The DOI matches its Crossref record; 7 of the 7 references it cites were found in a registry or an index.
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].
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“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]
Experimental — accuracy not yet measuredThe cited source says what the claim attributes to it.
In the citing paper
“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]
The cited source says what the claim attributes to it.
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]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
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
Cited by
Sorted by citations
Items 401–500 of 1,047
Recent Advances in Natural Language Inference: A Survey of Benchmarks, Resources, and ApproachesPreprint
Storks, S., Gao, Q., & Chai, J. Y. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1904.01172 · Cited by 289
“The latest release, ConceptNet 5.5 (Speer et al., 2017), contains over 21 million links between over 8 million nodes, having been augmented by several additional resources including Cyc (Lenat & Guha, 1989), WordNet (Fellbaum, 1999), and DBpedia (Auer et al., 2007).”
BackgroundMethodInfluentialXu, T., Jin, L., Fan, X., Zhou, Y., Pasupathy, S., & Talwadker, R. (2015) · ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC/FSE) · doi:10.1145/2786805.2786852 · Cited by 172
“Then, we convert each word in a query string to the root forms of the word based on WordNets [38].”
MethodThe contribution of neuroscience to consumer research: A conceptual framework and empirical review
Solnais, C., Andreu-Pérez, J., Sánchez‐Fernández, J., & Abela, J. A. I. (2013) · Journal of Economic Psychology · doi:10.1016/j.joep.2013.02.011 · Cited by 176
Cardon, D., Cointet, J., Mazières, A., & Carey-Libbrecht, L. (2018) · Réseaux · doi:10.3917/res.211.0173 · Cited by 246
“…d’images dont les éléments ont été annotés manuellement en 21 841 catégories en s’appuyant sur la structure hiérarchique d’une autre base de données classique en Traitement Automatique de la Langue, Wordnet (Miller, 1995).”
PairRE: Knowledge Graph Embeddings via Paired Relation Vectors
Chao, L., He, J., Wang, T., & Chu, W. (2021) · doi:10.18653/v1/2021.acl-long.336 · Cited by 221
“Knowledge graphs store huge amounts of structured data as the form of triples, with projects such as WordNet [19], Freebase [2], YAGO [25] and DBpedia [17].”
Extractor: Extracting Attack Behavior from Threat Reports
Satvat, K., Gjomemo, R., & Venkatakrishnan, V. N. (2021) · IEEE European Symposium on Security and Privacy · doi:10.1109/eurosp51992.2021.00046 · Cited by 192
Discriminative Transfer Learning with Tree-based Priors
Srivastava, N., & Salakhutdinov, R. (2013) · Cited by 171
KVQA: Knowledge-Aware Visual Question Answering
Shah, S. B., Mishra, A., Yadati, N., & Talukdar, P. P. (2019) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v33i01.33018876 · Cited by 185
“However, most of these works still focus on bridging images and commonsense Knowledge Graphs like WordNet (Miller 1995) and Conceptnet (Liu and Singh 2004), unlike ours which requires bridging images and world knowledge.”
BackgroundAn architecture for a generic dialogue shell
Allen, J. F., Byron, D. K., Dzikovska, M. O., Ferguson, G., Galescu, L., & Stent, A. J. (2000) · Natural Language Engineering · doi:10.1017/s135132490000245x · Cited by 171
Virtual character performance from speech
Marsella, S., Xu, Y., Lhommet, M., Feng, A., Scherer, S., & Shapiro, A. (2013) · Proceedings of the 12th ACM SIGGRAPH/Eurographics Symposium on Computer Animation · doi:10.1145/2485895.2485900 · Cited by 168
Text-enhanced representation learning for knowledge graph
Wang, Z., & Li, J. (2016) · International Joint Conference on Artificial Intelligence · Cited by 173
Mrkšić, N., Vulić, I., Séaghdha, D. Ó., Leviant, I., Reichart, R., Gašić, M., Korhonen, A., & Young, S. J. (2017) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00063 · Cited by 170
“However, their performance has not surpassed that of fine-tuning methods.3 Fine-Tuning Pre-trained Vectors Rothe and Schütze (2015) fine-tune word vector spaces to improve the representations of synsets/lexemes found in WordNet.”
