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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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“WN18RR is a subset of WordNet [8], which features lexical relationships between word senses.”
From Modeling Heterogeneous Hierarchies with Relation-specific Hyperbolic Cones (2021) · cites this paper as [8]
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Introduction to WordNet: An On-line Lexical Database *
Miller, G. A., Beckwith, R., Fellbaum, C., Gross, D., & Miller, K. J. (1990) · International Journal of Lexicography · doi:10.1093/ijl/3.4.235 · Cited by 4,197
Self-citationFREQUENCY ANALYSIS OF ENGLISH USAGE: LEXICON AND GRAMMAR
Francis, W. N., Kučera, H., & Mackie, A. W. (1983) · Medical Entomology and Zoology · Cited by 2,310
Contextual correlates of semantic similarity
Miller, G. A., & Charles, W. G. (1991) · Language and Cognitive Processes · doi:10.1080/01690969108406936 · Cited by 1,634 · cited 1×
“Miller and Charles [ 5 ] proposed that a contextual representation associated with each sense characterizes sentential contexts in which a given word can be used to express that sense.”
BackgroundSelf-citationMiller, G. A., Leacock, C., Tengi, R., & Bunker, R. T. (1993) · DARPA Workshop on Speech and Natural Language (HLT) · doi:10.3115/1075671.1075742 · Cited by 612 · cited 1×
“Semantic concordances are being prepared to provide a basis for empirical studies of sense identification [7].”
BackgroundSelf-citationMiller, G. A., & Fellbaum, C. (1991) · Cognition · doi:10.1016/0010-0277(91)90036-4 · Cited by 452 · cited 1×
“The semantic relations in WordNet [6] were chosen because they apply broadly throughout English and because they are familiar— a user need not have advanced training in linguistics to understand them.”
MethodSelf-citationTowards Building Contextual Representations of Word Senses Using Statistical Models
Leacock, C., Towell, G. G., & Voorhees, E. M. (1993) · Cited by 29
The categorization of sentential contexts
Charles, W. G. (1988) · Journal of Psycholinguistic Research · doi:10.1007/bf01067226 · Cited by 12 · cited 1×
“People are quite skillful in making such distinctions [1].”
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- Francis W. N., 1982, Mass.
- Leacock C., 1993, Proceedings of the Workshop on the Acquisition of Lexical Knowledge from Text, 10
- Miller G. A., 1992, B. Levin and, 197
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“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.”
MethodInfluentialDRC: A dual route cascaded model of visual word recognition and reading aloud.
Coltheart, M., Rastle, K., Perry, C., Langdon, R., & Ziegler, J. (2001) · Psychological Review · doi:10.1037/0033-295x.108.1.204 · Cited by 4,500
Knowledge Graph Embedding by Translating on Hyperplanes
Wang, Z., Zhang, J., Feng, J., & Chen, Z. (2014) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v28i1.8870 · Cited by 3,915
“2008), WordNet (Miller 1995) and GeneOntology (Ashburner et al.”
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Lin, Y., Liu, Z., Sun, M., Liu, Y., & Zhu, X. (2015) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v29i1.9491 · Cited by 3,749
“In WordNet, each entity is a synset consisting of several words, corresponding to a distinct word sense.”
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Bowman, S. R., Angeli, G., Potts, C., & Manning, C. D. (2015) · Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/d15-1075 · Cited by 3,377
MoleculeNet: a benchmark for molecular machine learning
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Datta, R., Joshi, D., Li, J., & Wang, J. Z. (2008) · ACM Computing Surveys · doi:10.1145/1348246.1348248 · Cited by 3,080
Places: A 10 Million Image Database for Scene Recognition
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“Candidate images are queried from several Image search engines using the set of WordNet synonyms.”
MethodInfluentialA Survey on Knowledge Graphs: Representation, Acquisition, and Applications
Ji, S., Pan, S., Cambria, E., Marttinen, P., & Yu, P. S. (2022) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2021.3070843 · Cited by 2,947
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“Aloimonos et al. [10, 26] propose a twolevel organization of activities into 6 groups: ground, general object, general person, specific object, specific person, group; which connects to verbs in WordNet.”
MethodAudio Set: An ontology and human-labeled dataset for audio events
Gemmeke, J. F., Ellis, D. P. W., Freedman, D., Jansen, A., Lawrence, W., Moore, R. C., Plakal, M., & Ritter, M. (2017) · 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) · doi:10.1109/icassp.2017.7952261 · Cited by 3,134
“We refined this initial structure by comparing it with existing audio event lists or taxonomies, including [9, 20, 11, 12, 21, 8, 17].”
