The English all-words task

By Benjamin Snyder, Martha Palmer. SENSEVAL@ACL, 2004

386 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/4fe3339321a29d3f175e7586290398f9a55f4310

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Citations in the corpus

Coarse Word-Sense Disambiguation Using Common Sense (2010, methodology): “In the fine-grained Senseval-3 WSD task, there was an inner-annotator agreement of 72.5% (Snyder and Palmer 2004); this annotation used expert lexicographers.”
Neural Sequence Learning Models for Word Sense Disambiguation (2017, background): “, 2017) and tested over a wide variety of standard benchmarks (Edmonds and Cotton, 2001; Snyder and Palmer, 2004; Pradhan et al., 2007; Navigli et al., 2007, 2013; Moro and Navigli, 2015).”
Does BERT Make Any Sense? Interpretable Word Sense Disambiguation with Contextualized Embeddings (2019, background): “The all-words task (Edmonds and Cotton, 2001; Snyder and Palmer, 2004) only provides a small number of documents as test data where each ambiguous word is anno-”
An Experimental Study of Graph Connectivity for Unsupervised Word Sense Disambiguation (2010, background): “Although supervised approaches outperform their unsupervised alternatives (see [5], [6] for overviews), they often require large amounts of training data to yield reliable results [7], and their coverage is typically limited to the words for which sense-labeled data exist.”
Word sense disambiguation: A survey (2009, background): “2007], whereas on fine-grained, WordNet-style sense inventories the inter-annotator agreement is estimated between 67% and 80% [Chklovski and Mihalcea 2003; Snyder and Palmer 2004; Palmer et al. 2007].”
ChatGPT: Jack of all trades, master of none (2023, methodology): “The framework consists of five evaluation datasets with standard English texts from Senseval [91, 92] and Semeval [93, 94, 95] competitions.”
Unsupervised Large-Vocabulary Word Sense Disambiguation with Graph-based Algorithms for Sequence Data Labeling (2005, methodology): “The algorithm was also evaluated on two other data sets, S ENSEVAL -3 English all-words data (Snyder and Palmer, 2004) and a subset of SemCor (Miller et al., 1993), although only ﬁne-grained sense evaluations could be conducted on these test sets.”
Word Sense Disambiguation: A Unified Evaluation Framework and Empirical Comparison (2017, background): “• Senseval-3 task 1 (Snyder and Palmer, 2004).”
Incorporating Coreference Resolution into Word Sense Disambiguation (2011, methodology): “Supervised WSD approaches provide the state of the art performances in benchmark evaluation [2].”
Personalizing PageRank for Word Sense Disambiguation (2009, methodology): “public evaluations (Palmer et al., 2001; Snyder and Palmer, 2004; Pradhan et al., 2007) but they need large amounts of hand-tagged data, which is typically very expensive to build.”
Unsupervised Graph-basedWord Sense Disambiguation Using Measures of Word Semantic Similarity (2007, unknown): “The experiments are primarily carried out on the SENSEVAL-2 [13] and SENSEVAL-3 [16] English all-words data sets.”
MASC: the Manually Annotated Sub-Corpus of American English (2008, background): “Some of these systems were among the top performers in SENSEVAL-3; for example, SenseLearner was second overall in the English-all-words task (Snyder and Palmer, 2004).”
GERBIL - Benchmarking Named Entity Recognition and Linking consistently (2017, methodology): “Babelfy has been evaluated using six datasets: three from earlier SemEval tasks [48,43,42], one from a Senseval task [55] and two already used for evaluating AIDA [26,25].”
Meaningful Clustering of Senses Helps Boost Word Sense Disambiguation Performance (2006, methodology): “As a ﬁrst experiment, we assessed the effect of the automatic sense clustering on the English all-words task at Senseval-3 (Snyder and Palmer, 2004).”
Entity Linking meets Word Sense Disambiguation: a Unified Approach (2014, methodology): “• The Senseval-3 dataset for English all-words WSD (Snyder and Palmer, 2004), which contains 899 nouns to be disambiguated using WordNet.”
SenseBERT: Driving Some Sense into BERT (2019, methodology): “We use SemCor [Miller et al., 1993] as our training dataset ( 226 , 036 annotated examples), and the SenseEval [Edmonds and Cotton, 2001, Snyder and Palmer, 2004] / SemEval [Pradhan et al., 2007, Navigli et al., 2013, Moro and Navigli, 2015] suite for evaluation (overall 7253 annotated examples),…”