Measuring semantic similarity between words using web search engines

628 citations recorded by the source corpus.

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A new sentence similarity measure and sentence based extractive technique for automatic text summarization (2009, methodology): “In paper Bollegala, Matsuo, and Ishizuka (2007) proposed a method which integrates both page counts and snippets to measure semantic similarity between a given pair of words.”
A Study on Similarity and Relatedness Using Distributional and WordNet-based Approaches (2009, methodology): “Bollegala et al. (2007) calculate a number of popular relatedness metrics based on page counts, like PMI, the Jaccard coefﬁcient, the Simpson co-efﬁcient and the Dice coefﬁcient, which are combined with lexico-syntactic patterns as model features.”
Short text classification in twitter to improve information filtering (2010, background): “Alternately, Bollegala et al [22] argues that page counts alone would not suffice to conclude similarity between words and proposes several page-count based similarity scores to reduce the effect of false positives.”
Ontology-based semantic similarity: A new feature-based approach (2012, background): “As stated in Bollegala et al. (2009), an objective evaluation of the accuracy of a semantic similarity function is difficult because the notion of similarity is a subjective human judgement.”
Learning to classify short and sparse text & web with hidden topics from large-scale data collections (2008, methodology): “2007 [10] use search engines to get the semantic relatedness between words.”