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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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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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UMBC_EBIQUITY-CORE: Semantic Textual Similarity Systems
Han, L., Kashyap, A. L., Finin, T., Mayfield, J., & Weese, J. (2013) · doi:10.13016/m28w38626 · Cited by 314
Crossley, S. A., Kyle, K. D., & McNamara, D. S. (2015) · Behavior Research Methods · doi:10.3758/s13428-015-0651-7 · Cited by 313
“For a number of indices, the tool incorporates a part-of-speech (POS) tagger from the Natural Language Tool Kit (Bird, Klein, & Loper, 2009) and synonym sets from the WordNet lexical database (Miller, 1995).”
MethodDecoupling Zero-Shot Semantic Segmentation
Ding, J., Xue, N., Xia, G.-S., & Dai, D. (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/cvpr52688.2022.01129 · Cited by 339
“The key idea in zero-shot learning is to transfer kowledge from seen classes to unseen classes via semantic representation, such as the semantic attributes [2,24,28,30], concept ontology [16,37,39,47,48] and semantic word vectors [17, 41, 60].”
BackgroundI2T: Image Parsing to Text Description
Yao, B., Yang, X., Lin, L., Lee, M. W., & Zhu, S. C. (2010) · Proceedings of the IEEE · doi:10.1109/jproc.2010.2050411 · Cited by 314
Xu, R., Xiong, C., Chen, W. R., & Corso, J. J. (2015) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v29i1.9512 · Cited by 311
“2011; Wang et al. 2011; Felzenszwalb et al. 2010) and natural language processing (Miller 1995) techniques.”
MethodEvaluating knowledge transfer and zero-shot learning in a large-scale setting
Rohrbach, M., Stark, M. M., & Schiele, B. (2011) · 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.2011.5995627 · Cited by 306
Guarino, N. (1997) · Lecture notes in computer science · doi:10.1007/3-540-63438-x_8 · Cited by 307
Action Genome: Actions As Compositions of Spatio-Temporal Scene Graphs
Ji, J., Krishna, R., Fei-Fei, L., & Niebles, J. C. (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/cvpr42600.2020.01025 · Cited by 326
Semantic integration of heterogeneous information sources
Bergamaschi, S., Castano, S., Vincini, M., & Beneventano, D. (2001) · Data & Knowledge Engineering · doi:10.1016/s0169-023x(00)00047-1 · Cited by 307
Improved annotation of the blogosphere via autotagging and hierarchical clustering
Brooks, C. H., & Montanez, N. (2006) · International World Wide Web Conference (WWW) · doi:10.1145/1135777.1135869 · Cited by 306
Comparing and combining sentiment analysis methods
Gonçalves, P., Araújo, M., Benevenuto, F., & Cha, M. (2013) · Cited by 304
Global Inference for Sentence Compression: An Integer Linear Programming Approach
Clarke, J., & Lapata, M. (2008) · Journal of Artificial Intelligence Research · doi:10.1613/jair.2433 · Cited by 306
NExT-QA: Next Phase of Question-Answering to Explaining Temporal Actions
Xiao, J., Shang, X., Yao, A., & Chua, T. (2021) · 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/cvpr46437.2021.00965 · Cited by 397
“The per-frame appearance feature is extracted from ResNet-101 [14] pretrained on ImageNet [6], from either Convolutional 3WUPS computes the Wu-Palmer similarity [48] of the words based on their depths in WordNet [33].”