MethodAffectNet: A Database for Facial Expression, Valence, and Arousal Computing in the Wild
Mollahosseini, A., Hasani, B., & Mahoor, M. H. (2019) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2017.2740923 · Cited by 2,338
“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].”
MethodSimplifying Graph Convolutional NetworksPreprint
Wu, F., Zhang, T., Junior, A. H. D. S., Fifty, C., Yu, T., & Weinberger, K. Q. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1902.07153 · Cited by 2,307
“, 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.”
MethodAttention-Based Bidirectional Long Short-Term Memory Networks for Relation Classification
Zhou, P., Shi, W. Y., Tian, J., Qi, Z., Li, B., Hao, H., & Xu, B. (2016) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p16-2034 · Cited by 2,147
EDA: Easy Data Augmentation Techniques for Boosting Performance on Text Classification Tasks
Wei, J., & Zou, K. (2019) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d19-1670 · Cited by 2,537
“All synonyms for synonym replacements and random insertions were generated using WordNet (Miller, 1995).”
MethodKnowledge Graph Embedding via Dynamic Mapping Matrix
Ji, G., He, S., Xu, L., Liu, K., & Zhao, J. (2015) · Annual Meeting of the Association for Computational Linguistics · doi:10.3115/v1/p15-1067 · Cited by 1,943
“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
Baader, F., Calvanese, D., McGuinness, D. L., Nardi, D., & Patel‐Schneider, P. F. (2009) · Cited by 1,872
The rise of deep learning in drug discovery
Chen, H., Engkvist, O., Wang, Y., Olivecrona, M., & Blaschke, T. (2018) · Drug Discovery Today · doi:10.1016/j.drudis.2018.01.039 · Cited by 1,770
“Inspired by WordNet [89] and ImageNet [90], Wu et al .”
MethodGOT-10k: A Large High-Diversity Benchmark for Generic Object Tracking in the Wild
Huang, L., Zhao, X., & Huang, K. (2021) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2019.2957464 · Cited by 1,809
“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.”
BackgroundMethodInfluentialText Data Augmentation for Deep Learning
Shorten, C., Khoshgoftaar, T. M., & Furht, B. (2021) · Journal Of Big Data · doi:10.1186/s40537-021-00492-0 · Cited by 1,852
“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
Maedche, A. (2002) · Kluwer international series in engineering and computer science · doi:10.1007/978-1-4615-0925-7 · Cited by 1,683
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“Curated Yes Cyc/OpenCyc [23], WordNet [24], UMLS [25]”
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Socher, R., Chen, D., Manning, C. D., & Ng, A. Y. (2013) · Cited by 1,656
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“embeddings including attributes, word2vec [27], glove [56] and wordnet hierarchy [57].”
BackgroundBuilding Watson: An Overview of the DeepQA Project
Ferrucci, D. A., Brown, E. W., Chu‐Carroll, J., Fan, J., Gondek, D. C., Kalyanpur, A. A., Lally, A., Murdock, J. W., Nyberg, E., Prager, J. M., et al. (2010) · AI Magazine · doi:10.1609/aimag.v31i3.2303 · Cited by 1,523
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Otter, D. W., Medina, J. R., & Kalita, J. K. (2021) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2020.2979670 · Cited by 1,836
“, WordNet [159]) or knowledge graphs (e.”
GQA: A New Dataset for Real-World Visual Reasoning and Compositional Question Answering
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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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Banko, M., Cafarella, M., Soderland, S. G., Broadhead, M., & Etzioni, O. (2007) · Cited by 1,410
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Chen, W.-Y., Liu, Y., Kira, Z., Wang, Y.-C. F., & Huang, J. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1904.04232 · Cited by 1,644
“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).”
BackgroundVisualizing a field of research: A methodology of systematic scientometric reviews
Chen, C., & Song, M. (2019) · PLoS ONE · doi:10.1371/journal.pone.0223994 · Cited by 1,432
“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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“WordNet-Affect [4], SentiWordNet [5, 6], WordNet [133], Opinion Lexicon (OL) [38], AFINN [55], SO-CAL [69], Subjectivity Lexicon [104], General Inquirer [105], SenticNet [106], SentiSense [113], and Micro-WNOp [119] together.”
