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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
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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.”
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“Semantic concordances are being prepared to provide a basis for empirical studies of sense identification [7].”
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“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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BackgroundZhu, Y., Xie, J., Liu, B., & Elgammal, A. (2019) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00994 · Cited by 111
“The relation among classes follows the WordNet [27] hierarchy.”
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“The retrofitting introduces external knowledge from the WordNet semantic lexicon (Miller, 1995).”
BackgroundOntology-based text clustering
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“Chaumartin [18] developed a linguistic rule-based system UPAR7, using WordNet [20], SentiWordNet [21] and WordNet-Affect [11] lexical resources.”
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“Using synsets from WordNet (Miller, 1995), we generated 5,551 synonyms from these seeds.”
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“ely evaluated on link prediction, a task that predicts the missing h in triple (?, r ,t) or missing t in (h r, ?). Two benchmark datasets FB15k (a subset of Freebase) and WN18 (extracted from WordNet [22]), created by Bordes et al. [5], are almost always used in such evaluation. Toutanova and Chen [31] noted that FB15…”
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“AC CE PT ED M AN US CR IP T specific WordNet [28] ID.”
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“Each word sense in the WordNet hierarchy has a top-level category based on part of speech (Miller, 1995).”
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“8 For the CL-SciSum data, since it is less domain-specific, we use the WordNet lexicon [55].”
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“In addition, the authors propose the use of WordNet (Miller, 1995) during the MSCG building phase to capture lexical knowledge between words and thus generate more abstractive compressions, and of a language model when re-ranking the shortest paths, to favor fluent compressions.”
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“WordNet (Miller, 1995) is a large lexical database for the English language.”
BackgroundMethodInfluentialScarlini, B., Pasini, T., & Roberto Navigli, A. (2020) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2020.emnlp-main.285 · Cited by 103
Niu, G., Li, Y., Tang, C., Geng, R., Dai, J., Liu, Q., Wang, H., Sun, J., Huang, F., & Si, L. (2021) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/3404835.3462925 · Cited by 105
OakInk: A Large-scale Knowledge Repository for Understanding Hand-Object Interaction
Yang, L., Li, K., Zhan, X., Wu, F., Xu, A., Liu, L., & Lu, C. (2022) · 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/cvpr52688.2022.02028 · Cited by 99
“Within the top-level classifications, we arrange the objects based on the WordNet [34] categories as the sub-level classification.”
MethodHYENA: Hierarchical Type Classification for Entity Names
Yosef, M. A., Bauer, S., Hoffart, J., Spaniol, M., & Weikum, G. (2012) · Cited by 100
A query expansion method for retrieving online BIM resources based on Industry Foundation Classes
Gao, G., Liu, Y.-S., Wang, M., Gu, M., & Yong, J. (2015) · Automation in Construction · doi:10.1016/j.autcon.2015.04.006 · Cited by 103
“WordNet [36] is a large lexical database of English, in which nouns, verbs, adjectives and adverbs are grouped into sets of cognitive synonyms (synsets).”
BackgroundInfluentialSong, W. G., Li, C. H., & Park, S. C. (2008) · Expert Systems with Applications · doi:10.1016/j.eswa.2008.12.046 · Cited by 100
Semantic Structure and Interpretability of Word Embeddings
Senel, L. K., Utlu, I., Yucesoy, V., Koç, A., & Çukur, T. (2018) · IEEE/ACM Transactions on Audio Speech and Language Processing · doi:10.1109/taslp.2018.2837384 · Cited by 100
“In addition, by comparing the human-categorized [30], which was used in [29] to investigate embedding dimensions, is constructed based on this type of relation that is also the most frequently encoded relation among sets of synonymous words in the WordNet database [1].”
BackgroundMethodInfluentialTourism Mobile App With Aspect-Based Sentiment Classification Framework for Tourist Reviews
Afzaal, M., Usman, M., & Fong, A. C. M. (2019) · IEEE Transactions on Consumer Electronics · doi:10.1109/tce.2019.2908944 · Cited by 99
“environment) using WordNet [30].”