MethodUnderstanding Plagiarism Linguistic Patterns, Textual Features, and Detection Methods
Alzahrani, S. M., Salim, N., & Abraham, A. (2012) · IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews) · doi:10.1109/tsmcc.2011.2134847 · Cited by 299
Constructing informative priors using transfer learning
Raina, R., Ng, A. Y., & Koller, D. (2006) · International Conference on Machine Learning · doi:10.1145/1143844.1143934 · Cited by 299
Wiebe, J., & Mihalcea, R. F. (2006) · International Conference on Computational Linguistics · doi:10.3115/1220175.1220309 · Cited by 298
Learning to Solve Arithmetic Word Problems with Verb Categorization
Hosseini, M. J., Hajishirzi, H., Etzioni, O., & Kushman, N. (2014) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.3115/v1/d14-1058 · Cited by 324
The Impact of Artificial Intelligence on the Labor Market
Webb, M. (2019) · SSRN Electronic Journal · doi:10.2139/ssrn.3482150 · Cited by 531
“Instead, I use WordNet (Miller 1995), a database developed at Princeton University that groups nouns into a hierarchy of concepts.”
MethodInfluentialWhat would users change in my app? summarizing app reviews for recommending software changes
Sorbo, A. D., Panichella, S., Alexandru, C. V., Shimagaki, J., Visaggio, C., Canfora, G., & Gall, H. C. (2016) · SIGSOFT FSE · doi:10.1145/2950290.2950299 · Cited by 183
“Once the dictionaries were enriched with WordNet synonyms, one of the authors and an external validator (a software engineer with more than 6 years of industrial experience) separately assigned each sentence in T test (described in Section 2.1) to one or more of the 12 defined concepts.”
MethodInfluentialMulti-Modal Knowledge Graph Construction and Application: A Survey
Zhu, X., Li, Z., Wang, X., Jiang, X., Sun, P., Wang, X., Xiao, Y., & Yuan, N. J. (2022) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2022.3224228 · Cited by 353
“WordNet [3], BabelNet [4]), encyclopedia knowledge (e.”
Distinguishing between facts and opinions for sentiment analysis: Survey and challenges
Chaturvedi, I., Cambria, E., Welsch, R. E., & Herrera, F. (2017) · Information Fusion · doi:10.1016/j.inffus.2017.12.006 · Cited by 297
“WordNet is an example of a well-known semantic network [38,39].”
BackgroundPersonalized query expansion for the web
Chirita, P. A., Firan, C. S., & Nejdl, W. (2007) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/1277741.1277746 · Cited by 290
Generating Natural Questions About an Image
Mostafazadeh, N., Misra, I., Devlin, J., Mitchell, M., He, X., & Vanderwende, L. (2016) · Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/p16-1170 · Cited by 270
“…queried a search engine with 1,200 event-centric query terms which were obtained as follows: we aggregated all ‘event’ and ‘process’ hyponyms in WordNet (Miller, 1995), 1,000 most frequent TimeBank events (Puste-jovsky et al., 2003) and a set of manually curated 30 stereotypical events, from which…”
MethodInfluentialA PDTB-styled end-to-end discourse parser
Lin, Z., Ng, H. T., & Kan, M. (2012) · Natural Language Engineering · doi:10.1017/s1351324912000307 · Cited by 289
Unsupervised Learning from Narrated Instruction Videos
Alayrac, J.-B., Bojanowski, P., Agrawal, N., Šivic, J., Ivan Laptev, A., & Lacoste-Julien, S. (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.2016.495 · Cited by 289
Image-embodied Knowledge Representation Learning
Xie, R., Liu, Z., Luan, H., & Sun, M. (2017) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2017/438 · Cited by 335
An efficient SQL-based RDF querying scheme
Chong, E. I., Das, S., Eadon, G., & Srinivasan, J. (2005) · Very Large Data Bases · Cited by 286
YAGO: A Multilingual Knowledge Base from Wikipedia, Wordnet, and Geonames
Rebele, T., Suchanek, F. M., Hoffart, J., Biega, J., Kuzey, E., & Weikum, G. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-46547-0_19 · Cited by 285
“TheWordNet KB [6] is a lexical database of the English language [20].”
