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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]
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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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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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BackgroundDigital Language Learning (DLL): Insights from Behavior, Cognition, and the Brain
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BackgroundMichalopoulos, G., Wang, Y., Kaka, H., Chen, H., & Wong, A. K. C. (2021) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/2021.naacl-main.139 · Cited by 149
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“2017a) using WordNet (Miller 1995), or indirectly transfer knowledge from other tasks, e.”
MethodRelational Message Passing for Knowledge Graph Completion
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“WN18 [4] contains conceptual-semantic and lexical relations among English words from WordNet [18].”
BackgroundKnowledge-Guided Semantic Transfer Network for Few-Shot Image Recognition
Li, Z., Tang, H., Peng, Z., Qi, G.-J., & Tang, J. (2023) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2023.3240195 · Cited by 128
“In fact, this knowledge graph is constructed based on WordNet [46], which contains all categories in the 21K ImageNet dataset [56].”
Choi, S., Yoon, J., Kim, K., Lee, J. Y., & Kim, C.-H. (2011) · Scientometrics · doi:10.1007/s11192-011-0420-z · Cited by 128
From One Point to A Manifold: Knowledge Graph Embedding For Precise Link PredictionPreprint
Xiao, H., Huang, M., & Zhu, X. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1512.04792 · Cited by 147
“Our experiments are conducted on four public benchmark datasets that are the subsets of Wordnet [Miller, 1995] and Freebase [Bollacker et al.”
MethodFriedman, C., Rindflesch, T. C., & Corn, M. (2013) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2013.06.004 · Cited by 136
Sentiment Analysis of News Articles: A Lexicon based Approach
Taj, S., Shaikh, B. B., & Meghji, A. F. (2019) · International Conference on Computing, Mathematics and Engineering Technologies (iCoMET) · doi:10.1109/icomet.2019.8673428 · Cited by 127
“WordNet contains more than 118,000 different word forms and more than 90,000 different word senses [17].”
BackgroundHierarchical Sequential Learning for Extracting Opinions and Their Attributes
Choi, Y., & Cardie, C. (2010) · Cited by 126
Behavioral Similarity – A Proper Metric
Kunze, M., Weidlich, M., & Weske, M. (2011) · Lecture notes in computer science · doi:10.1007/978-3-642-23059-2_15 · Cited by 126
Measuring Prerequisite Relations Among Concepts
Liang, C., Wu, Z., Huang, W., & Giles, C. L. (2015) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d15-1193 · Cited by 126
HittER: Hierarchical Transformers for Knowledge Graph Embeddings
Chen, S., Liu, X., Gao, J., Jiao, J., Zhang, R., & Ji, Y. (2021) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2021.emnlp-main.812 · Cited by 149
“Nathani et al. (2019) also propose an attention-based feature embedding to capture multi-hop neighbor information, but unfortunately, their reported results have been proven to be unreliable in a recent re-evaluation (Sun et al., 2020).”
ResultKnowledge graph and knowledge reasoning: A systematic review
Tian, L., Xue, Z., Wu, Y., Zhou, W.-T., Zhang, J., & Zhang, T. (2022) · Journal of Electronic Science and Technology · doi:10.1016/j.jnlest.2022.100159 · Cited by 133
Image-Grounded Conversations: Multimodal Context for Natural Question and Response GenerationPreprint
Mostafazadeh, N., Brockett, C., Dolan, B., Galley, M., Gao, J., Spithourakis, G., & Vanderwende, L. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1701.08251 · Cited by 125
“ion about the selected image. Images were sampled from the VQG dataset by querying a search engine using event-centric query terms that aggregated `event' and `process' hyponyms in WordNet (Miller, 1995) and using freVisual Context Textual Context Oh my gosh, i'm so buying this shirt. I found a cawaii b…”
MethodUniversal Decompositional Semantics on Universal Dependencies
White, A. S., Reisinger, D., Sakaguchi, K., Vieira, T., Zhang, S., Rudinger, R., Rawlins, K., & Durme, B. V. (2016) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d16-1177 · Cited by 137
“However, here we rely on the rich pre-existing taxonomy of lexical knowledge captured in the WordNet hierarchy (Miller, 1995) in order to more efficiently gather implicit property responses.”
MethodWord Sense Disambiguation: A comprehensive knowledge exploitation framework
Wang, Y., Wang, M., & Fujita, H. (2019) · Knowledge-Based Systems · doi:10.1016/j.knosys.2019.105030 · Cited by 125
Predicting Failures of Vision Systems
Zhang, P., Wang, J., Farhadi, A., Hebert, M., & Parikh, D. (2014) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2014.456 · Cited by 124
“, WordNet [38]), their approach trades off specificity for accuracy.”
BackgroundA framework for multi-document abstractive summarization based on semantic role labelling
Khan, A., Salim, N. B., & Kumar, Y. J. (2015) · Applied Soft Computing · doi:10.1016/j.asoc.2015.01.070 · Cited by 124
“Therefore, this study exploits Jiang’s semantic similarity measure [21] for computing semantic similarity between each pair of PASs Jiang’s measure is information content based measure and consider that each concept in the WordNet [26] hold certain information .”
Leveraging Social Media to Promote Public Health Knowledge: Example of Cancer Awareness via Twitter
Xu, S., Markson, C., Costello, K. L., Xing, C. Y., Demissie, K., & Llanos, A. A. M. (2016) · JMIR Public Health and Surveillance · doi:10.2196/publichealth.5205 · Cited by 126
“Then for each tweet, using Wordnet (a lexical database where nouns, verbs and adjectives are collected into sets of cognitive synonyms) [45,46], the top five synonyms, when available, for each noun and verb were appended to the tweets, resulting in expanded tweets while retaining their original meanings.”
Method
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
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