMiller, G. A., Leacock, C., Tengi, R., & Bunker, R. T. (1993) · DARPA Workshop on Speech and Natural Language (HLT) · doi:10.3115/1075671.1075742 · Cited by 612 · cited 1×
“Semantic concordances are being prepared to provide a basis for empirical studies of sense identification [7].”
BackgroundSelf-citationMiller, G. A., & Fellbaum, C. (1991) · Cognition · doi:10.1016/0010-0277(91)90036-4 · Cited by 452 · cited 1×
“The semantic relations in WordNet [6] were chosen because they apply broadly throughout English and because they are familiar— a user need not have advanced training in linguistics to understand them.”
MethodSelf-citationTowards Building Contextual Representations of Word Senses Using Statistical Models
Leacock, C., Towell, G. G., & Voorhees, E. M. (1993) · Cited by 29
The categorization of sentential contexts
Charles, W. G. (1988) · Journal of Psycholinguistic Research · doi:10.1007/bf01067226 · Cited by 12 · cited 1×
“People are quite skillful in making such distinctions [1].”
Background
Research works printed but not linked: 3
- Francis W. N., 1982, Mass.
- Leacock C., 1993, Proceedings of the Workshop on the Acquisition of Lexical Knowledge from Text, 10
- Miller G. A., 1992, B. Levin and, 197
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“Experimental setup: We provided Pluto with a set of data points to look for, enhanced with the meaning—sense id of the data point in Wordnet’s dictionary—and the class of the data point (i.e., user attribute or user interest).”
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“In particular, various natural language resources, such as WordNet (Miller, 1995) and FrameNet (Baker, Fillmore, & Lowe, 1998) are to a substantial degree representations of commonsense knowledge.”
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“The ILSVRC has been running annual competitions since 2010, backed by the ImageNet dataset which contains over 14 million images with various associated metadata, organised to follow the paradigms of the popular WordNet dataset [3].”
MethodWhen Do You Need Billions of Words of Pretraining Data?
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“Miller (1995); Schuler (2005), RTE from Dagan et al. (2006); Bar Haim et al. (2006); Giampiccolo et al. (2007); Bentivogli et al. (2009) Results We plot the results on SuperGLUE in Figure 6.”
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“The instructions then continued with: (1) a refresher on parts of speech, (2) the format of the event representation, (3) examples of events and their corresponding sentences, (4) resources on WordNet and VerbNet with details on how to use both, and (5) additional general guidelines and unusual cases they might encoun…”
BackgroundMMKRL: A robust embedding approach for multi-modal knowledge graph representation learning
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BackgroundCausal Relation Extraction Using Cue Phrase and Lexical Pair Probabilities
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“The idea of using random walk with restart has been applied on graphs constructed from the WordNet [19], with the stationary distribution to represent the semantics of words.”
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“To obtain the missing VAD scores or emotion categories of a word, we use the WordNet database [23] to find its synonyms and check if the synonyms appear in the other lexicon.”
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“Second, we combine Wikipedia and WordNet [53], which is a lexical database for the English language, containing 155,327 words organized in 175,979 synsets.”
MethodInfluentialA survey on the techniques, applications, and performance of short text semantic similarity
Han, M., Zhang, X., Yuan, X., Jiang, J., Yun, W., & Gao, C. (2020) · Concurrency and Computation Practice and Experience · doi:10.1002/cpe.5971 · Cited by 89
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“WordNet [24] is one of the widely used lexical ontologies.”
MethodZero-Shot and Few-Shot Learning With Knowledge Graphs: A Comprehensive Survey
Chen, J., Geng, Y., Chen, Z., Pan, J. Z., He, Y., Zhang, W., Horrocks, I. R., & Chen, H. (2023) · Proceedings of the IEEE · doi:10.1109/jproc.2023.3279374 · Cited by 90
“Each image is labeled with one class, each class is matched to a WordNet [31] entity, and the class hierarchies from”
BackgroundMethodInfluentialExploring Large Language Models for Knowledge Graph Completion
Yao, L., Peng, J., Mao, C., & Luo, Y. (2025) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp49660.2025.10889242 · Cited by 162
“Large knowledge graphs (KG) like FreeBase [1], YAGO [2], and WordNet [3] serve as a powerful foundation for numerous critical AI tasks, including semantic search, recommendation [4], and question answering [5].”
An Algorithm for Merging and Aligning Ontologies: Automation and Tool Support
Noy, N. F., & Musen, M. A. (1999) · Cited by 80
A geo-service semantic integration in Spatial Data Infrastructures
Vaccari, L., Shvaiko, P., & Marchese, M. (2008) · Iris (University of Trento) · doi:10.2902/1725-0463.2009.04.art2 · Cited by 80
Probabilistic Optimization of Semantic Process Model Matching
Leopold, H., Niepert, M., Weidlich, M., Mendling, J., Dijkman, R., & Stuckenschmidt, H. (2012) · Lecture notes in computer science · doi:10.1007/978-3-642-32885-5_25 · Cited by 79
Semantic Web service discovery: state-of-the-art and research challenges
Ngan, L. D., & Kanagasabai, R. (2012) · Personal and Ubiquitous Computing · doi:10.1007/s00779-012-0609-z · Cited by 79
Volkova, S., Wilson, T., & Yarowsky, D. (2013) · Cited by 79
Language-based colorization of scene sketches
Zou, C., Mo, H., Gao, C., Du, R., & Fu, H. (2019) · ACM Transactions on Graphics · doi:10.1145/3355089.3356561 · Cited by 86
“In the future, the part-level information can be annotated from the Word-Net [Miller 1995] while the color information can be computed as a linear mixture of several known colors.”