BackgroundASL-LEX: A lexical database of American Sign Language
Caselli, N. K., Sehyr, Z. S., Cohen-Goldberg, A. M., & Emmorey, K. D. (2016) · Behavior Research Methods · doi:10.3758/s13428-016-0742-0 · Cited by 284
“…than 40,000 English words (Balota et al., 2007); for other databases see, for example, WordNet (Miller, Beckwith, Fellbaum, Gross, Miller, 1990; Miller, 1995) and MRC Psycholinguistic Database (Coltheart, 1981) for English, CELEX for English, Dutch, and German, (Baayen, Piepenbrock, &…”
BackgroundUsing Structured Events to Predict Stock Price Movement: An Empirical Investigation
Ding, X., Zhang, Y., Liu, T., & Duan, J. (2014) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.3115/v1/d14-1148 · Cited by 283
Progress in Neural NLP: Modeling, Learning, and Reasoning
Zhou, M., Duan, N., Liu, S., & Shum, H. (2020) · Engineering · doi:10.1016/j.eng.2019.12.014 · Cited by 325
“Knowledge graphs built in this way, such as WordNet [55], are usually of high quality, but cover limited facts.”
BackgroundOntology-based photo annotation
Guus Schreiber, A., Dubbeldam, B., Wielemaker, J., & Wielinga, B. J. (2001) · IEEE Intelligent Systems · doi:10.1109/5254.940028 · Cited by 283
WordNet improves text document clustering
Andreas Hotho, A., Staab, S., & Gerd Stumme, A. (2003) · Cited by 276
Nassirtoussi, A. K., Aghabozorgi, S. R., Wah, T. Y., & Ngo, D. C. L. (2014) · Expert Systems with Applications · doi:10.1016/j.eswa.2014.08.004 · Cited by 282
“Specifically, the semantics of categories are represented by senses of terms appearing in the category labels as well as the interpretation of them by WordNet (Miller, 1995).”
BackgroundIdentifying expressions of opinion in context
Breck, E. J., Choi, Y., & Cardie, C. (2007) · International Joint Conference on Artificial Intelligence · Cited by 276
Ontological considerations in GIScience
Agarwal, P. (2005) · International Journal of Geographical Information Systems · doi:10.1080/13658810500032321 · Cited by 270
Ontology change: classification and survey
Flouris, G., Manakanatas, D., Kondylakis, H., Plexousakis, D., & Antoniou, G. (2008) · The Knowledge Engineering Review · doi:10.1017/s0269888908001367 · Cited by 272
Using ODP metadata to personalize search
Chirita, P. A., Nejdl, W., Paiu, R., & Kohlschütter, C. (2005) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/1076034.1076067 · Cited by 269
Survey on Multi-Output Learning
Xu, D., Shi, Y., Tsang, I. W.-H., Ong, Y.-S., Gong, C., & Shen, X. (2019) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2019.2945133 · Cited by 293
Release Planning of Mobile Apps Based on User Reviews
Villarroel, L., Bavota, G., Russo, B., Oliveto, R., & Penta, M. D. D. (2016) · International Conference on Software Engineering · doi:10.1145/2884781.2884818 · Cited by 271
“One possibility to unify synonyms would be to use existing thesaurus such as WordNet [23].”
Researching Mental Health Disorders in the Era of Social Media: Systematic Review
Wongkoblap, A., Vadillo, M. A., & Ćurčin, V. (2017) · Journal of Medical Internet Research · doi:10.2196/jmir.7215 · Cited by 286
“WordNet is a large English lexicon that can be used to extract parts of speech from text and find semantic meanings of words [85].”
BackgroundA Unified Model for Word Sense Representation and Disambiguation
Chen, X., Liu, Z., & Sun, M. (2014) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.3115/v1/d14-1110 · Cited by 274
PhishStorm: Detecting Phishing With Streaming Analytics
Marchal, S., Francois, J., State, R., & Engel, T. (2014) · IEEE Transactions on Network and Service Management · doi:10.1109/tnsm.2014.2377295 · Cited by 271
“…obfuscation techniques [15], with examples given in Table I for the domain paypal.com: • Type I: URL obfuscation with other domain: The mld.ps is a real domain name, usually registered by the phisher, while the original domain being phished is part of the path, the query or the upper level domain.”
