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About: WordNet is a(n) research topic. Over the lifetime, 6097 publication(s) have been published within this topic receiving 230066 citation(s). The topic is also known as: Princeton WordNet. more


Proceedings ArticleDOI: 10.1109/CVPR.2009.5206848
Jia Deng1, Wei Dong1, Richard Socher1, Li-Jia Li1  +2 moreInstitutions (1)
20 Jun 2009-
Abstract: The explosion of image data on the Internet has the potential to foster more sophisticated and robust models and algorithms to index, retrieve, organize and interact with images and multimedia data. But exactly how such data can be harnessed and organized remains a critical problem. We introduce here a new database called “ImageNet”, a large-scale ontology of images built upon the backbone of the WordNet structure. ImageNet aims to populate the majority of the 80,000 synsets of WordNet with an average of 500-1000 clean and full resolution images. This will result in tens of millions of annotated images organized by the semantic hierarchy of WordNet. This paper offers a detailed analysis of ImageNet in its current state: 12 subtrees with 5247 synsets and 3.2 million images in total. We show that ImageNet is much larger in scale and diversity and much more accurate than the current image datasets. Constructing such a large-scale database is a challenging task. We describe the data collection scheme with Amazon Mechanical Turk. Lastly, we illustrate the usefulness of ImageNet through three simple applications in object recognition, image classification and automatic object clustering. We hope that the scale, accuracy, diversity and hierarchical structure of ImageNet can offer unparalleled opportunities to researchers in the computer vision community and beyond. more

Topics: WordNet (57%), Image retrieval (54%)

31,274 Citations

Journal ArticleDOI: 10.1145/219717.219748
George A. Miller1Institutions (1)
Abstract: 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. WordNet1 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]. more

Topics: WordNet (68%), Lexical database (64%), eXtended WordNet (63%) more

13,247 Citations

Journal ArticleDOI: 10.2307/417141
01 Sep 2000-Language
Abstract: Part 1 The lexical database: nouns in WordNet, George A. Miller modifiers in WordNet, Katherine J. Miller a semantic network of English verbs, Christiane Fellbaum design and implementation of the WordNet lexical database and searching software, Randee I. Tengi. Part 2: automated discovery of WordNet relations, Marti A. Hearst representing verb alterations in WordNet, Karen T. Kohl et al the formalization of WordNet by methods of relational concept analysis, Uta E. Priss. Part 3 Applications of WordNet: building semantic concordances, Shari Landes et al performance and confidence in a semantic annotation task, Christiane Fellbaum et al WordNet and class-based probabilities, Philip Resnik combining local context and WordNet similarity for word sense identification, Claudia Leacock and Martin Chodorow using WordNet for text retrieval, Ellen M. Voorhees lexical chains as representations of context for the detection and correction of malapropisms, Graeme Hirst and David St-Onge temporal indexing through lexical chaining, Reem Al-Halimi and Rick Kazman COLOR-X - using knowledge from WordNet for conceptual modelling, J.F.M. Burg and R.P. van de Riet knowledge processing on an extended WordNet, Sanda M. Harabagiu and Dan I Moldovan appendix - obtaining and using WordNet. more

Topics: eXtended WordNet (74%), EuroWordNet (72%), WordNet (70%) more

12,607 Citations

Open accessJournal ArticleDOI: 10.1093/IJL/3.4.235
Abstract: Standard alphabetical procedures for organizing lexical information put together words that are spelled alike and scatter words with similar or related meanings haphazardly through the list. Unfortunately, there is no obvious alternative, no other simple way for lexicographers to keep track of what has been done or for readers to find the word they are looking for. But a frequent objection to this solution is that finding things on an alphabetical list can be tedious and time-consuming. Many people who would like to refer to a dictionary decide not to bother with it because finding the information would interrupt their work and break their train of thought. more

  • Figure 1. Bipolar Adjective Structure
    Figure 1. Bipolar Adjective Structure
  • Figure 3. Four kinds of entailment relations among verbs
    Figure 3. Four kinds of entailment relations among verbs
  • Table 1
    Table 1
  • Table 4
    Table 4
  • Table 2
    Table 2
  • + 3

Topics: Lexical database (58%), WordNet (54%), eXtended WordNet (51%)

4,814 Citations

Open accessProceedings ArticleDOI: 10.1145/1242572.1242667
08 May 2007-
Abstract: We present YAGO, a light-weight and extensible ontology with high coverage and quality. YAGO builds on entities and relations and currently contains more than 1 million entities and 5 million facts. This includes the Is-A hierarchy as well as non-taxonomic relations between entities (such as HASONEPRIZE). The facts have been automatically extracted from Wikipedia and unified with WordNet, using a carefully designed combination of rule-based and heuristic methods described in this paper. The resulting knowledge base is a major step beyond WordNet: in quality by adding knowledge about individuals like persons, organizations, products, etc. with their semantic relationships - and in quantity by increasing the number of facts by more than an order of magnitude. Our empirical evaluation of fact correctness shows an accuracy of about 95%. YAGO is based on a logically clean model, which is decidable, extensible, and compatible with RDFS. Finally, we show how YAGO can be further extended by state-of-the-art information extraction techniques. more

  • Table 5: Sample facts of YAGO
    Table 5: Sample facts of YAGO
  • Table 7: Leila headquarteredIn facts
    Table 7: Leila headquarteredIn facts
  • Table 6: Sample queries on YAGO
    Table 6: Sample queries on YAGO
  • Table 4: Size of other ontologies
    Table 4: Size of other ontologies
  • Table 2: Size of YAGO (facts)
    Table 2: Size of YAGO (facts)
  • + 2

Topics: Ontology (information science) (53%), WordNet (53%), Knowledge extraction (52%) more

3,180 Citations

No. of papers in the topic in previous years

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Topic's top 5 most impactful authors

Maciej Piasecki

67 papers, 785 citations

Christiane Fellbaum

60 papers, 6.9K citations

German Rigau

52 papers, 1.6K citations

Pushpak Bhattacharyya

50 papers, 465 citations

Eneko Agirre

42 papers, 3.2K citations

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