The Senseval-3 English lexical sample task

By Rada Mihalcea, Timothy Chklovski, Adam Kilgarriff. 2004

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Citations in the corpus

context2vec: Learning Generic Context Embedding with Bidirectional LSTM (2016, methodology): “As our supervised WSD dataset we used the Senseval-3 lexical sample dataset (Mihalcea et al., 2004), denoted SE-3, which includes 7,860 train and 3,944 test instances.”
Embeddings for Word Sense Disambiguation: An Evaluation Study (2016, methodology): “As benchmark for this experiment, we considered the Senseval-2 (Edmonds and Cotton, 2001), Senseval-3 (Snyder and Palmer, 2004), and SemEval-2007 (Pradhan et al., 2007) English all-words tasks.”
It Makes Sense: A Wide-Coverage Word Sense Disambiguation System for Free Text (2010, result): “Table 2 compares the performance of our system to the top two systems that participated in the above tasks (Yarowsky et al., 2001; Mihalcea and Moldovan, 2001; Mihalcea et al., 2004).”
BabelNet: The automatic construction, evaluation and application of a wide-coverage multilingual semantic network (2012, methodology): “In this task, a variety of knowledge bases are assessed by first generating so-called topic signatures [65] and then using these to perform monolingual Word Sense Disambiguation (WSD) on standard datasets from previous Senseval [74] and SemEval [109] competitions.”
Large-Scale Named Entity Disambiguation Based on Wikipedia Data (2007, background): “This task, which has supported large-scale evaluations – SENSEVAL 1-3 (Kilgarriff and Rosenzweig, 2000; Edmonds and Cotton, 2001; Mihalcea et al., 2004) – aims to assign dictionary meanings to all the instances of a predetermined set of polysemous words in a corpus (for example, choose whether the…”
GERBIL - Benchmarking Named Entity Recognition and Linking consistently (2017, methodology): “Also, GERBIL includes the Senseval 2 and 3 datasets which took place 2001 and 2004 respectively and activates them for the Entity Recognition task only [16,37].”
Neural Sequence Learning Models for Word Sense Disambiguation (2017, background): “Finally, K˚ageb¨ack and Salomonsson (2016) present a supervised classiﬁer based on bidirectional LSTM for the lexical sample task (Kilgarriff, 2001; Mihalcea et al., 2004).”
Multi-task Learning for Universal Sentence Embeddings: A Thorough Evaluation using Transfer and Auxiliary Tasks (2018, methodology): “Following Melamud et al. (2016), we conduct the supervised WSD task using Senseval-3 lexical sample dataset (Mihalcea et al., 2004) using the publicly available tool 5 and report F1 score.”
AutoExtend: Extending Word Embeddings to Embeddings for Synsets and Lexemes (2015, methodology): “† We do a grid search (step size .1) for optimal values of α and β , optimizing the average score of Senseval-2 and Senseval-3.”
Word Sense and Subjectivity (2006, methodology): “The experiments are performed on the set of ambiguous nouns from the SENSEVAL-3 English lexical sample evaluation (Mihalcea et al., 2004).”