BackgroundJointly Embedding Knowledge Graphs and Logical Rules
Guo, S., Wang, Q., Wang, L., Wang, B., & Guo, L. (2016) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d16-1019 · Cited by 278
“(ii) We evaluate KALE with link prediction and triple classification tasks on WordNet (Miller, 1995) and Freebase (Bollacker et al., 2008).”
MethodA Survey on Knowledge Graph Embedding: Approaches, Applications and Benchmarks
Dai, Y., Wang, S., Xiong, N. N., & Guo, W. (2020) · Electronics · doi:10.3390/electronics9050750 · Cited by 294
Write a Classifier: Zero-Shot Learning Using Purely Textual Descriptions
Elhoseiny, M., Saleh, B., & Elgammal, A. (2013) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2013.321 · Cited by 267
Activity labeling in process modeling: Empirical insights and recommendations
Mendling, J., Reijers, H. A., & Recker, J. (2009) · Information Systems · doi:10.1016/j.is.2009.03.009 · Cited by 267
Semantic Matching: Algorithms and Implementation
Giunchiglia, F., Yatskevich, M., & Shvaiko, P. (2007) · Lecture notes in computer science · doi:10.1007/978-3-540-74987-5_1 · Cited by 261
Representation and competition in the perception of spoken words
Gaskell, M. G., & Marslen‐Wilson, W. D. (2002) · Cognitive Psychology · doi:10.1016/s0010-0285(02)00003-8 · Cited by 261
Improving agile requirements: the Quality User Story framework and tool
Lucassen, G., Dalpiaz, F., van der Werf, J. M. E. M., & Brinkkemper, S. (2016) · Requirements Engineering · doi:10.1007/s00766-016-0250-x · Cited by 260
“AQUSA could implement each of the similarity measures that we outlined in [35] using the WordNet lexical database [40] to detect semantic similarity.”
MethodGraph-based Natural Language Processing and Information Retrieval
Mihalcea, R. F., & Radev, D. (2011) · Cambridge University Press eBooks · doi:10.1017/cbo9780511976247 · Cited by 261
Learning causality for news events prediction
Radinsky, K., Davidovich, S., & Markovitch, S. (2012) · International World Wide Web Conference (WWW) · doi:10.1145/2187836.2187958 · Cited by 272
Process Model Generation from Natural Language Text
Friedrich, F., Mendling, J., & Puhlmann, F. (2011) · Notes on numerical fluid mechanics and multidisciplinary design · doi:10.1007/978-3-642-21640-4_36 · Cited by 275
Neural Natural Language Inference Models Enhanced with External Knowledge
Chen, Q., Zhu, X., Ling, Z.-H., Inkpen, D. Z., & Wei, S. (2018) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p18-1224 · Cited by 283
“When training TransE for WordNet, relations are represented with vectors of 20 dimension.”
BackgroundMethodInfluentialWhat is Semantic Communication? A View on Conveying Meaning in the Era of Machine Intelligence
Lan, Q., Wen, D., Zhang, Z., Zeng, Q., Chen, X., Popovski, P., & Huang, K. (2021) · Journal of Communications and Information Networks · doi:10.23919/jcin.2021.9663101 · Cited by 284
Semantic Relation Reasoning for Shot-Stable Few-Shot Object Detection
Zhu, C., Chen, F., Ahmed, U., Shen, Z., & Savvides, M. (2021) · 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/cvpr46437.2021.00867 · Cited by 254
Semantic Network Analysis as a Method for Visual Text Analytics
Drieger, P. (2013) · Procedia - Social and Behavioral Sciences · doi:10.1016/j.sbspro.2013.05.053 · Cited by 278
A survey on question answering technology from an information retrieval perspective
Kolomiyets, O., & Moens, M. (2011) · Information Sciences · doi:10.1016/j.ins.2011.07.047 · Cited by 263
Opinion Mining and Information Fusion: A survey
Balazs, J., & Velásquez, J. D. (2015) · Information Fusion · doi:10.1016/j.inffus.2015.06.002 · Cited by 253
AutoComment: Mining question and answer sites for automatic comment generation
Wong, E., Yang, J., & Tan, L. (2013) · IEEE/ACM International Conference on Automated Software Engineering (ASE) · doi:10.1109/ase.2013.6693113 · Cited by 251
Few-Shot Image Recognition With Knowledge Transfer
Peng, Z., Li, Z., Zhang, J., Li, Y., Qi, G.-J., & Tang, J. (2019) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00053 · Cited by 250
“Each node in this knowledge graph represents a semantic category, and two nodes are linked if they are correlated in WordNet.”
