Structural semantic interconnection: a knowledge-based approach to Word Sense Disambiguation

By Roberto Navigli, Paola Velardi. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2005

368 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/312e6b42a75b2dc510aa30924da4ea7018697deb

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In Artificial Intelligence

Citations in the corpus

Meaningful Clustering of Senses Helps Boost Word Sense Disambiguation Performance (2006, methodology): “Each collocation is mapped to the WordNet sense inventory in a semi-automatic manner and transformed into a relatedness edge (Navigli and Velardi, 2005).”
BabelNet: The automatic construction, evaluation and application of a wide-coverage multilingual semantic network (2012, methodology): “We also compare with Structural Semantic Interconnections [94] (SSI) – a knowledge-based system that participated out of competition – and the unsupervised proposal from [19] (TreeMatch).”
A Survey of Automatic Query Expansion in Information Retrieval (2012, background): “In [Navigli and Velardi 2005], the authors argue that instead of replacing a given query word with its synonyms, hyperonyms, and hyponyms, it might be better to extract the concepts that pertain to the same semantic domain of query, through other types of definitional information derivable from Word”
Query Expansion Techniques for Information Retrieval: a Survey (2017, positive): “For example, reference [196] suggests that instead of considering the replacement of the initial query term with its synonyms, hyponyms, and hyperonyms, it is better to extract the similar concept from the same domain of the given query from WordNet (such as the common nodes and glossy terms).”
Using Wikipedia for Automatic Word Sense Disambiguation (2007, background): “Among the various knowledge-based (Lesk, 1986; Galley and McKeown, 2003; Navigli and Velardi, 2005) and data-driven (Yarowsky, 1995; Ng and Lee, 1996; Pedersen, 2001) word sense disambiguation methods that have been proposed to date, supervised systems have been constantly observed as leading to the”
Wikify!: linking documents to encyclopedic knowledge (2007, background): “[13, 16, 22], and (2) data-driven algorithms that are based on probabilities collected from large amounts of sense-annotated data, e.”
Incorporating Coreference Resolution into Word Sense Disambiguation (2011, methodology): “Some WSD systems are built on lexical knowledge base [4-9].”
Personalizing PageRank for Word Sense Disambiguation (2009, methodology): “In (Navigli and Velardi, 2005) the authors develop a knowledge-based WSD method based on lexical chains called structural semantic intercon-”
An Experimental Study of Graph Connectivity for Unsupervised Word Sense Disambiguation (2010, methodology): “Graph-based algorithms often consist of two stages [4], [9], [15].”