Testing the Distributioanl Hypothesis: The influence of Context on Judgements of Semantic Similarity

By Scott McDonald, Michael Ramscar. 2001

129 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/878e4a4bcbd9938269cc44e28888e7492bd1df93

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Citations in the corpus

Using WordNet-based Context Vectors to Estimate the Semantic Relatedness of Concepts (2006, background): “Additionally, numerous studies (Carnine et al., 1984; Miller and Charles, 1991; McDonald and Ramscar, 2001) have shown that context plays a vital role in deﬁning the meanings of words.”
Representing words as regions in vector space (2009, methodology): “In psychology, vector space models have been used to model synonymy (Landauer and Dumais, 1997; Padó and Lapata, 2007), lexical priming phenomena (Lowe and McDonald, 2000), and similarity judgments (McDonald and Ramscar, 2001).”
Frequency in lexical processing (2016, result): “This measure reflects collocational richness and is similar in spirit to the work by McDonald and Shillcock (2001) and McDonald and Ramscar (2001) on the importance of a word’s contextual distinctiveness.”
Measures of semantic similarity and relatedness in the biomedical domain (2007, background): “Research [2,33,34] has shown that humans use the context of words and concepts to build a mental semantic representation of concepts.”
Vector Space Models of Word Meaning and Phrase Meaning: A Survey (2012, background): “One fact that points in the latter direction is that human subjects are able to induce the meaning of unknown words from distributional context (McDonald and Ramscar 2001).”
A Structured Vector Space Model for Word Meaning in Context (2008, methodology): “These vectors are able to provide a robust model of semantic similarity that has been used in NLP (Salton et al., 1975; McCarthy and Carroll, 2003; Manning et al., 2008) and to model experimental results in cognitive science (Landauer and Dumais, 1997; McDonald and Ramscar, 2001).”
An amorphous model for morphological processing in visual comprehension based on naive discriminative learning. (2011, methodology): “53 f (e ) = 72 18 2 Following Lund and Burgess (1996a); Landauer and Dumais (1997); McDonald and Ramscar (2001); Moscoso del Prado Mart́ın, Kostić, and Filipović Durdević (2009) and Filipović Durdević, Durdević, and Kostić (2008), we used a cosine similarity measure to represent the semantic…”