MethodLearning Semantic Hierarchies via Word Embeddings
Fu, R., Guo, J., Qin, B., Che, W., Wang, H., & Liu, T. (2014) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.3115/v1/p14-1113 · Cited by 254
Transductive Unbiased Embedding for Zero-Shot Learning
Song, J., Shen, C., Yang, Y., Liu, Y., & Song, M. (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.2018.00113 · Cited by 245
Gao, T. Y., Dontcheva, M., Adar, E., Liu, Z., & Karahalios, K. G. (2015) · ACM Symposium on User Interface Software and Technology (UIST) · doi:10.1145/2807442.2807478 · Cited by 244
“The first compares the “distance” between the n-gram and the lexicon by calculating the graph distance on the WordNet graph [19] (nodes in WordNet are words and edges include semantic relationships such as hypernyms or synonyms).”
BackgroundSemantic Projection Network for Zero- and Few-Label Semantic Segmentation
Xian, Y., Choudhury, S., He, Y., Schiele, B., & Akata, Z. (2019) · 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.2019.00845 · Cited by 248
“Thus, there has been a large group of studies [2, 39, 36, 10] utilizing other sources such as Word2vec [29], fastText [19], or hierarchies [30] for building semantic embeddings.”
BackgroundSentiment Analysis for Fake News Detection
Alonso, M. Á., Vilares, D., Gómez‐Rodríguez, C., & Vilares, J. (2021) · Electronics · doi:10.3390/electronics10111348 · Cited by 267
“For instance, Esuli and Sebastiani [122] presented SentiWordNet, a lexical resource where every entry in WordNet [123] is assigned a positive, neutral, and negative score, which is generated by a set of ternary semi-supervised classifiers.”
BackgroundHybrid Transformer with Multi-level Fusion for Multimodal Knowledge Graph Completion
Chen, X., Zhang, N., Li, L., Deng, S., Tan, C., Xu, C., Huang, F., Si, L., & Chen, H. (2022) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/3477495.3531992 · Cited by 276
“We adopt two publicly available datasets for multimodal link prediction, including: 1)WN18-IMG:WN18 [3] is a knowledge graph originally extracted from WordNet [30].”
MethodOntology-driven geographic information systems
Fonseca, F., & Egenhofer, M. J. (1999) · ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (SIGSPATIAL GIS) · doi:10.1145/320134.320137 · Cited by 240
Computing Semantic Similarity of Concepts in Knowledge Graphs
Zhu, G., & Iglesias, C. Á. (2016) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2016.2610428 · Cited by 254
Using distributional semantics to study syntactic productivity in diachrony: A case study
Perek, F. (2016) · Linguistics · doi:10.1515/ling-2015-0043 · Cited by 241
Comparing geospatial entity classes: an asymmetric and context-dependent similarity measure
Rodríguez, M. A., & Egenhofer, M. J. (2004) · International Journal of Geographical Information Systems · doi:10.1080/13658810310001629592 · Cited by 238
A neural joint model for entity and relation extraction from biomedical text
Li, F., Zhang, M., Fu, G., & Ji, D. (2017) · BMC Bioinformatics · doi:10.1186/s12859-017-1609-9 · Cited by 250
“Their method used knowledge bases such as Word-Net [36] and CTD [37] to help improving performances.”
