WordNet : an electronic lexical database

By Christiane Fellbaum. 2000

784 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Natural Language Processing, Evaluation & Benchmarks

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

In Natural Language Processing

In Evaluation & Benchmarks

Citations in the corpus

SemEval-2017 Task 1: Semantic Textual Similarity Multilingual and Crosslingual Focused Evaluation (2017, background): “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 discus”
Distributional Memory: A General Framework for Corpus-Based Semantics (2010, neutral): “The Almuhareb–Poesio (AP) set includes 402 concepts from WordNet, balanced in terms of frequency and ambiguity.”
Ontologies are us: A unified model of social networks and semantics (2005, neutral): “Emergent semantics is likely to best complementwell-established, but slowly evolving ontologies suchasWordNet [8],which lack the associative component.8 Wehave also compared the two networks based on object and person overlap and noted the advantage of the second network: the possibility to extract ”
The New C Standard (Excerpted material) An Economic and Cultural Commentary (2008, neutral): “16[105] is a semantic network whose design was inspired by current psycholinguistic theories WordNet of human lexical memory.”
Linking and Extending an Open Multilingual Wordnet (2013, neutral): “One of the many attractions of the semantic network WordNet (Fellbaum, 1998), is that there are numerous wordnets being built for different languages.”
Learning about the world through long-term query logs (2008, neutral): “, applications using WordNet [Fellbaum 1998]).”
Exploring place through user-generated content: Using Flickr tags to describe city cores (2010, background): “In a large scale analysis of users’ tagging behavior and the information provided through tags, Sigurbörnsson and van Zwol [34] found that 28% of the tags in a random set of 52 million photos from Flickr corresponded to the location type of WordNet [8] categories.”
Using Machine Translation Evaluation Techniques to Determine Sentence-level Semantic Equivalence (2005, methodology): “The semantic distance measure we used was that of (Jiang and Conrath, 1997) defined using the relationships between words in the WordNet database (Fellbaum, 1998).”