InfluentialMulti-Scale Dynamic Convolutional Network for Knowledge Graph Embedding
Zhang, Z., Li, Z., Liu, H., & Xiong, N. N. (2020) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2020.3005952 · Cited by 173
A Survey of Available Corpora for Building Data-Driven Dialogue SystemsPreprint
Serban, I. V., Lowe, R., Henderson, P., Charlin, L., & Pineau, J. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1512.05742 · Cited by 169
EmoSenticSpace: A novel framework for affective common-sense reasoning
Poria, S., Gelbukh, A. F., Cambria, E., Hussain, A., & Huang, G.-B. (2014) · Knowledge-Based Systems · doi:10.1016/j.knosys.2014.06.011 · Cited by 166
Bilro, R. G., Loureiro, S. M. C., & Guerreiro, J. (2018) · Journal of Hospitality Marketing & Management · doi:10.1080/19368623.2018.1506375 · Cited by 185
“grouped into sets of cognitive synonyms, each expressing a distinct concept (Miller, 1995).”
BackgroundDetecting fake news for reducing misinformation risks using analytics approaches
Zhang, C., Gupta, A., Kauten, C., Deokar, A. V., & Qin, X. (2019) · European Journal of Operational Research · doi:10.1016/j.ejor.2019.06.022 · Cited by 167
MOS: Towards Scaling Out-of-distribution Detection for Large Semantic Space
Huang, R., & Li, Y. (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/cvpr46437.2021.00860 · Cited by 183
“For example, in the case of ImageNet, each class is associated with a synset in WordNet [34], from which we can build the taxonomy as a hierarchical tree.”
MethodRecent Developments in Document Clustering
Andrews, N., & Fox, E. A. (2007) · Computer Science Technical Reports (Virginia Tech) · Cited by 167
ParaSite: mining structural information on the Web
Spertus, E. (1997) · Computer Networks and ISDN Systems · doi:10.1016/s0169-7552(97)00033-0 · Cited by 167
Saha, D., Floratou, A., Sankaranarayanan, K., Minhas, U. F., Mittal, A., & Özcan, F. (2016) · Proceedings of the VLDB Endowment · doi:10.14778/2994509.2994536 · Cited by 166
“WordNet [25] and BabelNet [26] are two popular frameworks which characterize English words and provide sets of synonyms for these words.”
BackgroundWinter, B. (2019) · Converging evidence in language and communication research · doi:10.1075/celcr.20 · Cited by 182
Measuring semantic similarity in the taxonomy of WordNet
Yang, D., & Powers, D. M. W. (2005) · Cited by 165
Simple Semantics in Topic Detection and Tracking
Makkonen, J., Ahonen-Myka, H., & Salmenkivi, M. (2004) · Information Retrieval · doi:10.1023/b:inrt.0000011210.12953.86 · Cited by 165
A knowledge-based search engine powered by wikipedia
Milne, D., Witten, I. H., & Nichols, D. M. (2007) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/1321440.1321504 · Cited by 165
Topic themes for multi-document summarization
Harabagiu, S. M., & Lăcătuşu, F. (2005) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/1076034.1076071 · Cited by 164
A Cross-Lingual Dictionary for English Wikipedia Concepts
Spitkovsky, V. I., & Chang, A. X. (2012) · Language Resources and Evaluation Conference (LREC) · doi:10.63317/44o84k2bkpd9 · Cited by 164
Semantic Search on Text and Knowledge Bases
Bast, H., Buchhold, B., & Haußmann, E. (2016) · now publishers, Inc. eBooks · doi:10.1561/1500000032 · Cited by 187
“WordNet [Miller, 1992] is a large ontology of the concepts of general-purpose real-world knowledge.”
