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This work aims to study the construction of migrant categories and immigration discourse on Swedish-speaking Facebook pages in the last decade. It combines the insights from computational linguistics and distributional semantics approach with those from discursive psychology to explore a corpus of more than 1 M Facebook posts. This allows one to compare the meanings of labels denoting various categories of migrants and identify the key interpretative repertoires used to discuss the immigration topic. The study finds that the 'immigrant' category has stronger association with potential costs, benefits and threat to the host society, while the 'refugee' category is presented as in need of support and solidarity. Nevertheless, objectification and exclusionary rhetoric are used in relation to both categories, although in different ways, while the immigration issue is often interpreted as a matter of Sweden's national concern rather than as a part of people's actual experiences and life paths. [ABSTRACT FROM AUTHOR]

Machines have achieved a broad and growing set of linguistic competencies, thanks to recent progress in Natural Language Processing (NLP). Psychologists have shown increasing interest in such models, comparing their output to psychological judgments such as similarity, association, priming, and comprehension, raising the question of whether the models could serve as psychological theories. In this article, we compare how humans and machines represent the meaning of words. We argue that contemporary NLP systems are fairly successful models of human word similarity, but they fall short in many other respects. Current models are too strongly linked to the text-based patterns in large corpora, and too weakly linked to the desires, goals, and beliefs that people express through words. Word meanings must also be grounded in perception and action and be capable of flexible combinations in ways that current systems are not. We discuss promising approaches to grounding NLP systems and argue that they will be more successful, with a more human-like, conceptual basis for word meaning. [ABSTRACT FROM AUTHOR]

We contrast two views of rivalry in word formation. Under the classical, categorical view, two processes are rivals if they are semantically equivalent. Under the more nuanced, gradient view, two processes can be rivals at different degrees, depending on how frequently they are amenable to be deployed as alternatives to one another. We propose to use methods from distributional semantics to explore the usefulness of both views. Building on data from French, we first show that distributional differences between average difference vectors capture semantic similarity across derivational processes in a manner comparable to the expectations of expert morphologists. We then propose an operational implementation of the classical view of rivalry based on computational classifiers: processes are rivals if and only if a classifier is unable to discriminate between them. Experimentation with French data shows that this operationalization correctly captures the broad brushes of rivalry, but also reveals finer, gradient aspects of competition in the spirit of gradient rivalry. [ABSTRACT FROM AUTHOR]

The human body is perhaps the most ubiquitous and salient visual stimulus that we encounter in our daily lives. Given the prevalence of images of human bodies in natural scene statistics, it is no surprise that our mental representations of the body are thought to strongly originate from visual experience. Yet, little is still known about high-level cognitive representations of the body. Here, we retrieved a body map from natural language, taking this as a window into high-level cognitive processes. We first extracted a matrix of distances between body parts from natural language data and employed this matrix to extrapolate a body map. To test the effectiveness of this high-level body map, we then conducted a series of experiments in which participants were asked to classify the distance between pairs of body parts, presented either as words or images. We found that the high-level body map was systematically activated when participants were making these distance judgments. Crucially, the linguistic map explained participants’ performance over and above the visual body map, indicating that the former cannot be simply conceived as a by-product of perceptual experience. These findings, therefore, establish the existence of a behaviorally relevant, high-level representation of the human body.

What does it take to learn a new word? Many of the words welearn, we have learned from language itself – by encounteringthem in various informative contexts. Here, we investigate thelimits of learning from context by studying how people learnnew words from very sparse contexts, at the extreme, a contextin which all content words are replaced by nonsense words. Wefind that participants exposed to even such extremely sparsecontexts nevertheless learn something about the meaning ofwords embedded in those contexts. Performance tended to bebetter when knowledge was assessed by first directing people’sattention to the part of speech of the target words.

Our knowledge of words consists of a lexico-semanticnetwork in which different words and their meaningsare connected by relations, such as similarity inmeaning. This research investigated the integration ofnew words into lexico-semantic networks.Specifically, we investigated whether new words canrapidly become linked with familiar words givenexposure to distributional regularities that areubiquitous in real-world language input, in whichfamiliar and new words either: (1) directly co-occur insentences, or (2) never co-occur, but instead shareeach other’s patterns of co-occurrence with anotherword. We observed that, immediately after sentencereading, familiar words came to be primed not only bynew words with which they co-occurred in sentences,but also by new words with which they shared co-occurrence. This finding represents a noveldemonstration that new words can be rapidlyintegrated into lexico-semantic networks fromexposure to distributional regularities.

