Spicy Adjectives and Nominal Donkeys: Capturing Semantic Deviance Using Compositionality in Distributional Spaces

Vecchi, Eva Maria; Marelli, Marco; Zamparelli, Roberto; Baroni, Marco

doi:10.1111/cogs.12330

Cited by 38 publications

(35 citation statements)

References 55 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A particularly exciting application of compositional distributional methods is that of Vecchi et al (2017), who showed that distributional models are able to distinguish between semantically acceptable and unacceptable adjective-noun phrases. Crucially, their data involves phrases that are unattested in a very large corpus; some phrases are unattested because they are semantically anomalous (angry lamp, legislative onion), and some due to the generative capacity of language, with its explosion of combinatory properties, together with the properties of the world, which make some combinations of adjectives and nouns unlikely even if they are perfectly acceptable (warm garlic, sophisticated senator ).…”

Section: Single Representation Polysemy Via Compositionmentioning

confidence: 99%

Distributional Semantics and Linguistic Theory

Boleda

2020

Annu. Rev. Linguist.

171

100

View full text Add to dashboard Cite

Distributional semantics provides multi-dimensional, graded, empirically induced word representations that successfully capture many aspects of meaning in natural languages, as shown in a large body of work in computational linguistics; yet, its impact in theoretical linguistics has so far been limited. This review provides a critical discussion of the literature on distributional semantics, with an emphasis on methods and results that are of relevance for theoretical linguistics, in three areas: semantic change, polysemy and composition, and the grammar-semantics interface (specifically, the interface of semantics with syntax and with derivational morphology). The review aims at fostering greater crossfertilization of theoretical and computational approaches to language, as a means to advance our collective knowledge of how it works.

show abstract

Section: Single Representation Polysemy Via Compositionmentioning

confidence: 99%

Distributional Semantics and Linguistic Theory

Boleda

2020

Annu. Rev. Linguist.

171

100

View full text Add to dashboard Cite

show abstract

“…[ [103][104][105][106] Words with many semantic or associative neighbors are less likely to be remembered in a free recall task and cued recall tasks, trigger lower feelings of knowing, and are more likely to be accepted in new word combinations. [23,[107][108][109] Words with high phonological clustering are more difficult to identify in spoken word recognition and lexical decision tasks whereas high associative clustering are remembered better in a cued recall task.…”

Section: Centralitymentioning

confidence: 99%

New Perspectives on the Aging Lexicon

Wulff

Deyne

Jones

et al. 2019

Trends in Cognitive Sciences

117

126

View full text Add to dashboard Cite

The field of cognitive aging has seen considerable advances in describing the linguistic and semantic changes that happen during the adult life span to uncover the structure of the mental lexicon (i.e., the mental repository of lexical and conceptual representations). Nevertheless, there is still debate concerning the sources of these changes, including the role of environmental exposure and several cognitive mechanisms associated with learning, representation, and retrieval of information. We review the current status of research in this field and outline a framework that promises to assess the contribution of both ecological and psychological aspects to the aging lexicon. Cognitive Aging and the Mental Lexicon There is consensus in the cognitive sciences that human development extends well beyond childhood and adolescence, and there has been remarkable empirical progress in the field of cognitive aging in past decades [1]. Nevertheless, the role of environmental and cognitive factors in age-related changes in the structure and processing of lexical and semantic representations (see Glossary) is still under debate. For example, age-related memory decline is commonly attributed to a decline in cognitive abilities [2,3], yet some researchers have proposed that massive exposure to language over the course of one's life leads to knowledge gains that may contribute to, if not fully account for, age-related memory deficits [4-6]. We argue that to resolve such debates we require an interdisciplinary approach that captures how information exposure across adulthood may change the way that we acquire, represent, and recall information. We summarize recent developments in the field (Figure 1, Table 1) and propose a conceptual framework (Figure 2, Key Figure) and associated research agenda that argues for combining ecological analyses, formal modeling, and large-scale empirical studies to shed light on the contents, structure, and neural basis of the aging mental lexicon in both health and disease. Mental Lexicon: Aging and Cognitive Performance The mental lexicon can be thought of as a repository of lexical and conceptual representations, composed of organized networks of semantic, phonological, orthographic, morphological, and other types of information [7]. The cognitive sciences have provided considerable knowledge about the computational (Box 1; [8-11]) and neural basis (Box 2; [12,13]) of lexical and semantic cognition, and there has been considerable interest in how such aspects of cognition change across adulthood and aging [14,15]. Past work on the aging lexicon emphasized the amount of information acquired across the life span (e.g., vocabulary gains across adulthood; [15]); however, new evaluations using graphbased approaches suggest that both quantity and structural aspects of representations differ between individuals [16] and change across the life span [17-19]. Such insights were gathered, for example, from a large-scale analysis of free association data from thousands of individuals [17], ranging from 10 to ...

show abstract

“…Research on cognitive science also benefits greatly from DSMs (Jones & Mewhort, 2007; Jones, Willits, & Dennis, 2015). Word vectors have been demonstrated to explain a number of cognitive phenomena relevant to semantic memory or the mental lexicon, such as word association (Jones, Gruenenfelder, & Recchia, 2018; Utsumi, 2015), semantic priming (Mandera, Keuleers, & Brysbaert, 2017), semantic transparency (Marelli & Baroni, 2015), and conceptual combination (Vecchi, Marelli, Zamparelli, & Baroni, 2017). In cognitive research on concepts, in particular on embodied versus symbolic processing, DSM is regarded as a de facto standard language model (Bolognesi & Steen, 2018; de Vega, Glenberg, & Graesser, 2008).…”

Section: Introductionmentioning

confidence: 99%

Exploring What Is Encoded in Distributional Word Vectors: A Neurobiologically Motivated Analysis

Utsumi¹

2020

Cognitive Science

View full text Add to dashboard Cite

The pervasive use of distributional semantic models or word embeddings for both cognitive modeling and practical application is because of their remarkable ability to represent the meanings of words. However, relatively little effort has been made to explore what types of information are encoded in distributional word vectors. Knowing the internal knowledge embedded in word vectors is important for cognitive modeling using distributional semantic models. Therefore, in this paper, we attempt to identify the knowledge encoded in word vectors by conducting a computational experiment using Binder et al.'s (2016) featural conceptual representations based on neurobiologically motivated attributes. In an experiment, these conceptual vectors are predicted from text‐based word vectors using a neural network and linear transformation, and prediction performance is compared among various types of information. The analysis demonstrates that abstract information is generally predicted more accurately by word vectors than perceptual and spatiotemporal information, and specifically, the prediction accuracy of cognitive and social information is higher. Emotional information is also found to be successfully predicted for abstract words. These results indicate that language can be a major source of knowledge about abstract attributes, and they support the recent view that emphasizes the importance of language for abstract concepts. Furthermore, we show that word vectors can capture some types of perceptual and spatiotemporal information about concrete concepts and some relevant word categories. This suggests that language statistics can encode more perceptual knowledge than often expected.

show abstract

Spicy Adjectives and Nominal Donkeys: Capturing Semantic Deviance Using Compositionality in Distributional Spaces

Cited by 38 publications

References 55 publications

Distributional Semantics and Linguistic Theory

Distributional Semantics and Linguistic Theory

New Perspectives on the Aging Lexicon

Exploring What Is Encoded in Distributional Word Vectors: A Neurobiologically Motivated Analysis

Contact Info

Product

Resources

About