Semantic distance in WordNet: An experimental, application-oriented evaluation of five measures

By Alexander Budanitsky, Graeme Hirst, Keith Alcock, Jiang— Conrath. 2004

927 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/6db4e86e6377cd703aaaf3a3b471b62e033757ae

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Citations in the corpus

Comparison of Semantic Similarity for Different Languages Using the Google n-gram Corpus and Second-Order Co-occurrence Measures (2011, background): “Budanitsky and Hirst [1] compared 5 different measures of semantic similarity based on WordNet.”
SentiWS - A Publicly Available German-language Resource for Sentiment Analysis (2010, methodology): “Although we used the PMI without questioning it, we are very aware of the fact that the weighting itself needs to be evaluated and possibly contrasted with other weighting methods (Landauer and Dumais, 1997; Richardson et al., 1994; Budanitsky and Hirst, 2001; Biemann, 2006) 7.”
Evaluating WordNet-based Measures of Lexical Semantic Relatedness (2006, unknown): “Since the first publication of the initial results of this work (Budanitsky and Hirst, 2001), Pedersen and his colleagues (Pedersen et al.”
Words with attitude (2002, background): “A recent evaluation of five distance measure can be found in (Budanitsky and Hirst, 2001).”
An Approach for Measuring Semantic Similarity between Words Using Multiple Information Sources (2003, methodology): “This is in line with many application areas of computational linguistics and artificial intelligence [ 4 ], [8], [10], [22], [23].”
Investigating Semantic Similarity Measures Across the Gene Ontology: The Relationship Between Sequence and Annotation (2003, unknown): “…similarity had defined similarity measures either with specific applications in mind, such as malapropism detection, or word sense disambiguation (see (Fellbaum, 1998) and references therein), and had tested results against the expectations of people (Resnik, 1999; Budanitsky and Hirst, 2001).”
Using Measures of Semantic Relatedness for Word Sense Disambiguation (2003, methodology): “Our empirical evaluation follows the model of Budanitsky and Hirst [3], who compare the five measures of relatedness previously described when applied to context sensitive spelling correction.”
Vector-based Models of Semantic Composition (2008, methodology): “We selected Jiang and Conrath’s measure since it has been shown to perform consistently well across several cognitive and NLP tasks (Budanitsky and Hirst, 2001).”
Investigating interactions of trust and interest similarity (2007, background): “, see [7] and [52]), we require that interest score sco( pm) accorded to pm, which is super-topic to pm+1, depends on the number of siblings, denoted sib(pm+1), of pm+1: the fewer siblings pm+1 possesses, the more interest score is accorded to its super-topic node pm: scoðpmÞ 1⁄4 jd scoðpmþ1Þ sibðpm”
Mining Meaning from Wikipedia (2008, background): “Budanitsky and Hirst (2001) differentiate between semantic similarity, where only predefined taxonomic relations are used to compute similarity, and semantic relatedness, where other relations like has-part and is-made-of are used as well.”
Measures of semantic similarity and relatedness in the biomedical domain (2007, methodology): “In the realm of domain-independent Natural Language Processing, semantic similarity has been recently used in numerous tasks such as spelling correction [8], word sense disambiguation [10,27], information extraction [28] and textual inference [29].”
Cross-lingual Semantic Relatedness Using Encyclopedic Knowledge (2009, background): “…language processing applications, including word sense disambiguation (Patwardhan et al., 2003), synonym identiﬁcation (Turney, 2001), automated essay scoring (Foltz et al., 1999), malapropism detection (Budanitsky and Hirst, 2001), coreference resolution (Strube and Ponzetto, 2006), and others.”
Corpus-based and Knowledge-based Measures of Text Semantic Similarity (2006, methodology): “There are a number of measures that were developed to quantify the degree to which two words are semantically related using information drawn from semantic networks – see e.g. (Budanitsky & Hirst 2001) for an overview.”
An Improved Model of Semantic Similarity Based on Lexical Co-Occurrence (2005, methodology): “WordNet itself does not provide a word-pair similarity metric, but various metrics based on its structure have been developed (Rada, Mili, Bicknell, & Blettner, 1989; Budanitsky & Hirst, 2001; Patwardhan, Banerjee, & Pedersen, 2003).”