Text Messaging-Based Medical Diagnosis Using Natural Language Processing and Fuzzy Logic
Omoregbe, N. A., Ndaman, I. O., Misra, S., Abayomi‐Alli, O., & Damaševičius, R. (2020) · Journal of Healthcare Engineering · doi:10.1155/2020/8839524 · Cited by 102
Recent trends in knowledge graphs: theory and practice
Tiwari, S. M., AL-Aswadi, F. N., & Gaurav, D. (2021) · Soft Computing · doi:10.1007/s00500-021-05756-8 · Cited by 105
Developing Engineering Ontology for Information Retrieval
Li, Z., Raskin, V., & Karthik Ramani, A. (2008) · Journal of Computing and Information Science in Engineering · doi:10.1115/1.2830851 · Cited by 99
The Art and Science of Analyzing Software Data
(2015) · Elsevier eBooks · doi:10.1016/c2012-0-07289-4 · Cited by 102
Futia, G., & Vetrò, A. (2020) · Information · doi:10.3390/info11020122 · Cited by 94
IntelliGO: a new vector-based semantic similarity measure including annotation origin
Benabderrahmane, S., Smaïl‐Tabbone, M., Poch, O., Napoli, A., & Devignes, M. (2010) · BMC Bioinformatics · doi:10.1186/1471-2105-11-588 · Cited by 98
Word Embedding-based Antonym Detection using Thesauri and Distributional Information
Ono, M., Miwa, M., & Sasaki, Y. (2015) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.3115/v1/n15-1100 · Cited by 98
Al-Halah, Z., Tapaswi, M., & Stiefelhagen, R. (2016) · 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.2016.643 · Cited by 98
“To test this, we query WordNet [28] with the AwA categories and extract the respective graph relevant to the hypernym links.”
MethodExploiting Web Images for Dataset Construction: A Domain Robust Approach
Yao, Y., Zhang, J., Shen, F., Hua, X., Xu, J., & Tang, Z. (2017) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2017.2684626 · Cited by 93
“iven query with Parts-Of-Speech (POS), specifically with NOUN, VERB, ADJECTIVE and ADVERB. Our motivation is to identify all related query expansions. GBNC is much more general and richer than WordNet [20] and ConceptNet [39]. Using GBNC can help us to find all the expansions ever published for any possible query. B. Fi…”
MethodInfluentialBootstrapping for Numerical Open IE
Saha, S., Pal, H., & Mausam, M. (2017) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p17-2050 · Cited by 98
“Some patterns are learned with specific words such as ‘contains’ in example (partial) (#contains#verb) (dobj# quantity #.+) ... { } We believe that this pattern should work with all inflections and synonyms of ‘contain’, BONIE uses WordNet to expand the pattern by including all inflections and synset synonyms.”
MethodInfluentialVashishth, S., Jain, P., & Talukdar, P. (2018) · International World Wide Web Conference (WWW) · doi:10.1145/3178876.3186030 · Cited by 104
“(3) WordNetwithWord-senseDisambiguation: Usingwordsense disambiguation [2] with Wordnet [22], we identify possible synsets for a given NP.”
BackgroundStereotypical Bias Removal for Hate Speech Detection Task using Knowledge-based Generalizations
Badjatiya, P., Gupta, M., & Varma, V. (2019) · International World Wide Web Conference (WWW) · doi:10.1145/3308558.3313504 · Cited by 97
“‘women’ and ‘man’. For the domain of hate speech, the term ‘women’ would be present more frequently as compared to ‘man’, even though they represent similar, if not same, groups. We leverage WordNet [17] database to obtain semantically related words and the lexical relations in order to find a suitable candidate for r…”
MethodCastro, N. M., & Siew, C. S. Q. (2020) · Proceedings of the Royal Society A Mathematical Physical and Engineering Sciences · doi:10.1098/rspa.2019.0825 · Cited by 98
“…there was an emergence of alternative ways of quantifying semantic similarity, including the use of latent semantic analysis (LSA; also called distributional semantics; [40]) to extract the statistical structure of natural language and through an analysis of lexical databases such as WordNet [41].”
BackgroundMethod
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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