MethodSome ontological principles for designing upper level lexical resources
Guarino, N. (1998) · Language Resources and Evaluation Conference (LREC) · doi:10.63317/59mkmo2uxke9 · Cited by 236
A semantic approach for text clustering using WordNet and lexical chains
Wei, T., Lu, Y., Chang, H., Zhou, Q., & Bao, X. (2014) · Expert Systems with Applications · doi:10.1016/j.eswa.2014.10.023 · Cited by 234
What does a platypus look like? Generating customized prompts for zero-shot image classification
Pratt, S., Covert, I., Liu, R., & Farhadi, A. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.01438 · Cited by 289
TGIF: A New Dataset and Benchmark on Animated GIF Description
Li, Y., Song, Y., Cao, L., Tetreault, J., Goldberg, L., Jaimes, A., & Luo, J. (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.2016.502 · Cited by 235
“We compare two different databases to represent semantic roles: WordNet [ 22] and FrameNet [2], which we refer to as SMT-WordNet and SMT-FrameNet , respectively.”
GlossBERT: BERT for Word Sense Disambiguation with Gloss Knowledge
Huang, L., Sun, C. C., Qiu, X., & Huang, X. (2019) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d19-1355 · Cited by 275
“In order to fully leverage gloss information, we propose GlossBERT to construct context-gloss pairs from all possible senses of the target word in WordNet, thus treating WSD task as a sentence-pair classification problem.”
MethodInfluentialNamed Entity Extraction for Knowledge Graphs: A Literature Overview
Al-Moslmi, T., Ocaña, M. G., Opdahl, A. L., & Veres, C. (2020) · IEEE Access · doi:10.1109/access.2020.2973928 · Cited by 258
Symeonidis, S., Effrosynidis, D., & Arampatzis, A. (2018) · Expert systems with applications · doi:10.1016/j.eswa.2018.06.022 · Cited by 214
Utilization of text mining as a big data analysis tool for food science and nutrition
Tao, D., Yang, P., & Feng, H. (2020) · Comprehensive Reviews in Food Science and Food Safety · doi:10.1111/1541-4337.12540 · Cited by 252
Targeted Supervised Contrastive Learning for Long-Tailed Recognition
Li, T., Cao, P., Yuan, Y., Fan, L., Yang, Y., Feris, R., Indyk, P., & Katabi, D. (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/cvpr52688.2022.00679 · Cited by 243
“The semantic distance of two classes is computed using WordNet hierarchy [29], which is a hierarchical structure that contains all ImageNet classes as leaf nodes.”
MethodMultiple Granularity Descriptors for Fine-Grained Categorization
Wang, D., Shen, Z., Shao, J., Zhang, W., Xue, X., & Zhang, Z. (2015) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.276 · Cited by 231
“Multi-grained labels references for a three-level taxonomic hierarchy (family, genus and species) come from the followings: American Ornithologists’ Union Check-list of North American Birds[8] and WordNet[28].”
BackgroundThe EPIC-KITCHENS Dataset: Collection, Challenges and Baselines
Damen, D., Doughty, H., Farinella, G. M., Fidler, S., Furnari, A., Kazakos, E., Moltisanti, D., Munro, J., Perrett, T., Price, W., et al. (2020) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2020.2991965 · Cited by 267
“We attempted to automate the clustering of verbs and nouns using combinations of WordNet [45], Word2Vec [46], and Lesk algorithm [47], how-”
MethodExtraction Patterns for Information Extraction Tasks: A Survey
Muslea, I. (1999) · Cited by 226
Comparing the performance of biomedical clustering methods
Wiwie, C., Baumbach, J., & Röttger, R. (2015) · Nature Methods · doi:10.1038/nmeth.3583 · Cited by 223
Mihalcea, R. F., & Strapparava, C. (2005) · doi:10.3115/1220575.1220642 · Cited by 224
Type-Constrained Representation Learning in Knowledge Graphs
Krompaß, D., Baier, S., & Volker Tresp, C. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-25007-6_37 · Cited by 230
The emergence of social media data and sentiment analysis in election prediction
Chauhan, P., Sharma, N., & Sikka, G. (2020) · Journal of Ambient Intelligence and Humanized Computing · doi:10.1007/s12652-020-02423-y · Cited by 242
“Dictionary can be manually created by users or some already available dictionaries used for sentiment analysis are Linguistic Inquiry and Word Count (LIWC) (Pennebaker et al. 2012), WordNet (Miller 1995), SentiWordNet (Esuli et al. 2006), SenticNet, 12 Sen-tiStrength, 13 and OpinionFinder (Wilson et al. 2005).”