BackgroundMethodDecoding the Semantic Content of Natural Movies from Human Brain Activity
Huth, A. G., Lee, T., Nishimoto, S., Bilenko, N. Y., Vu, A. T., & Gallant, J. L. (2016) · Frontiers in Systems Neuroscience · doi:10.3389/fnsys.2016.00081 · Cited by 165
“Our decoder used a HLR model based on the graphical structure of WordNet, a semantic taxonomy that was manually constructed by a team of linguists (Miller, 1995).”
MethodBIOSSES: a semantic sentence similarity estimation system for the biomedical domain
Soğancıoğlu, G., Öztürk, H., & Özgür, A. (2017) · Bioinformatics · doi:10.1093/bioinformatics/btx238 · Cited by 159
Hybrid sentiment classification on twitter aspect-based sentiment analysis
Zainuddin, N., Selamat, A., & Ibrahim, R. B. (2017) · Applied Intelligence · doi:10.1007/s10489-017-1098-6 · Cited by 164
“Lexicon-based approaches do not require any training dataset and use sentiment lexicons such as Word Net [24] and SentiWordNet [5], for classification purposes.”
Semantically Tied Paired Cycle Consistency for Zero-Shot Sketch-Based Image Retrieval
Dutta, A., & 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.2019.00523 · Cited by 166
“Thus, there has been a large group of studies [29, 3, 53, 38, 37, 11] which utilize other auxiliary information, such as, text-based [30] or hierarchical model [32] for label embedding.”
BackgroundChen, B., Li, J., Lu, G., Yu, H., & Zhang, D. D. (2020) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2020.2967084 · Cited by 176
“[30] considered WordNet [36] as the source for building the structured knowledge graph.”
BackgroundGlobal Viewing of Heterogeneous Data Sources
Castano, S., & De Antonellis, V. (2001) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/69.917566 · Cited by 279
Matching Ontologies in Open Networked Systems: Techniques and Applications
Castano, S., Ferrara, A., & Montanelli, S. (2006) · Lecture notes in computer science · doi:10.1007/11617808_2 · Cited by 163
Sentiment Data Flow Analysis by Means of Dynamic Linguistic Patterns
Poria, S., Cambria, E., Gelbukh, A. F., Bisio, F., & Hussain, A. (2015) · IEEE Computational Intelligence Magazine · doi:10.1109/mci.2015.2471215 · Cited by 163
Monitoring the public opinion about the vaccination topic from tweets analysis
D’Andrea, E., Ducange, P., Bechini, A., Renda, A., & Marcelloni, F. (2018) · Expert Systems with Applications · doi:10.1016/j.eswa.2018.09.009 · Cited by 169
“, WordNet (Miller, 1995), SentiWordNet (Baccianella et al.”
A survey on aspect-based sentiment classification
Brauwers, G., & Frasincar, F. (2023) · ACM Computing Surveys · doi:10.1145/3503044 · Cited by 165
Semantic Similarity from Natural Language and Ontology Analysis
Harispe, S., Ranwez, S., Janaqi, S., & Montmain, J. (2015) · Synthesis lectures on human language technologies · doi:10.2200/s00639ed1v01y201504hlt027 · Cited by 161
Incorporating Global Visual Features into Attention-based Neural Machine Translation.
Calixto, I., & Liu, Q. (2017) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d17-1105 · Cited by 172
Zero-Shot Object Detection: Learning to Simultaneously Recognize and Localize Novel Concepts
Rahman, S., Khan, S. H., & Porikli, F. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-20887-5_34 · Cited by 167
Zero-Shot Grounding of Objects From Natural Language Queries
Sadhu, A., Chen, K., & Nevatia, R. (2019) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00479 · Cited by 168
“In addition to region phrases, visual genome also provides entity names mapped to synsets in wordnet [31].”
High performance question/answering
PAŞCA, M. A., & Harabagiu, S. M. (2001) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/383952.384025 · Cited by 161
BOLD5000, a public fMRI dataset while viewing 5000 visual images
Chang, N., Pyles, J. A., Marcus, A., Gupta, A., Tarr, M. J., & Aminoff, E. (2019) · Scientific Data · doi:10.1038/s41597-019-0052-3 · Cited by 200
“These super categories were created by using the WordNet hierarchy [44].”
