An Intrinsic Information Content Metric for Semantic Similarity in WordNet

By Nuno Seco, Tony Veale, Jer Hayes. European Conference on Artificial Intelligence, 2004

668 citations recorded by the source corpus.

How researchers see this paper

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Domain-specific Knowledge Extraction from the Web of Data (2018, methodology): “We utilize the structural semantics of hierarchies to quantify the abstractness of a category [Resnik 1995] [Seco et al. 2004].”
Semantic similarity methods in wordNet and their application to information retrieval on the web (2005, methodology): “In this work information content is computed according to [21]: WordNet is used as a statistical resource for computing the probabilities of occurrence of terms.”
Question Classification using Head Words and their Hypernyms (2008, methodology): “…paper, we propose two approaches to augment WordNet semantic features, with the ﬁrst augmenting the hyper-nyms of head words as extracted in previous section directly, and the second making use of a WordNet similarity package (Seco et al., 2004), which implicitly employs the structure of hypernyms.”
Ontology-based semantic similarity: A new feature-based approach (2012, methodology): “Following a similar principle as intrinsic IC approaches (Seco et al., 2004; Zhou et al., 2008), in which the IC of the concept is computed as a non-linear function of the amount of semantic features, we introduce the logarithm in our calculation, obtaining the following formulation.”