Learning to Merge Word Senses

By Rion Snow, Sushant Prakash, Dan Jurafsky, Andrew Y. Ng. 2007

119 citations recorded by the source corpus.

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Citations in the corpus

WiC: the Word-in-Context Dataset for Evaluating Context-Sensitive Meaning Representations (2018, background): “Sense clustering is not a very well-deﬁned problem (McCarthy et al., 2016) and there are different strategies to perform this sense distinction (Snow et al., 2007; Pilehvar et al., 2013; Mancini et al., 2017).”
Learning Structured Embeddings of Knowledge Bases (2011, methodology): “in (Ng and Cardie 2002; Snow et al. 2007)), this remains a small contribution compared to what could be achieved with such gigantic knowledge quantities.”
Sense discovery via co-clustering on images and text (2015, background): “For example, WordNet has good coverage of common nouns, however it has been criticized for being too fine-grained [41].”
Towards a Seamless Integration of Word Senses into Downstream NLP Applications (2017, methodology): “The issue can be tackled by grouping together similar senses of the same word, either using automatic clustering techniques (Nav-igli, 2006; Agirre and Lopez, 2003; Snow et al., 2007) or with the help of WordNet’s lexicographer ﬁles 5 .”
Probase: a probabilistic taxonomy for text understanding (2012, background): “It has been widely observed that different NLP applications require different sense granularities in order to best exploit word sense distinctions [33].”
From Word to Sense Embeddings: A Survey on Vector Representations of Meaning (2018, background): “It has been pointed out that sense distinctions in WordNet might be too ﬁne-grained to be useful for many NLP applications (Navigli, 2006; Snow et al., 2007; Hovy et al., 2013).”
Align, Disambiguate and Walk: A Unified Approach for Measuring Semantic Similarity (2013, unknown): “Following the evaluation methodology of Snow et al. (2007), we combine the Senseval-2 and OntoNotes datasets into a third dataset.”