MethodEvaluating question answering over linked data
López, V., Unger, C., Cimiano, P., & Motta, E. (2013) · Journal of Web Semantics · doi:10.1016/j.websem.2013.05.006 · Cited by 160
Multimodal Data Enhanced Representation Learning for Knowledge Graphs
Wang, Z., Li, L., Li, Q., & Zeng, D. D. (2019) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2019.8852079 · Cited by 166
“For visual knowledge, images are all extracted from ImageNet [10], a large image database built according to WordNet hierarchy, containing images for entities in WordNet.”
BackgroundInfluentialUsing WordNet and lexical operators to improve Internet searches
MOLDOVAN, D. I., & Mihalcea, R. F. (2000) · IEEE Internet Computing · doi:10.1109/4236.815847 · Cited by 159
Sparse Overcomplete Word Vector Representations
Faruqui, M., Tsvetkov, Y., Yogatama, D., Dyer, C., & Smith, N. A. (2015) · doi:10.3115/v1/p15-1144 · Cited by 165
“Yet word vectors do not look anything like the representations described in most lexical semantic theories, which focus on identifying classes of words (Levin, 1993; Baker et al., 1998; Schuler, 2005) and relationships among word meanings (Miller, 1995).”
BackgroundIdentifying Text Polarity Using Random Walks
Yousef, A. H., & Radev, D. (2010) · Cited by 159
Joung, J., & Kim, K. (2016) · Technological Forecasting and Social Change · doi:10.1016/j.techfore.2016.08.020 · Cited by 159
Event Representations for Automated Story Generation with Deep Neural Nets
Martin, L. J., Ammanabrolu, P., Wang, X., Hancock, W. S., Singh, S., Harrison, B. E., & Riedl, M. O. (2018) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v32i1.11430 · Cited by 170
“There are times when the Working Memory will not be able to fill named entity and WordNet synset placeholder slots because the most recent event bigram does not contain the element necessary for reuse.”
BackgroundInfluentialVegFru: A Domain-Specific Dataset for Fine-Grained Visual Categorization
Hou, S., Feng, Y., & Wang, Z. (2017) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.66 · Cited by 159
Interactive plant identification based on social image data
Joly, A., Goëau, H., Bonnet, P., Bakić, V., Barbe, J., Selmi, S., Yahiaoui, I., Carré, J., Mouysset, E., Molino, J., et al. (2013) · Ecological Informatics · doi:10.1016/j.ecoinf.2013.07.006 · Cited by 164
“The 22 K English concepts of the WordNet ontology (Miller, 1995) were fi rst used as text queries in popular web image search engines (e.g. Google Image).”
MethodJakob, N., Weber, S. H., Müller, M.-C., & Gurevych, I. (2009) · doi:10.1145/1651461.1651473 · Cited by 158
A retrospective of knowledge graphs
Yan, J., Wang, C., Cheng, W., Gao, M. Z., & Zhou, A. (2016) · Frontiers of Computer Science · doi:10.1007/s11704-016-5228-9 · Cited by 160
“• WordNet WordNet [107] was initially conceived as a lexical database for machine translation.”
MethodInfluentialABO: Dataset and Benchmarks for Real-World 3D Object Understanding
Collins, J., Goel, S., Deng, K., Luthra, A., Xu, L. L., Gündoğdu, E., Zhang, X., Vicente, T. F. Y., Dideriksen, T. L., Arora, H., et al. (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/cvpr52688.2022.02045 · Cited by 206
“To enable these meshes to easily be used for comparison with existing meth-ods trained on 3D datasets such as ShapeNet, we have collected category annotations for each 3D model and mapped them to noun synsets under the WordNet [49] taxonomy.”