There is ongoing discussion about how to conceptualize the nature of the distinction between inflection and derivation. A common approach relies on qualitative differences in the semantic relationship between inflectionally versus derivationally related words: inflection yields ways to discuss the same concept in different syntactic contexts, while derivation gives rise to words for related concepts. This differential can be expected to manifest in the predictability of word frequency between words that are related derivationally or inflectionally: predicting the token frequency of a word based on information about its base form or about related words should be easier when the two words are in an inflectional relationship, rather than a derivational one. We compare prediction error magnitude for statistical models of token frequency based on distributional and frequency information of inflectionally or derivationally related words in French. The results conform to expectations: it is easier to predict the frequency of a word from properties of an inflectionally related word than from those of a derivationally related word. Prediction error provides a quantitative, continuous method to explore differences between individual processes and differences yielded by employing different predicting information, which in turn can be used to draw conclusions about the nature and manifestation of the inflection–derivation distinction.

This study uses word embeddings to investigate the semantic changes underlying the creation of two adversative connectives in Portuguese, porém and mas 'but, however'. For porém, we chart its development from an original PP formed by a preposition with a causal meaning (por) and a demonstrative pronoun that referred anaphorically to a previous proposition (en(de)). For mas, we trace its change from an adverb meaning 'more'. Adopting a distributional semantics approach, we use word embedding models trained on two corpora, the CIPM (Corpus Informatizado do Português Medieval, containing texts from the 12th–16th centuries) and COLONIA (containing texts from the 16th–20th centuries). We produce a measure of change based on the similarity scores of porém and mas with respect to words in relevant semantic categories in each corpus, representing the source and the target meanings. This paper, which constitutes the first computational study of semantic change in Portuguese, also discusses challenges and outlines steps to be taken into consideration when choosing embedding algorithms for small historical corpora. [ABSTRACT FROM AUTHOR]

This paper provides a comparative analysis of cross-lingual word embedding by studying the impact of different variables on the quality of the embedding models within the distributional semantics framework. Distributional semantics is a method for the semantic representation of words, phrases, sentences, and documents. This method aims at capturing as much information as possible from the contextual information in a vector space. The early study in this domain focused on monolingual word embedding. Further progress used cross-lingual data to capture the contextual semantic information across different languages. The main contribution of this research is to make a comparative study to find out the superior impact of the learning methods, supervised and unsupervised in training and post-training approaches in different embedding algorithms, to capture semantic properties of the words in cross-lingual embedding models to be applicable in tasks that deal with multi-languages, such as question retrieval. To this end, we study the cross-lingual embedding models created by BilBOWA, VecMap, and MUSE embedding algorithms along with the variables that impact the embedding models' quality, namely the size of the training data and the window size of the local context. In our study, we use the unsupervised monolingual Word2Vec embedding model as the baseline and evaluate the quality of embeddings on three data sets: Google analogy, mono- and cross-lingual words similar lists. We further investigated the impact of the embedding models in the question retrieval task. [ABSTRACT FROM AUTHOR]

Ambiguity in word meanings is a long-standing challenge in processing natural language. Word sense disambiguation (WSD) deals with this challenge. Prior neural language models make use of recurrent neural network and architecture with long short-term memory. These models process the words in sequence, are slower and not truly bi-directional, so they are not able to capture and represent the contextual meanings of the words, hence they are not competent in contextual semantic representation for WSD. Recent, Bi-Directional Encoder Representation from Transformers (BERT) is long short-term memory-based transformer model that is deeply bi-directional. It uses attention mechanisms, which process and use the relevance of the entire context at a time in both directions, so it is well suited to leverage the meanings in distributed representation for WSD. We have used BERT for obtaining contextual word embedding of context and sense gloss of Marathi language ambiguous word. For this purpose, we have used 282 moderately ambiguous Marathi words catering to 1004 senses distributed over 5282 Marathi sentences harvested by linguists from online Marathi websites. We have calculated semantic similarity between the pair of context and gloss embedding using Minkowski distance family and cosine similarity measures and assigned plausible sense to the given Marathi ambiguous word. Our empirical evaluation shows that the cosine similarity measure outperforms and yields an average disambiguation accuracy of 75.26% for the given Marathi sentence. [ABSTRACT FROM AUTHOR]

