Vector Space Models of Lexical Meaning

By Stephen Clark. 2015

233 citations recorded by the source corpus.

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

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Citations in the corpus

Compositionality Decomposed: How do Neural Networks Generalise? (2019, methodology): “Baroni & Zamparelli, 2010; Coecke, Sadrzadeh, & Clark, 2010).(1) A more recent trend in word composition exploits deep learning, a class of machine learning techniques that model language in a completely data-driven fashion, by defining a loss on a downstream task (such as sentiment analysis, langua”
Climbing towards NLU: On Meaning, Form, and Understanding in the Age of Data (2020, background): ““But aren’t neural representations meaning too?” The internal representations of a neural network have been found to capture certain aspects of meaning, such as semantic similarity (Mikolov et al., 2013; Clark, 2015).”
Don’t count, predict! A systematic comparison of context-counting vs. context-predicting semantic vectors (2014, methodology): “…track of the contexts (e.g., co-occurring words) in which target terms appear in a large corpus as proxies for meaning representations, and apply geometric techniques to these vectors to measure the similarity in meaning of the corresponding words (Clark, 2013; Erk, 2012; Turney and Pantel, 2010).”
Multimodal Distributional Semantics (2013, methodology): “General overviews of semantic space models are provided by Clark (2013), Erk (2012), Manning and Schütze (1999), Sahlgren (2006) and Turney and Pantel (2010).”
Emerging Language Spaces Learned From Massively Multilingual Corpora (2018, background): “, [6,7]) and applied to a number of practical tasks.”
Combining Language and Vision with a Multimodal Skip-gram Model (2015, background): “DSMs have been very effectively applied to a variety of semantic tasks (Clark, 2015; Mikolov et al., 2013b; Turney and Pantel, 2010).”
Improving zero-shot learning by mitigating the hubness problem (2014, background): “…linguistics and neural language modeling has shown that contextual co-occurrence patterns of words in corpora can be effectively exploited to learn high-quality vector-based representations of their meaning in an unsupervised manner (Collobert et al., 2011; Clark, 2015; Turney & Pantel, 2010).”
Measuring Compositionality in Representation Learning (2019, background): “A long line of work in natural language processing (Coecke et al., 2010; Baroni & Zamparelli, 2010; Clark, 2012; Fyshe et al., 2015) focuses on learning composition functions to produce distributed representations of phrases and sentences—that is, for purposes of modeling rather than evaluation.”
An Exploration of Discourse-Based Sentence Spaces for Compositional Distributional Semantics (2015, methodology): “Following the procedure outlined in Polajnar and Clark (2014), we employed t-test weighting (Curran, 2004) and context selection, and reduced our noun vectors (n) to K = 100 dimensions using Singular Value Decomposition (SVD).”
Compositional-ly Derived Representations of Morphologically Complex Words in Distributional Semantics (2013, background): “Recent surveys include Clark (2012), Erk (2012) and Turney and Pantel (2010).”
Building a shared world: mapping distributional to model-theoretic semantic spaces (2015, background): “concepts of formal semantics in distributional terms (Coecke et al., 2011; Bernardi et al., 2013; Grefenstette, 2013; Hermann et al., 2013; Baroni et al., 2014a; Clarke, 2012); b) the hybrid approaches, which try to keep the set-theoretic apparatus for function words and integrate distributions as c”
Associative Multichannel Autoencoder for Multimodal Word Representation (2018, background): “In recent years we have seen a surge of interest in building computational models that represent word meanings from patterns of word co-occurrence in corpora (Turney and Pantel, 2010; Mikolov et al., 2013; Penning-ton et al., 2014; Clark, 2015; Wang et al., 2018b).”
High-risk learning: acquiring new word vectors from tiny data (2017, background): “Distributional models (DS: Turney and Pantel (2010); Clark (2012); Erk (2012)), and in particular neural network approaches (Bengio et al., 2003; Collobert et al., 2011; Huang et al., 2012; Mikolov et al., 2013), do not fare well in the absence of large corpora.”
Distributional Models of Word Meaning (2018, methodology): “For other types of weighting functions used in DS, see Curran (2003), Evert (2008), Turney & Pantel (2010), and Kiela & Clark (2014). The distributional similarity between two lexemes u and v is measured with the similarity between their distributional vectors u and v.”
Learning Visually Grounded Sentence Representations (2017, background): “Purely text-based semantic models, which represent word meaning as a distribution over other words (Harris, 1954; Turney and Pantel, 2010; Clark, 2015), suﬀer from the grounding problem (Harnad, 1990).”
Formal Distributional Semantics: Introduction to the Special Issue (2016, background): “Recently, researchers have started exploring other modalities, using, for instance, visual and auditory information extracted from images and sound ﬁles (Feng and Lapata 2010; Bruni et al. 2012; Roller and Schulte Im Walde 2013; Kiela and Clark 2015; Lopopolo and van Miltenburg 2015).”
“Look, some Green Circles!”: Learning to Quantify from Images (2016, background): “…the development of both a) a computational theory of meaning rooted in situated language use, as pursued by the ﬁeld of Distributional Semantics (Clark, 2012; Erk, 2012) and b) vision-based applications such as image caption generation and visual question answering (Antol et al., 2015), going…”