Grounded Models of Semantic Representation

By Carina Silberer, Mirella Lapata. 2012

100 citations recorded by the source corpus.

Subfields: Natural Language Processing, Representation & Self-Supervised Learning

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Citations in the corpus

Grounded Language Learning in a Simulated 3D World (2017, background): “For instance, it was shown that learnt concept representation spaces more faithfully reﬂect human semantic intuitions if induced from information about the perceptible properties of objects as well as from raw text (Silberer and Lapata, 2012).”
Multimodal Distributional Semantics (2013, background): “Several interesting recent experiments use features produced by human subjects in concept description tasks (so-called " semantic norms " ) as a surrogate of true perceptual features (Andrews et al., 2009; Johns & Jones, 2012; Silberer & Lapata, 2012; Steyvers, 2010).”
See No Evil, Say No Evil: Description Generation from Densely Labeled Images (2014, methodology): “Labels gathered in this way are a type of feature norms (McRae et al., 2005), which have been used in the cognitive science literature to approximate human perception and were recently used as a visual proxy in distributional semantics (Silberer and Lapata, 2012).”
Visually Grounded Neural Syntax Acquisition (2019, unknown): “…visual and textual representations. word-level concreteness estimation based on text (Turney et al., 2011; Hill et al., 2013), human judgments (Silberer and Lapata, 2012; Hill and Ko-rhonen, 2014a; Brysbaert et al., 2014), and multi-modal data (Hill and Korhonen, 2014b; Hill et al., 2014;…”
Semantic Speech Retrieval With a Visually Grounded Model of Untranscribed Speech (2017, background): “Semantics are difficult to annotate, so most studies evaluate models using soft human ratings on tasks such as word similarity (Feng and Lapata, 2010; Silberer and Lapata, 2012; Bruni et al., 2014), word association (Bruni et al.”
Is this a wampimuk? Cross-modal mapping between distributional semantics and the visual world (2014, background): “The resulting multimodal space has been shown to provide a good approximation to human concept similarity judgments (Silberer and Lapata, 2012).”
Multimodal Machine Learning: A Survey and Taxonomy (2017, methodology): “Starting from work by Feng and Lapata [62], grounding is usually performed by finding a common latent space between the representations [62], [190] (in case of parallel datasets) or by learning unimodal representations separately and then concatenating them to lead to a multimodal one [30], [105], [”
Associative Multichannel Autoencoder for Multimodal Word Representation (2018, background): “A class of models extends Latent Dirichlet Allocation (Blei et al., 2003) to jointly learn topic distributions from words and perceptual units (Andrews et al., 2009; Silberer and Lapata, 2012; Roller and Schulte im Walde, 2013).”
Learning Grounded Meaning Representations with Autoencoders (2014, background): “…from a corpus of multimodal documents (i.e., BBC news articles and their associated images), whereas others (Steyvers, 2010; Andrews et al., 2009; Silberer and Lapata, 2012) use feature norms obtained in longitudinal elicitation studies (see McRae et al. (2005) for an example) as an…”
Quantifying the Visual Concreteness of Words and Topics in Multimodal Datasets (2018, background): “, 2013) or by incorporating human judgments of perception into models (Silberer and Lapata, 2012; Hill and Korhonen, 2014a).”
Commonly Uncommon: Semantic Sparsity in Situation Recognition (2016, background): “Attribute-based learning [24, 25, 12], cross-modal transfer [39, 28, 15, 26] and using text priors [32, 18] have all been proposed but they study classification or other simplified settings.”
Evaluation by Association: A Systematic Study of Quantitative Word Association Evaluation (2017, methodology): “While parts of the USF data set have been used for evaluation in NLP before (Michelbacher et al., 2007; Silberer and Lapata, 2012; Kiela et al., 2014; Hill and Korhonen, 2014, inter alia), we conduct the ﬁrst full study regarding the evaluation on the quantitative WA task.”
The Emergence of Semantics in Neural Network Representations of Visual Information (2018, background): “Indeed, joint models of semantics based on images and text have been developed, and word vectors have been grounded in the visual space (Silberer and Lapata, 2012, 2014; Bruni and Baroni, 2013).”
Situation Recognition: Visual Semantic Role Labeling for Image Understanding (2016, background): “Recent work has also explored other areas of cross pollination, including video recognition [20], cross modal mappings [42, 29, 16], coreference [8, 28], and affordances [52].”
Learning Image Embeddings using Convolutional Neural Networks for Improved Multi-Modal Semantics (2014, methodology): “property norming experiments (Silberer and Lapata, 2012; Roller and Schulte im Walde, 2013) or from processing and extracting features from images (Feng and Lapata, 2010; Leong and Mihalcea, 2011; Bruni et al.”