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 information is ubiquitous in real-world knowledge graphs.
“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]
In “More Than 166,000 Word Form and Sense Pairs” (page unavailable), Miller says hyponymy and hypernymy organize noun meanings in WordNet into a hierarchical structure. This supports hierarchy as a way to organize lexical knowledge, but the paper does not establish that hierarchical relations are ubiquitous or indispensable across real-world knowledge graphs. (LLM, conf=0.85)
Experimental — accuracy not yet measuredKnowledge about hierarchical relations (i.e., transitive closure) is explicitly available in the definition of the relation in KGs such as ConceptNet, WordNet and Gene Ontology.
“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]
WN18RR is a subset of WordNet, which features lexical relationships between word senses.
“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 · see citing works
Self-citationFREQUENCY ANALYSIS OF ENGLISH USAGE: LEXICON AND GRAMMAR
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Contextual correlates of semantic similarity
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“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
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The categorization of sentential contexts
Charles, W. G. (1988) · Journal of Psycholinguistic Research · doi:10.1007/bf01067226 · Cited by 12 · cited 1× · see citing works
“People are quite skillful in making such distinctions [1].”
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Research works printed but not linked: 1
- [4]4. Miller, G. A., Ed. WordNet: An on-line lexical database. Inter- national Journal of Lexicography 3, 4 (Winter 1990), 235--312.
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“By us-ing WordNet as a backbone (Miller, 1995), ImageNet already takes care of disambiguating word meanings and of combining together synonyms into the same object category.”
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“32 × 32 pixels) images that were collected using WordNet (Miller 1995) synsets as queries.”
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“2008), WordNet (Miller 1995) and GeneOntology (Ashburner et al.”
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“In WordNet, each entity is a synset consisting of several words, corresponding to a distinct word sense.”
MethodInfluentialA large annotated corpus for learning natural language inference
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“The EmotioNet [22] consists of one million images of facial expressions downloaded from the Internet by selecting all the words derived from the word “feeling” inWordNet [31].”
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“, 2018) uses a GCN to map the category names — based on their relations in WordNet (Miller, 1995) — to image feature domain, and find the most similar category to a query image feature vector.”
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“All synonyms for synonym replacements and random insertions were generated using WordNet (Miller, 1995).”
MethodKnowledge Graph Embedding via Dynamic Mapping Matrix
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“Knowledge Graphs such as WordNet (Miller 1995), Freebase (Bollacker et al. 2008) and Yago (Suchanek et al. 2007) have been playing a pivotal role in many AI applications, such as relation extraction(RE), question answering(Q&A), etc.”
MethodThe Description Logic Handbook
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“Inspired by WordNet [89] and ImageNet [90], Wu et al .”
MethodGOT-10k: A Large High-Diversity Benchmark for Generic Object Tracking in the Wild
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“This work presents a large unified tracking benchmark that solves the above problems by (1) Using WordNet [1] as the backbone to guide a comprehensive and unbiased population of object classes, and (2) Following the one-shot protocol to avoid biased evaluation results.”
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“These kinds of graphs have been heavily developed with notable examples such as WordNet [40], Penn Treebank [41], and the ImageNet class label structure [17].”
MethodOntology Learning for the Semantic Web
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“Curated Yes Cyc/OpenCyc [23], WordNet [24], UMLS [25]”
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“embeddings including attributes, word2vec [27], glove [56] and wordnet hierarchy [57].”
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“, WordNet [159]) or knowledge graphs (e.”
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“dataset, following Xu et al. [39] 4We expand the original Visual Genome dataset with 5k new scene graphs collected through crowdsourcing. 5Note that we cannot effectively use the wordnet annotations [26] used throughout the Visual Genome dataset since they are highly inaccurate, relating objects to irrelevant senses,…”
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“One difference between CUB and mini -ImageNet is their domain difference in base and novel classes since classes in mini -ImageNet have a larger divergence than CUB in a word-net hierarchy Miller (1995).”
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“Search systems have utilized additional resources such as WordNet (36) and domain ontologies (37, 38) to enhance users’ original terms with their synonyms and/or closely related concepts.”