The distributional pattern of words in language forms the basis of linguistic distributional knowledge and contributes to conceptual processing, yet many questions remain regarding its role in cognition. We propose that corpus-based linguistic distributional models can represent a cognitively plausible approach to understanding linguistic distributional knowledge when assumed to represent an essential component of semantics, when trained on corpora representative of human language experience, and when they capture the diverse distributional relations that are useful to cognition. Using an extensive set of cognitive tasks that vary in the complexity of conceptual processing required, we systematically evaluate a wide range of model families, corpora, and parameters, and demonstrate that there is no one-size-fits-all approach for how linguistic distributional knowledge is used across cognition. Rather, linguistic distributional knowledge is a rich source of information about the world that can be accessed flexibly according to the conceptual complexity of the task at hand. [ABSTRACT FROM AUTHOR]

Distributional semantics has deeply changed in the last decades. First, predict models stole the thunder from traditional count ones, and more recently both of them were replaced in many NLP applications by contextualized vectors produced by neural language models. Although an extensive body of research has been devoted to Distributional Semantic Model (DSM) evaluation, we still lack a thorough comparison with respect to tested models, semantic tasks, and benchmark datasets. Moreover, previous work has mostly focused on task-driven evaluation, instead of exploring the differences between the way models represent the lexical semantic space. In this paper, we perform a large-scale evaluation of type distributional vectors, either produced by static DSMs or obtained by averaging the contextualized vectors generated by BERT. First of all, we investigate the performance of embeddings in several semantic tasks, carrying out an in-depth statistical analysis to identify the major factors influencing the behavior of DSMs. The results show that (i) the alleged superiority of predict based models is more apparent than real, and surely not ubiquitous and (ii) static DSMs surpass BERT representations in most out-of-context semantic tasks and datasets. Furthermore, we borrow from cognitive neuroscience the methodology of Representational Similarity Analysis (RSA) to inspect the semantic spaces generated by distributional models. RSA reveals important differences related to the frequency and part-of-speech of lexical items. [ABSTRACT FROM AUTHOR]

There is ongoing discussion about how to conceptualize the nature of the distinction between inflection and derivation. A common approach relies on qualitative differences in the semantic relationship between inflectionally versus derivationally related words: inflection yields ways to discuss the same concept in different syntactic contexts, while derivation gives rise to words for related concepts. This differential can be expected to manifest in the predictability of word frequency between words that are related derivationally or inflectionally: predicting the token frequency of a word based on information about its base form or about related words should be easier when the two words are in an inflectional relationship, rather than a derivational one. We compare prediction error magnitude for statistical models of token frequency based on distributional and frequency information of inflectionally or derivationally related words in French. The results conform to expectations: it is easier to predict the frequency of a word from properties of an inflectionally related word than from those of a derivationally related word. Prediction error provides a quantitative, continuous method to explore differences between individual processes and differences yielded by employing different predicting information, which in turn can be used to draw conclusions about the nature and manifestation of the inflection–derivation distinction. [ABSTRACT FROM AUTHOR]

Copyright of Revista Signos is the property of Pontificia Universidad Catolica de Valparaiso and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This article offers an exploratory quantitative analysis of the conceptual career of climate in US English over the period 1800–2010. Our aim is to qualify two, closely related arguments circulating in Environmental Humanities scholarship regarding the concept's history, namely that we only started to think of climate as a global entity aft er the introduction of general circulation models during the final quarter of the twentieth century, and, second, that climatic change only became an issue of environmental concern once scientists began to approach climate as a global model. While we do not dispute that the computer revolution resulted in a significantly new understanding of climate, our analysis points to a longer process of singularization and growing abstraction starting in the early nineteenth century that might help to nuance and deepen insights developed in environmental history. [ABSTRACT FROM AUTHOR]

The emergence of key concepts in Reinhart Koselleck's sense has been much discussed in conceptual history, but mainly for the eighteenth and nineteenth centuries. The article documents a post–World War II emergence of the concept of health, from relative anonymity to becoming a key concept, comparable to concepts such as politics, democracy, and culture. While previous research has emphasized conceptual mobility, this article focuses on conceptual aggregation, where the concept of health assembles and assimilates meanings, becoming essential to discourse. This is explained with reference to the development of the welfare state and the political use of a positive, expanded health concept. The article utilizes a collocation analysis of Norwegian digitized newspapers 1950–2010, culled from the uniquely extensive database of the Norwegian National Library. [ABSTRACT FROM AUTHOR]