MethodFine-Grained Recognition in the Wild: A Multi-task Domain Adaptation Approach
Gebru, T., Hoffman, J., & Fei-Fei, L. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.151 · Cited by 152
Duan, S., Kementsietsidis, A., Srinivas, K., & Udrea, O. (2011) · ACM SIGMOD International Conference on Management of Data (SIGMOD) · doi:10.1145/1989323.1989340 · Cited by 157
Using Wikipedia knowledge to improve text classification
Wang, P., Hu, J., Zeng, H.-J., & Chen, Z. (2008) · Knowledge and Information Systems · doi:10.1007/s10115-008-0152-4 · Cited by 157
Automatic Ontology Matching via Upper Ontologies: A Systematic Evaluation
Mascardi, V., Locoro, A., & Rosso, P. (2009) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2009.154 · Cited by 157
Image Generation From Small Datasets via Batch Statistics Adaptation
Noguchi, A., & Harada, T. (2019) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00284 · Cited by 166
“We plotted some classes in the categories “dogs”, “birds”, “arthropods”, and “devices” using different colors, and used WordNet [19] to categorize these classes.”
Gill, D. M., Kissová, Z., Lee, J., & Prowse, V. L. (2018) · Management Science · doi:10.1287/mnsc.2017.2907 · Cited by 157
“Valid words were taken to be the words appearing in the “WordNet” (Miller, 1995; wordnet.princeton.edu) or “iSpell” (lasr.cs.ucla.edu/geoff/ispell.html) databases of English words.”
BackgroundOpen-Domain Question–Answering
Prager, J. M. (2007) · Foundations and Trends® in Information Retrieval · doi:10.1561/1500000001 · Cited by 157
Grounding words in perception and action: computational insights
Roy, D. K. (2005) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2005.06.013 · Cited by 159
Enhancing Pre-Trained Language Representations with Rich Knowledge for Machine Reading Comprehension
Yang, A., Wang, Q., Liu, J., Liu, K., Lyu, Y., Wu, H., She, Q., & Li, S. (2019) · Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/p19-1226 · Cited by 155
“We use two KBs: WordNet and NELL.”
MethodYoon, J., Park, H., & Kim, K. (2012) · Scientometrics · doi:10.1007/s11192-012-0830-6 · Cited by 155
Learning Design Semantics for Mobile Apps
Liu, T. F., Craft, M., Situ, J., Yumer, E., Měch, R., & Kumar, R. (2018) · ACM Symposium on User Interface Software and Technology (UIST) · doi:10.1145/3242587.3242650 · Cited by 178
Ontology-based Text Document Clustering.
Staab, S., & Andreas Hotho, A. (2002) · Künstliche Intelligenz · Cited by 155
Open-world Machine Learning: Applications, Challenges, and Opportunities
Parmar, J., Chouhan, S. S., Raychoudhury, V., & Rathore, S. S. (2022) · ACM Computing Surveys · doi:10.1145/3561381 · Cited by 163
“edu/data/amazon WordNet [90] https://wordnet.”
BackgroundResultInfluentialModeling Relation Paths for Knowledge Graph Completion
Shen, Y., Ding, N., Zheng, H.-T., Li, Y., & Yang, M. (2021) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2020.2970044 · Cited by 153
Learning Spatial Knowledge for Text to 3D Scene Generation
Chang, A., Savva, M., & Manning, C. D. (2014) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.3115/v1/d14-1217 · Cited by 154
“We use the WordNet hierarchy to resolve hyponym or hy-pernym referents to objects in the scene.”