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“aw inputs without any human guidance besides labeled training data. In recent years, DNNs have surpassed human performance in many application domains due to increasing availability of large datasets [20, 38, 47], specialized hardware [34, 50], and efficient training algorithms [31, 39, 66, 71]. A DNN consists of mult…”
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“YAGO builds its classification implicitly from the category system in Wikipedia and the lexical resource WordNet [66], with infobox properties manually mapped to a fixed set of attributes.”
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“Competitive methods for this data have all utilized both heterogeneous features (e.g. word overlap/similarity, negation modeling, sentence/phrase composition) as well as external resources (e.g. Wordnet (Miller 1995)), and a wide variety of learning algorithms have been applied (e.g. SVM, Random forest, k-Nearest Neig…”
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“Such a taxonomy can be built automatically from a pre-defined ontology such as WordNet [36, 46, 1] .”
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“For instance on the Caltech-UCSD birds database (CUB) [46], previous zero-shot learning approaches [13, 2, 3] have used human-encoded attributes [24], or simplified language models such as bag-of-words [16], WordNet-hierarchyderived features [29], and neural word embeddings such as Word2Vec [28] and GloVE [37].”
Methodvon Rueden, L., Mayer, S., Beckh, K., Georgiev, B., Giesselbach, S., Heese, R., Kirsch, B., Walczak, M., Pfrommer, J., Pick, A., et al. (2021) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2021.3079836 · Cited by 878 · see citing works
“For example, WordNet [117] represents semantic and lexical relations of words such as synonymy.”
BackgroundBetter Word Representations with Recursive Neural Networks for Morphology
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“Various standard lexical resources are used for joke production, including a British pronunciation dictionary (to find different words with a similar pronunciation, such as ‘bizarre’ and ‘bazaar’) and WordNet (Miller, 1995, to find words with a similar meaning, such as bazaar and market). jape uses various techniques…”
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“wordnet hierarchies [24], or costly human annotations e.”
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“We estimate that the objects have coverage for nearly 21K WordNet entities [48] (see appendix for details).”
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“WordNet [20] is a lexical database available online, which offers a large repository of English lexical items.”
BackgroundKnowledge Enhanced Contextual Word Representations
Peters, M. E., Neumann, M., Logan, R., Schwartz, R., Joshi, V., Singh, S., & Smith, N. A. (2019) · Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP) · doi:10.18653/v1/d19-1005 · Cited by 661 · see citing works
“We experiment with integrating both WordNet (Miller, 1995) and Wikipedia, thus explicitly adding word sense knowledge and facts about named entities (including those unseen at training time).”
MethodLogAnomaly: Unsupervised Detection of Sequential and Quantitative Anomalies in Unstructured Logs
Meng, W., Liu, Y., Zhu, Y., Zhang, S., Pei, D., Liu, Y., Chen, Y., Zhang, R., Tao, S., Sun, P., et al. (2019) · Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2019/658 · Cited by 708 · see citing works
“Therefore, we first search synonyms and antonyms of the words in templates in WordNet.”
BackgroundThe Geometry of Culture: Analyzing the Meanings of Class through Word Embeddings
Kozlowski, A. C., Taddy, M., & Evans, J. A. (2019) · American Sociological Review · doi:10.1177/0003122419877135 · Cited by 682 · see citing works
““An Empirical Study of Semantic Similarity in WordNet and Word2Vec.””
MethodInfluentialChen, Z., & Liu, B. (2018) · Synthesis Lectures on Artificial Intelligence and Machine Learning · doi:10.2200/s00737ed1v01y201610aim033 · Cited by 154 · see citing works
Can a Suit of Armor Conduct Electricity? A New Dataset for Open Book Question Answering
Mihaylov, T., Clark, P., Khot, T., & Sabharwal, A. (2018) · Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/d18-1260 · Cited by 2,991 · see citing works
“Unlike the WordNet gain, adding ConceptNet (Speer et al., 2017) introduces a distraction and reduces the score.”
