GAMBL, genetic algorithm optimization of memory-based WSD

By B. Decadt, Veronique Hoste, Walter Daelemans, Antal van den Bosch. 2004

112 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Citations in the corpus

A semantic matching energy function for learning with multi-relational data (2013, methodology): “Finally, we report the F1-score of Gamble (Decadt et al. 2004), winner of Senseval-3, on our subset of its data.”
SemEval-2007 Task-17: English Lexical Sample, SRL and All Words (2007, methodology): “For the ﬁne-grained All Words sense tagging task, which has always used WordNet, the system performance has ranged from our 59% to 65.2 (Sen-seval3, (Decadt et al., 2004)) to 69% (Seneval2, (Chklovski and Mihalcea, 2002)).”
It Makes Sense: A Wide-Coverage Word Sense Disambiguation System for Free Text (2010, methodology): “To overcome the lack of sense-annotated training examples, besides the training instances from the widely used sense-annotated corpus SEMCOR (Miller et al., 1994) and DSO corpus (Ng and Lee, 1996), we also follow the approach described in Chan and Ng (2005) to extract more training ex- amples from…”
Joint Learning of Words and Meaning Representations for Open-Text Semantic Parsing (2012, result): “Finally, we also report the results of Gamble (Decadt et al., 2004 ), winner of Senseval-3, on our subset of its data.”
Incorporating Coreference Resolution into Word Sense Disambiguation (2011, background): “[3] proposed a memory-based approach which provides the best performance in senseval-3 all word tasks.”
Learning to Merge Word Senses (2007, methodology): “We consider the outputs of the top 3 all-words WSD systems that participated in Senseval-3: Gambl (Decadt et al., 2004), SenseLearner (Mihal-cea and Faruque, 2004 2004).”
An Experimental Study of Graph Connectivity for Unsupervised Word Sense Disambiguation (2010, background): “This is not surprising: GAMBL [69] is a memory-based system which learns word experts from an extensive collection of training sets.”