BackgroundImitation Attacks and Defenses for Black-box Machine Translation Systems
Wallace, E., Stern, M., & Song, D. (2020) · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2020.emnlp-main.446 · Cited by 75
Learning to Generate Scene Graph from Natural Language Supervision
Zhong, Y., Shi, J., Yang, J., Xu, C., & Li, Y. (2021) · 2021 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00184 · Cited by 73
Studying Complex Discursive Systems..: Centering Resonance Analysis of Communication
Corman, S. R., Kuhn, T., Mcphee, R. D., & Dooley, K. J. (2002) · Human Communication Research · doi:10.1111/j.1468-2958.2002.tb00802.x · Cited by 274
The cascading neural network: building the Internet of Smart Things
Leroux, S., Bohez, S., De Coninck, E., Verbelen, T., Vankeirsbilck, B., Simoens, P., & Dhoedt, B. (2017) · Knowledge and Information Systems · doi:10.1007/s10115-017-1029-1 · Cited by 59
“The ImageNet dataset [4] contains millions of images, organized following the WordNet [43] hierarchy.”
MethodA Conversational Robot for Older Adults with Alzheimer's Disease
Pou-Prom, C., Raimondo, S., & Rudzicz, F. (2020) · ACM Transactions on Human-Robot Interaction · doi:10.1145/3380785 · Cited by 59
“Semantic features were derived from WordNet [26].”
BackgroundInfluentialGO FIGURE: A Meta Evaluation of Factuality in Summarization
Gabriel, S., Celikyilmaz, A., Jha, R., Choi, Y., & Gao, J. (2021) · Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021 · doi:10.18653/v1/2021.findings-acl.42 · Cited by 75
“To change a random adjective, we use WordNet (Miller, 1995) to obtain the synsets for that adjective and swap the adjective for its antonym.”
MethodSaalasti, S., Alho, J., Bar, M., Glerean, E., Honkela, T., Kauppila, M., Sams, M., & Jääskeläinen, I. P. (2019) · Brain and Behavior · doi:10.1002/brb3.1288 · Cited by 50
PICO element detection in medical text without metadata: are first sentences enough?
Huang, K.-C., Chiang, I.-J., Xiao, F., Liao, C.-C., Liu, C. C.-H., & Wong, J.-M. (2013) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2013.07.009 · Cited by 52
Exploiting Image Generality for Lexical Entailment Detection
Kiela, D., Rimell, L., Vulić, I., & Clark, S. (2015) · Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 2: Short Papers) · doi:10.3115/v1/p15-2020 · Cited by 55
Pretrain-KGE: Learning Knowledge Representation from Pretrained Language Models
Zhang, Z., Liu, X., Zhang, Y., Su, Q., Sun, X., & He, B. (2020) · Findings of the Association for Computational Linguistics: EMNLP 2020 · doi:10.18653/v1/2020.findings-emnlp.25 · Cited by 52
“A typical KG such as Freebase (Bollacker et al., 2008) and Word-Net (Miller, 1995), consists of a set of triplets in the form of ( h, r, t ) with the head entity h and the tail entity t as nodes and relation r as edges in the graph.”
BackgroundText analysis for detecting terrorism-related articles on the web
Choi, D., Ko, B., Kim, H., & Kim, P. (2014) · Journal of Network and Computer Applications · doi:10.1016/j.jnca.2013.05.007 · Cited by 50
Metadata
- Authors
- Miller, G. A.
- Affiliations
- Princeton University (US)
- Corresponding author
- Miller, George A.
- Venue
- Communications of the ACM
- Published
- 1995-11
- Volume
- 38
- Issue
- 11
- Pages
- 39-41
- ISSN
- 0001-0782 (ISSN-L)
- 1557-7317
- OpenAlex
- W2081580037
- Semantic Scholar
- 68c03788224000794d5491ab459be0b2a2c38677
- Cited by
- 14,015
- References
- 7
- Citations by year
- 2026: 335 · 2025: 506 · 2024: 575 · 2023: 721 · 2022: 707 · 2021: 1008 · 2020: 1071 · 2019: 1068 · 2018: 961 · 2017: 841 · 2016: 816 · 2015: 855 · 2014: 723 · 2013: 633 · 2012: 565
- FWCI
- 38.49
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Communications of the ACM (publishedVersion)
- Funders
- James S. McDonnell Foundation
- Advanced Research Projects Agency
- Office of Naval Research
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Natural Language Processing Techniques
- Social Sciences › Arts and Humanities › Language and Linguistics › Lexicography and Language Studies
- Physical Sciences › Computer Science › Artificial Intelligence › Semantic Web and Ontologies
- Keywords
- WordNet, lexical databases, synonym sets, semantic relations, word meaning, natural language processing, machine-readable dictionaries, English vocabulary
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 ›
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