MethodInfluentialFew-Shot Object Detection With Attention-RPN and Multi-Relation Detector
Fan, Q., Zhuo, W., Tang, C., & Tai, Y. (2020) · 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/cvpr42600.2020.00407 · Cited by 680 · see citing works
Zero-Shot Recognition via Semantic Embeddings and Knowledge Graphs
Wang, X., Ye, Y., & Gupta, A. (2018) · 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.2018.00717 · Cited by 659 · see citing works
Universality and diversity in human song
Mehr, S. A., Singh, M., Knox, D., Ketter, D., Pickens-Jones, D., Atwood, S., Lucas, C., Jacoby, N., Egner, A., Hopkins, E. J., et al. (2019) · Science · doi:10.1126/science.aax0868 · Cited by 667 · see citing works
GRAM: Graph-based Attention Model for Healthcare Representation Learning
Choi, E., Bahadori, M. T., Song, L., Stewart, W. F., & Sun, J. (2017) · Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining · doi:10.1145/3097983.3098126 · Cited by 637 · see citing works
Learning Syntactic Patterns for Automatic Hypernym Discovery
Snow, R., Jurafsky, D. S., & Ng, A. Y. (2004) · neural information processing systems · Cited by 658 · see citing works
KEPLER: A Unified Model for Knowledge Embedding and Pre-trained Language Representation
Wang, X., Gao, T., Zhu, Z., Zhang, Z., Liu, Z., Li, J., & Tang, J. (2021) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00360 · Cited by 676 · see citing works
“(2) KEPLER-WordNet WordNet (Miller, 1995) is an English lexical graph, where nodes are lemmas and synsets, and edges are their relations.”
BackgroundSemEval-2017 Task 1: Semantic Textual Similarity Multilingual and Crosslingual Focused Evaluation
Cer, D., Diab, M., Agirre, E., Lopez-Gazpio, I., & Specia, L. (2017) · Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017) · doi:10.18653/v1/s17-2001 · Cited by 1,562 · see citing works
“Significant research effort has focused on STS over English sentence pairs.2 English STS is a 1i.a., news headlines, video and image descriptions, glosses from lexical resources including WordNet (Miller, 1995; Fellbaum, 1998), FrameNet (Baker et al., 1998), OntoNotes (Hovy et al., 2006), web discussion fora, plagiari…”
BackgroundInfluentialGeirhos, R., Rubisch, P., Michaelis, C., Bethge, M., Wichmann, F. A., & Brendel, W. (2018) · arXiv · doi:10.48550/arxiv.1811.12231 · Cited by 1,490 · see citing works
“The 1,000 ImageNet class predictions were mapped to the 16 categories using the WordNet hierarchy (Miller, 1995)—e.g. ImageNet category tabby cat would be mapped to cat .”
MethodA Corpus and Cloze Evaluation for Deeper Understanding of Commonsense Stories
Mostafazadeh, N., Chambers, N., He, X., Parikh, D., Batra, D., Vanderwende, L., Kohli, P., & Allen, J. (2016) · Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies · doi:10.18653/v1/n16-1098 · Cited by 598 · see citing works
“Here we count event as any hyponym of ‘event’ or ‘process’ in WordNet (Miller., 1995).”
MethodDeriving concept hierarchies from text
Sanderson, M., & Croft, B. W. (1999) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/312624.312679 · Cited by 617 · see citing works
Contextual Augmentation: Data Augmentation by Words with Paradigmatic Relations
Kobayashi, S. (2018) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/n18-2072 · Cited by 617 · see citing works
“According to the prior works, we can use words from a synset for the word actor obtained from WordNet ( histrion, player, thespian, and role player ).”