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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…”
BackgroundGeneric Schema Matching with Cupid
Madhavan, J., Bernstein, P. A., & Rahm, E. (2001) · Cited by 1,296
Terminology for biorelated polymers and applications (IUPAC Recommendations 2012)
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Ontology learning for the Semantic Web
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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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Huth, A. G., Nishimoto, S., Vu, A. T., & Gallant, J. L. (2012) · Neuron · doi:10.1016/j.neuron.2012.10.014 · Cited by 932
Novak, P. K., Smailović, J., Sluban, B., & Mozetič, I. (2015) · PLoS ONE · doi:10.1371/journal.pone.0144296 · Cited by 980
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Learning Deep Representations of Fine-Grained Visual Descriptions
Reed, S., Akata, Z., Lee, H., & Schiele, B. (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.2016.13 · Cited by 818
“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 999
“For example, WordNet [117] represents semantic and lexical relations of words such as synonymy.”
BackgroundBetter Word Representations with Recursive Neural Networks for Morphology
Luong, M.-T., Socher, R., & Manning, C. D. (2013) · Conference on Computational Natural Language Learning · Cited by 789
Gatt, A., & Krahmer, E. (2018) · Journal of Artificial Intelligence Research · doi:10.1613/jair.5477 · Cited by 776
“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…”
BackgroundInfluentialEvaluating ontological decisions with OntoClean
Guarino, N., & Welty, C. A. (2002) · Communications of the ACM · doi:10.1145/503124.503150 · Cited by 762
Retrofitting Word Vectors to Semantic Lexicons
Faruqui, M., Dodge, J., Jauhar, S. K., Dyer, C., Hovy, E. H., & Smith, N. A. (2015) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.3115/v1/n15-1184 · Cited by 785
Short text classification in twitter to improve information filtering
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News implied volatility and disaster concerns
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Latent Embeddings for Zero-Shot Classification
Xian, Y., Akata, Z., Sharma, G., Nguyen, Q., Hein, M., & Schiele, B. (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.2016.15 · Cited by 725
“wordnet hierarchies [24], or costly human annotations e.”
BackgroundMethodInfluentialA Survey of Data Augmentation Approaches for NLP
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Objaverse: A Universe of Annotated 3D Objects
Deitke, M., Schwenk, D., Salvador, J., Weihs, L., Michel, O., VanderBilt, E., Schmidt, L., Ehsanit, K., Kembhavi, A., & Farhadi, A. (2023) · 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52729.2023.01263 · Cited by 725
“We estimate that the objects have coverage for nearly 21K WordNet entities [48] (see appendix for details).”
BackgroundEnabling Personalized Search over Encrypted Outsourced Data with Efficiency Improvement
Fu, Z.-J., Ren, K., Shu, J., Sun, X.-M., & Huang, F. (2016) · IEEE Transactions on Parallel and Distributed Systems · doi:10.1109/tpds.2015.2506573 · Cited by 745
“WordNet [20] is a lexical database available online, which offers a large repository of English lexical items.”
BackgroundLogAnomaly: 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
“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 741
““An Empirical Study of Semantic Similarity in WordNet and Word2Vec.””
MethodInfluentialKEPLER: 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
“(2) KEPLER-WordNet WordNet (Miller, 1995) is an English lexical graph, where nodes are lemmas and synsets, and edges are their relations.”
BackgroundChen, Z., & Liu, B. (2018) · Synthesis Lectures on Artificial Intelligence and Machine Learning · doi:10.2200/s00737ed1v01y201610aim033 · Cited by 154
Can a Suit of Armor Conduct Electricity? A New Dataset for Open Book Question Answering
Mihaylov, T., Clark, P. E., Khot, T., & Sabharwal, A. (2018) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d18-1260 · Cited by 1,602
“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 696
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 671
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 724
Knowledge 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
“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).”
MethodLearning Syntactic Patterns for Automatic Hypernym Discovery
Snow, R., Jurafsky, D. S., & Ng, A. Y. (2004) · neural information processing systems · Cited by 658
SemEval-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
“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
“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 .”
MethodGRAM: 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
Deriving 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
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 718
“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
“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 616
“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 647
“, 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
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
“We group collocations whose pairs of words are synonyms and use Wordnet as a synonym dictionary.”
MethodInfluentialA 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
“Here we count event as any hyponym of ‘event’ or ‘process’ in WordNet (Miller., 1995).”
MethodSentiment 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
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
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 657
“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
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
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
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
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 565
Mihalcea, R. F. (2005) · doi:10.3115/1220575.1220627 · Cited by 541
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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