MethodInfluentialCollective privacy management in social networks
Squicciarini, A., Shehab, M., & Paci, F. (2009) · International World Wide Web Conference (WWW) · doi:10.1145/1526709.1526780 · Cited by 223
Husari, G., Al-Shaer, E. S., Ahmed, M., Chu, B., & Niu, X. (2017) · Annual Computer Security Applications Conference · doi:10.1145/3134600.3134646 · Cited by 222
“In addition, we have incorporated WordNet [17], � esaurus [8], and Watson Synonym [30].”
MethodThe SENSEVAL-3 English Lexical Sample Task
Mihalcea, R. F., Chklovski, T. A., & Kilgarriff, A. (2004) · University of North Texas Digital Library (University of North Texas) · Cited by 221
Promptdiff: a fixed-point algorithm for comparing ontology versions
Noy, N. F., & Musen, M. A. (2002) · doi:10.5555/777092.777207 · Cited by 221
Ontology research and development. Part 1 - a review of ontology generation
Ding, Y., & Foo, S. S.-B. (2002) · Journal of Information Science · doi:10.1177/0165551024234020 · Cited by 221
SimVerb-3500: A Large-Scale Evaluation Set of Verb Similarity
Gerz, D., Vulić, I., Hill, F., Reichart, R., & Korhonen, A. (2016) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d16-1235 · Cited by 262
“To examine this hypothesis, we resort to WordNet (Miller, 1995), where different semantic usages of words are listed as so-called synsets.”
BackgroundInfluentialInkpen, D. Z., & Association for Computing Machinery-Digital Library (2010) · Cited by 218
Compacter: Efficient Low-Rank Hypercomplex Adapter LayersPreprint
Mahabadi, R. K., Henderson, J., & Ruder, S. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2106.04647 · Cited by 266
A Protégé Plug-In for Ontology Extraction from Text Based on Linguistic Analysis
Paul Buitelaar, C., Olejnik, D., & Sintek, M. (2004) · Lecture notes in computer science · doi:10.1007/978-3-540-25956-5_3 · Cited by 217
Chang, J. C., Amershi, S., & Kamar, E. S. (2017) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3025453.3026044 · Cited by 233
“For our Cars and Cats image datsets, we collected images from ImageNet [17] by first collecting all images that corresponded to WordNet [42] concepts containing the keyword “car” or “cat”, and then sub-sampling the set down to approximately 600 images per dataset while ensuring no sub-concept (such…”
MethodSentiment Analysis in Health and Well-Being: Systematic Review
Žunić, A., Corcoran, P., & Spasić, . (2019) · JMIR Medical Informatics · doi:10.2196/16023 · Cited by 239
“In absolute terms, the number of these terms ranges from as little as 330 in WordNet-Affect to as much as 11,687 in SentiWordNet.”
MethodInfluentialImageNet-21K Pretraining for the MassesPreprint
Ridnik, T., Ben-Baruch, E., Noy, A., & Zelnik‐Manor, L. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2104.10972 · Cited by 596
“Using the WordNet synset hyponym (subtype) and hypernym (supertype) relations, we can obtain for each class its parent class, if exists, and a list of child classes, if exists.”
BackgroundMethodInfluential
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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