BackgroundText mining for market prediction: A systematic review
Nassirtoussi, A. K., Aghabozorgi, S. R., Wah, T. Y., & Ngo, D. C. L. (2014) · Expert Systems with Applications · doi:10.1016/j.eswa.2014.06.009 · Cited by 628 · see citing works
“Mittermayer (2004) does this by filtering for the top 1000 terms out of all terms. The most common approach however is setting a minimum occurrence limit and reducing the terms by selecting the ones reaching a number of occurrences (Butler & Kešelj, 2009; Schumaker & Chen, 2009). Next common approach is using a predef…”
BackgroundMethodInfluentialBenitez-Quiroz, C. F., Srinivasan, R., & Martı́nez, A. M. (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.2016.600 · Cited by 613 · see citing works
“WordNet includes synonyms (i.e., words that have the same or nearly the same meaning), hyponyms (i.e., subordinate nouns or nouns of more specific meaning, which defines a hierarchy of relationships), troponymys (i.e., verbs of more specific meaning, which defines a hierarchy of verbs), and entailments (i.e., deductio…”
MethodInfluentialA survey of multimodal sentiment analysis
Soleymani, M., García, D., Jou, B., Schuller, B. W., Chang, S., & Pantić, M. (2017) · Image and Vision Computing · doi:10.1016/j.imavis.2017.08.003 · Cited by 620 · see citing works
“, WordNet [134], ConceptNet [135], and General Inquirer [136] for inferring the speaker’s sentiment.”
BackgroundSimilarity of business process models: Metrics and evaluation
Dijkman, R., Dumas, M., van Dongen, B. F., Käärik, R., & Mendling, J. (2010) · Information Systems · doi:10.1016/j.is.2010.09.006 · Cited by 612 · see citing works
Detection and Resolution of Rumours in Social Media
Zubiaga, A., Aker, A., Bontcheva, K., Liakata, M., & Procter, R. (2019) · ACM Computing Surveys · doi:10.1145/3161603 · Cited by 608 · see citing works
How Do Users Like This Feature? A Fine Grained Sentiment Analysis of App Reviews
Guzmán, E., & Maalej, W. (2014) · IEEE International Requirements Engineering Conference (RE) · doi:10.1109/re.2014.6912257 · Cited by 611 · see citing works
“We group collocations whose pairs of words are synonyms and use Wordnet as a synonym dictionary.”
MethodInfluentialSentiment analysis in Facebook and its application to e-learning
Ortigosa, Á., Martin, J., & Carro, R. M. (2013) · Computers in Human Behavior · doi:10.1016/j.chb.2013.05.024 · Cited by 594 · see citing works
Large-Scale Sentiment Analysis for News and Blogs
Godbole, N., Srinivasaiah, M., & Skiena, S. (2007) · International Conference on Weblogs and Social Media · Cited by 581 · see citing works
Image Segmentation Using Text and Image Prompts
Lüddecke, T., & Ecker, A. S. (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/cvpr52688.2022.00695 · Cited by 592 · see citing works
“1): • In generalized zero-shot segmentation, seen as well as unseen categories needs to be segmented by putting un-seen categories in relation to seen ones, e.g. through word embeddings [1] or WordNet [2].”
MethodInfluentialEvaluation of Output Embeddings for Fine-grained Image Classification
Akata, Z., Reed, S., Walter, D., Lee, H., & Schiele, B. (2015) · Max Planck Digital Library · Cited by 567 · see citing works
Automated Text Analysis for Consumer Research
Humphreys, A., & Wang, R. J.-H. (2018) · Journal of Consumer Research · doi:10.1093/jcr/ucx104 · Cited by 636 · see citing works
OntoSeek: content-based access to the Web
Guarino, N., Masolo, C., & Vetere, G. (1999) · IEEE Intelligent Systems and their Applications · doi:10.1109/5254.769887 · Cited by 553 · see citing works
Nikfarjam, A., Sarker, A., O’Connor, K., Ginn, R., & Gonzalez‐Hernandez, G. (2015) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocu041 · Cited by 543 · see citing works
Evaluation methods for unsupervised word embeddings
Schnabel, T., Labutov, I., Mimno, D., & Joachims, T. (2015) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d15-1036 · Cited by 543 · see citing works
Mihalcea, R. F. (2005) · doi:10.3115/1220575.1220627 · Cited by 541 · see citing works
- 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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