SemEval-2007 Task 10: English Lexical Substitution Task

By Diana McCarthy, Roberto Navigli. 2007

285 citations recorded by the source corpus.

In this paper we describe the English Lexical Substitution task for SemEval. In the task, annotators and systems find an alternative substitute word or phrase for a target word in context. The task involves both finding the synonyms and disambiguating the context. Participating systems are free to use any lexical resource. There is a subtask which requires identifying cases where the word is functioning as part of a multiword in the sentence and detecting what that multiword is.

Subfields: Artificial Intelligence, Natural Language Processing, Evaluation & Benchmarks

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Citations in the corpus

context2vec: Learning Generic Context Embedding with Bidirectional LSTM (2016, methodology): “The first is the dataset introduced in the lexical substitution task of SemEval 2007 (McCarthy and Navigli, 2007), denoted LST-07, split into 300 dev sentences and 1,710 test sentences.”
A Structured Vector Space Model for Word Meaning in Context (2008, methodology): “For this experiment, we use the SemEval-1 lexical substitution (lexsub) dataset (McCarthy and Navigli, 2007), which contains 10 instances each of 200 target words in sentential contexts, drawn from Sharoff’s (2006) English Internet Corpus.”
Word sense disambiguation: A survey (2009, background): “—word sense induction and discrimination [Agirre and Soroa 2007], for a comparative assessment of unsupervised systems among themselves and with supervised and knowledge-based systems; —a lexical substitution task [McCarthy and Navigli 2007], aiming at the objective evaluation of both supervised and”
Learning to Rank Lexical Substitutions (2013, methodology): “The task to generate lexical substitutions in context (McCarthy and Navigli, 2007), i.”
Modeling Word Meaning in Context with Substitute Vectors (2015, methodology): “The dataset introduced in the lexical substitution task of SemEval 2007 (McCarthy and Navigli, 2007), denoted here LS07, is the most widely used for the evaluation of lexical substitution.”
Composition in Distributional Models of Semantics (2010, methodology): “and Pado (2008) apply this method to a word sense disambiguation task which involves finding synonyms which may be substituted for a given word in context (McCarthy and Navigli, 2007).”
A Simple Word Embedding Model for Lexical Substitution (2015, methodology): “Lexical substitution tasks have become very popular for evaluating context-sensitive lexical inference models since the introduction of the original task in SemEval-2007 (McCarthy and Navigli, 2007) and additional later variants (Biemann, 2013; Kremer et”
SenticNet 5: Discovering Conceptual Primitives for Sentiment Analysis by Means of Context Embeddings (2018, methodology): “As lexical substitution datasets, we used the LST-07 dataset from the SemEval 2007 lexical substitution task (McCarthy and Navigli 2007) and the 15-thousand target word all-words LST-14 dataset from SemEval 2014 (Kremer et al.”
A Challenge Set for Advancing Language Modeling (2012, result): “The past work which is most similar to ours is derived from the lexical substitution track of SemEval2007 (McCarthy and Navigli, 2007).”
Detecting Asymmetric Semantic Relations in Context: A Case-Study on Hypernymy Detection (2017, background): “In-context lexical semantic tasks Besides entailment, other lexical semantic tasks studied in context include lexical substitution (McCarthy and Navigli, 2007) and cross-lingual lexical substitution (Mihalcea et al.”
Computational Approaches to Sentence Completion (2012, result): “The past work which is most similar to ours is derived from the lexical substitution track of SemEval-2007 (McCarthy and Navigli, 2007).”
Word Sense Clustering and Clusterability (2016, methodology): “For this reason, we represent instances through manually annotated translations (Mihalcea, Sinha, and McCarthy 2010) and paraphrases (McCarthy and Navigli 2007).”
FASTSUBS: An Efficient and Exact Procedure for Finding the Most Likely Lexical Substitutes Based on an N-Gram Language Model (2012, methodology): “L EXICAL substitutes have proven useful in applications such as paraphrasing [1], text simplification [2], and machine translation [3].”
Learning to Represent Words in Context with Multilingual Supervision (2015, methodology): “The task was introduced in SemEval-2007 (McCarthy & Navigli, 2007) involves both finding the synonyms and disambiguating the context.”