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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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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].”
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- 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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“…also require additional inputs: an open-domain knowledge base such as Freebase [7] for detecting named entities; domain-specific dictionary/ontology; a pre-built mapping table between phrases in q nl and SQL keywords; WordNet [29]; and a word embedding matrix for calculating word similarity.”
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“For example, according to WordNet, a hypernym of the verb “bow” is “gesticulation,” which has been reflected in the structure of the motion description tree, as shown in Fig.”
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“words derived from the word “feeling” in WordNet [27].”
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“Also, the Linguistic distance returns the value 1 if the original words are synonymous in WordNet, and 0.5 if the word w1 is the hypernym of w2 or vice versa, otherwise it computes the traditional substring distance between the first word and the second one.”
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Clarkson, P. M., Ponn, J., Richardson, G. D., Rudzicz, F., Tsang, A., & Wang, J. (2020) · Abacus · doi:10.1111/abac.12182 · Cited by 120
Confalonieri, R., Weyde, T., Besold, T. R., & Martı́n, F. M. D. P. (2021) · Artificial Intelligence · doi:10.1016/j.artint.2021.103471 · Cited by 112
Ruiz-Casado, M., Alfonseca, E., & Castells, P. (2005) · Lecture notes in computer science · doi:10.1007/11428817_7 · Cited by 115
SentiTurkNet: a Turkish polarity lexicon for sentiment analysis
Dehkharghani, R., Saygın, Y., Yanikoglu, B. A., & Oflazer, K. (2015) · Language Resources and Evaluation · doi:10.1007/s10579-015-9307-6 · Cited by 112
“• English WordNet (Miller 1995): This lexical resource groups synonym terms in a set called synset that includes a gloss (natural language explanation) for each synset.”
BackgroundCommon Sense Data Acquisition for Indoor Mobile Robots
Gupta, R. K., & Kochenderfer, M. J. (2004) · Cited by 112
Building and using a semantivisual image hierarchy
Li, L.-J., Wang, C., Lim, Y., Blei, D. M., & Fei-Fei, L. (2010) · 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.2010.5540027 · Cited by 112
Leveraging multi-domain prior knowledge in topic models
Chen, Z., Mukherjee, A., Liu, B., Hsu, M., Castellanos, M., & Ghosh, R. (2013) · International Joint Conference on Artificial Intelligence · Cited by 112
Views in a large-scale XML repository
Aguiléra, V., Cluet, S., Milo, T., Veltri, P., & Vodislav, D. (2002) · The VLDB Journal · doi:10.1007/s00778-002-0065-x · Cited by 112
Gao, J., Zhang, B.-W., & Chen, X.-H. (2014) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2014.11.009 · Cited by 111
“…concept B One direct way of measuring semantic similarity is to count the shortest path length between the compared concepts in a net (e.g. WordNet, Ontology, Mesh1, etc.) (Miller, 1995), Rada et al. adopted such method in their metric, and they found the metric fit well with medical semantic nets.”
BackgroundMethodInfluentialThe Emergence of Semantic Meaning in the Ventral Temporal Pathway
Carlson, T. A., Simmons, R., Kriegeskorte, N., & Slevc, L. R. (2013) · Journal of Cognitive Neuroscience · doi:10.1162/jocn_a_00458 · Cited by 111
AskNow: A Framework for Natural Language Query Formalization in SPARQL
Dubey, M., Dasgupta, S., Sharma, A., Höffner, K., & Lehmann, J. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-34129-3_19 · Cited by 111
Probabilistic Embedding of Knowledge Graphs with Box Lattice Measures
Vilnis, L., Li, X., Murty, S., & McCallum, A. (2018) · Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/p18-1025 · Cited by 111
Billion-scale federated learning on mobile clients
Niu, C., Wu, F., Tang, S., Hua, L., Jia, R., Lv, C., Wu, Z., & Chen, G. (2020) · ACM Annual International Conference on Mobile Computing and Networking (MobiCom) · doi:10.1145/3372224.3419188 · Cited by 111
“For example, (1) in the e-commerce recommendation, a tree structure can be used to partition the goods IDs, where the tree is initialized with the category information and further can be optimized using learning methods [57, 58]; and (2) in the natural language processing, a word semantic hierarchy can be built with t…”
BackgroundFine-Grained Scene Graph Generation with Data Transfer
Zhang, A., Yao, Y., Chen, Q., Ji, W., Liu, Z., Sun, M., & Chua, T. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-19812-0_24 · Cited by 111
LogFiT: Log Anomaly Detection Using Fine-Tuned Language Models
Almodovar, C., Sabrina, F., Karimi, S., & Azad, S. (2024) · IEEE Transactions on Network and Service Management · doi:10.1109/tnsm.2024.3358730 · Cited by 111
“Log Content Variability: To test the models’ ability to handle variation in the syntactic structure of the log data, the evaluation set is dynamically modified during model evaluation on the BGL dataset, so that the top 10% most commonly occurring verbs are replaced with their WordNet [48] lemmas.”
MethodLearning semantic relationships for better action retrieval in images
Ramanathan, V., Li, C., Deng, J., Han, W., Li, Z., Gu, K., Song, Y., Samy Bengio, A., Rosenberg, C., & Fei-Fei, L. (2015) · 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.2015.7298713 · Cited by 109
“Nevertheless, linguistic tools such as WordNet still provide valuable cues.”
MethodInfluentialYoon, J., & Kim, K. (2011) · Expert Systems with Applications · doi:10.1016/j.eswa.2011.08.154 · Cited by 110
Ordinal Common-sense Inference
Zhang, S., Rudinger, R., Duh, K., & Durme, B. V. (2017) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00068 · Cited by 116
“(c) Deriving properties via WordNet : At this step, we want to associate with each WordNet sense a set of possible properties.”
BackgroundMethodInfluentialKnowledge graph embedding with concepts
Guan, N., Song, D., & Liao, L. (2018) · Knowledge-Based Systems · doi:10.1016/j.knosys.2018.10.008 · Cited by 112
Johnson, M., Albizri, A., Harfouche, A., & Wamba, S. F. (2022) · International Journal of Information Management · doi:10.1016/j.ijinfomgt.2022.102479 · Cited by 128
Six challenges for the Semantic Web
Benjamins, R., Contreras, J., Corcho, Ó., & Gómez-Pérez, A. (2002) · Hispana · Cited by 110
Ontology-driven geographic information integration: A survey of current approaches
Buccella, A., Cechich, A., & Fillottrani, P. R. (2008) · Computers & Geosciences · doi:10.1016/j.cageo.2008.02.033 · Cited by 110
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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