MethodKnowledge base completion using embeddings and rules
Wang, Q., Wang, B., & Guo, L. (2015) · International Conference on Artificial Intelligence · Cited by 152
Hippocampal theta codes for distances in semantic and temporal spaces
Solomon, E. A., Lega, B. C., Sperling, M. R., & Kahana, M. J. (2019) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1906729116 · Cited by 149
Can Consumer-Posted Photos Serve as a Leading Indicator of Restaurant Survival? Evidence from Yelp
Zhang, M., & Luo, L. (2016) · Management Science · doi:10.1287/mnsc.2022.4359 · Cited by 169
Semantically Smooth Knowledge Graph Embedding
Guo, S., Wang, Q., Wang, B., Wang, L., & Guo, L. (2015) · doi:10.3115/v1/p15-1009 · Cited by 152
“Knowledge Graphs (KGs) like WordNet (Miller, 1995), Freebase (Bollacker et al., 2008), and DB-∗ pedia (Lehmann et al., 2014) have become extremely useful resources for many NLP relat-ed applications, such as word sense disambiguation (Agirre et al., 2014), named entity recognition (Magnini et al.,…”
BackgroundStance Classification of Context-Dependent Claims
Bar-Haim, R., Bhattacharya, I., Dinuzzo, F., Saha, A., & Slonim, N. (2017) · Conference of the European Chapter of the Association for Computational Linguistics · doi:10.18653/v1/e17-1024 · Cited by 153
“We consider morphological similarity, paths in WordNet (Miller, 1995; Fellbaum, 1998), and cosine similarity of word2vec embeddings (Mikolov et al., 2013).”
MethodSpam detection in social media using convolutional and long short term memory neural network
Jain, G., Шарма, М., & Agarwal, B. (2019) · Annals of Mathematics and Artificial Intelligence · doi:10.1007/s10472-018-9612-z · Cited by 156
A Two-Stage Approach to Few-Shot Learning for Image Recognition
Das, D., & Lee, C. S. G. (2019) · IEEE Transactions on Image Processing · doi:10.1109/tip.2019.2959254 · Cited by 152
“This suggested that an additional class relevance criterion based on Word-Net [68] might be more appropriate.”
BackgroundA comprehensive overview of knowledge graph completion
Shen, T., Zhang, F., & Cheng, J. (2022) · Knowledge-Based Systems · doi:10.1016/j.knosys.2022.109597 · Cited by 171
Graph-Theoretic Techniques for Web Content Mining
Schenker, A., Kandel, A., Horst Bunke, A., & Last, M. (2005) · Series in machine perception and artificial intelligence · doi:10.1142/5832 · Cited by 152
Open information extraction for the web
Etzioni, O., & Banko, M. (2009) · Cited by 152
PaStaNet: Toward Human Activity Knowledge Engine
Li, Y., Xu, L., Liu, X., Huang, X., Xu, Y., Wang, S., Fang, H.-S., Ma, Z., Chen, M., & Lu, 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/cvpr42600.2020.00046 · Cited by 156
The Benefits of a Model of Annotation
Passonneau, R. J., & Carpenter, B. (2014) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00185 · Cited by 151
“The items that were annotated are occurrences of words in their sentence contexts, and each label is a WordNet sense (Miller, 1995).”
BackgroundA semantic-expansion approach to personalized knowledge recommendation
Liang, T., Yang, Y.-F., Chen, D., & Ku, Y.-C. (2007) · Decision Support Systems · doi:10.1016/j.dss.2007.05.004 · Cited by 151
Atoms of recognition in human and computer vision
Ullman, S., Assif, L., Fetaya, E., & Harari, D. (2016) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1513198113 · Cited by 158
“The decision was based on the WordNet hierarchy (31): We allowed sister terms that have the same direct parent (hypernym) or two levels up.”
BackgroundThe Semantic Communication Game
Güler, B., Yener, A., & Swami, A. (2018) · IEEE Transactions on Cognitive Communications and Networking · doi:10.1109/tccn.2018.2872596 · Cited by 151
“Semantic relations between different concepts are often represented by using word taxonomies [29], such as the WordNet taxonomy illustrated in Fig.”
BackgroundMethodAutomated Crowdturfing Attacks and Defenses in Online Review Systems
Yao, Y., Viswanath, B., Cryan, J., Zheng, H., & Zhao, B. Y. (2017) · Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security · doi:10.1145/3133956.3133990 · Cited by 150
“Relevancy is estimated by calculating lexical similarity using WordNet [44], a widely used lexical database that groups English words into sets of synonyms and measures their concept relatedness [51].”
MethodRill, S., Reinel, D., Scheidt, J., & Zicari, R. V. (2014) · Knowledge-Based Systems · doi:10.1016/j.knosys.2014.05.008 · Cited by 150
“SWN and SOW were generated using the WordNet [24] lexical database.”
