Using Corpus Statistics and WordNet Relations for Sense Identification

By Claudia Leacock, Martin Chodorow, George A. Miller. 1998

Subfields: Natural Language Processing, Computational Linguistics & Semantics

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Citations in the corpus

ITNLP-AiKF at SemEval-2017 Task 1: Rich Features Based SVR for Semantic Textual Similarity Computing (2017, methodology): “Lch similarity (Leacock et al., 1998) measure two words similarity by using the depth of concepts in the WordNet hierarchy tree.”
Word Embedding based Edit Distance (2018, background): “For example, one can take the minimum distance between two words in WordNet as similarity between them, and calculate the similarity between two sentences on the basis of their words (Wu and Palmer, 1994, Leacock et al., 1998, Fernando and Stevenson, 2008).”
Using Wikipedia for Automatic Word Sense Disambiguation (2007, methodology): “A first set of methods consists of algorithms that generate sense annotated data using words semantically related to a given ambiguous word (Leacock et al., 1998; Mihalcea and Moldovan, 1999; Agirre and Martinez, 2004).”
On the Use of Automatically Acquired Examples for All-Nouns Word Sense Disambiguation (2008, methodology): “The seminal work of Leacock, Chodorow, and Miller (1998) showed that the approach was promising, with good results on a small sample of nouns.”
Ontology Matching (2007, methodology): “A similar measure is the one of Leacock–Chodorow (Leacock et al. 1998), which is function of the length of the shortest path.”
Automatic Evaluation of Topic Coherence (2010, methodology): “The measure of semantic similarity devised by Leacock et al. (1998) finds the shortest path between two WordNet synsets (sp(c1, c2)) using hypernym and synonym relationships.”
Enriching very large ontologies using the WWW (2000, background): “close relatives for a given concept, in contrast to (Leacock et al., 1998) which only focuses on monosemous relatives, and (Mihalcea and Moldovan, 1999) which uses synonyms and a different strategy to process the gloss.”