Literal and Metaphorical Sense Identification through Concrete and Abstract Context

By Peter D. Turney, Yair Neuman, Dany H. Assaf, Yohai Cohen. Conference on Empirical Methods in Natural Language Processing, 2011

339 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/cb0d80684acc65c6a6b7d291cb5a0e70b9317899

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Citations in the corpus

Visually Grounded Compound PCFGs (2020, background): “Learning involves estimating concreteness of spans, which generalizes word-level concreteness (Turney et al., 2011; Kiela et al., 2014).”
Quantifying the Visual Concreteness of Words and Topics in Multimodal Datasets (2018, background): “Concreteness in datasets has been previously studied in either text-only cases (Turney et al., 2011; Hill et al., 2013) or by incorporating human judgments of perception into models (Silberer and Lapata, 2012; Hill and Korhonen, 2014a).”
Equal But Not The Same: Understanding the Implicit Relationship Between Persuasive Images and Text (2018, methodology): “Resources and methods exist to predict concreteness and specificity [7, 22, 37].”
Combining Language and Vision with a Multimodal Skip-gram Model (2015, methodology): “…is a Wikipedia 2009 dump comprising approximately 800M tokens.1 To train the multimodal models, we add visual information for 5,100 words that have an entry in ImageNet (Deng et al., 2009), occur at least 500 times in the corpus and have concreteness score ≥ 0.5 according to Turney et al. (2011).”
Learning Visually-Grounded Semantics from Contrastive Adversarial Samples (2018, unknown): “In addition, since images usually reﬂect concrete concepts better than abstract ones, we compute the concreteness of words following Turney et al. (2011), and only consider those heads with concreteness larger than θ = 0 .”
Distributional Semantics Beyond Words: Supervised Learning of Analogy and Paraphrase (2013, methodology): “Turney et al. (2011) estimated PPMI ( x i , x j , left ) by sampling the corpus for phrases containing x i and then looking for x j to the left of x i in the sampled phrases (and likewise for right ).”
Visually Grounded Neural Syntax Acquisition (2019, unknown): “The concreteness given by Turney et al. (2011) and Brysbaert et al. (2014) highly correlate with each other.”