MethodForging high-quality User Stories: Towards a discipline for Agile Requirements
Lucassen, G., Dalpiaz, F., van der Werf, J. M. E. M., & Brinkkemper, S. (2015) · IEEE International Requirements Engineering Conference (RE) · doi:10.1109/re.2015.7320415 · Cited by 150
“IV using the WordNet lexical database [26] to detect semantic similarity.”
MethodZhong, Q., Ding, L., Liu, J., Du, B., Jin, H., & Tao, D. (2023) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2023.3250499 · Cited by 165
“3) Knowledge-Based Representations: To incorporate external knowledge and enrich the semantic features, we introduce the knowledge graphs of WordNet2 [48] as the external knowledge base, which contains more than 166,000 word form and sense pairs, and employs sets of synonyms to represent the concepts.”
Geographic knowledge extraction and semantic similarity in OpenStreetMap
Ballatore, A., Bertolotto, M., & Wilson, D. C. (2012) · Knowledge and Information Systems · doi:10.1007/s10115-012-0571-0 · Cited by 149
Zero-Shot Hashing via Transferring Supervised Knowledge
Yang, Y., Luo, Y., Chen, W., Shen, F., Shao, J., & Shen, H. T. (2016) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/2964284.2964319 · Cited by 148
“ImageNet is an image dataset organized according to the Word-Net [18] hierarchy.”
MethodCNN-RNN: a large-scale hierarchical image classification framework
Guo, Y., Liu, Y., Bakker, E. M., Guo, Y., & Lew, M. S. (2017) · Multimedia Tools and Applications · doi:10.1007/s11042-017-5443-x · Cited by 154
“ImageNet is an image dataset organized according to the WordNet hierarchy [27].”
MethodOptimizing semantic LSTM for spam detection
Jain, G., Шарма, М., & Agarwal, B. (2018) · International Journal of Information Technology · doi:10.1007/s41870-018-0157-5 · Cited by 153
“For the words not mapped in word2vec, a similar word is found using the semantic or conceptual dictionaries: WordNet [31] and ConceptNet [32].”
MethodSocial chemistry 101: Learning to reason about social and moral norms
Forbes, M., Hwang, J. D., Shwartz, V., Sap, M., & Choi, Y. (2020) · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2020.emnlp-main.48 · Cited by 169
Twitter Sentiment Analysis: A Bootstrap Ensemble Framework
Hassan, A., Abbasi, A., & Zeng, D. D. (2013) · International Conference on Social Computing · doi:10.1109/socialcom.2013.56 · Cited by 148
DLIREC: Aspect Term Extraction and Term Polarity Classification System
Toh, Z., & Wan, M. (2014) · International Workshop on Semantic Evaluation (SemEval) · doi:10.3115/v1/s14-2038 · Cited by 148
“This feature represents the set of syntactic categories (e.g “noun.food”) of the current token as organized in WordNet lexicographer files (Miller, 1995).”
BackgroundQuery Expansion Based on Crowd Knowledge for Code Search
Nie, L., Jiang, H., Ren, Z., Sun, Z., & Li, X. (2016) · IEEE Transactions on Services Computing · doi:10.1109/tsc.2016.2560165 · Cited by 150
“Lu et al. [28] propose a query expansion method denoted as P WordNet by leveraging the Part-Of-Speech (POS) of each word in queries and WordNet [29] to expand queries.”
MethodA semantic similarity-based perspective of affect lexicons for sentiment analysis
Araque, Ó., Zhu, G., & Iglesias, C. Á. (2018) · Knowledge-Based Systems · doi:10.1016/j.knosys.2018.12.005 · Cited by 155
Deep generative models for peptide design
Wan, F., Kontogiorgos-Heintz, D., & de la Fuente‐Núñez, C. (2022) · Digital Discovery · doi:10.1039/d1dd00024a · Cited by 154
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 ›
Are you an author, or do you see a problem with this page? Request a review