Lexical semantic change detection has been a rapidly developing field of science in recent years. Existed algorithms of lexical semantic change detection face difficulties when they are used to work with words denoting named entities. This paper proposes a method that allows one to reveal a word in a large corpus that started being used as a named entity, as well as to date the first usage of this word as a proper name. To solve this problem, firstly, we offer an algorithm that allows for detecting words in a large corpus denoting named entities. The recognizer is based on an analysis of co-occurrences with the most frequent words and was trained on data from the English subcorpus of the Google Books Ngram corpus. The achieved recognition accuracy of named entities is 98.44% on the test sample. Secondly, we test the possibility of applying the trained recognizer to diachronic data. The analysed cases show that the recognizer initially trained using the total bigram frequencies for a long time interval, at least for any frequent word, provides stable results for the annual frequency values. This can make the recognizer a good tool for language evolution studies, especially for detecting new meanings of words. The analysed cases show that the proposed method allows revealing new word meanings associated with named entities, as well as detecting genericized meaning of words that were earlier used as proper names.

Previous research in computational linguistics dedicated a lot of effort to using language modeling and/or distributional semantic models to predict metrics extracted from eye-tracking data. However, it is not clear whether the two components have a distinct contribution, with recent studies claiming that surprisal scores estimated with large-scale, deep learning-based language models subsume the semantic relatedness component. In our study, we propose a regression experiment for estimating different eye-tracking metrics on two English corpora, contrasting the quality of the predictions with and without the surprisal and the relatedness components. Different types of relatedness scores derived from both static and contextual models have also been tested. Our results suggest that both components play a role in the prediction, with semantic relatedness surprisingly contributing also to the prediction of function words. Moreover, they show that when the metric is computed with the contextual embeddings of the BERT model, it is able to explain a higher amount of variance.

Do all languages convey semantic knowledge in the same way?If language simply mirrors the structure of the world, theanswer should be a qualified “yes”. If, however, languagesimpose structure as much as reflecting it, then even ostensiblythe “same” word in different languages may mean quitedifferent things. We provide a first pass at a large-scalequantification of cross-linguistic semantic alignment ofapproximately 1000 meanings in 55 languages. We find thatthe translation equivalents in some domains (e.g., Time,Quantity, and Kinship) exhibit high alignment acrosslanguages while the structure of other domains (e.g., Politics,Food, Emotions, and Animals) exhibits substantial cross-linguistic variability. Our measure of semantic alignmentcorrelates with known phylogenetic distances betweenlanguages: more phylogenetically distant languages have lesssemantic alignment. We also find semantic alignment tocorrelate with cultural distances between societies speakingthe languages, suggesting a rich co-adaptation of language andculture even in domains of experience that appear mostconstrained by the natural world.

Theories of grounded cognition assume that conceptual representations are grounded in sensorimotor experience. However, abstract concepts such as jealousy or childhood have no directly associated referents with which such sensorimotor experience can be made; therefore, the grounding of abstract concepts has long been a topic of debate. Here, we propose (a) that systematic relations exist between semantic representations learned from language on the one hand and perceptual experience on the other hand, (b) that these relations can be learned in a bottom-up fashion, and (c) that it is possible to extrapolate from this learning experience to predict expected perceptual representations for words even where direct experience is missing. To test this, we implement a data-driven computational model that is trained to map language-based representations (obtained from text corpora, representing language experience) onto vision-based representations (obtained from an image database, representing perceptual experience), and apply its mapping function onto language-based representations for abstract and concrete words outside the training set. In three experiments, we present participants with these words, accompanied by two images: the image predicted by the model and a random control image. Results show that participants' judgements were in line with model predictions even for the most abstract words. This preference was stronger for more concrete items and decreased for the more abstract ones. Taken together, our findings have substantial implications in support of the grounding of abstract words, suggesting that we can tap into our previous experience to create possible visual representation we don't have. [ABSTRACT FROM AUTHOR]

The vocabulary for describing odors in English natural language is not well understood, as prior studies of odor descriptions have often relied on preselected descriptors and odor ratings. Here, we present a data‐driven approach that automatically identifies English odor descriptors based on their degree of olfactory association, and derive their semantic organization from their distributions in natural texts, using a distributional‐semantic language model. We identify 243 descriptors that are much more strongly associated with olfaction than English words in general. We then derive the semantic organization of these olfactory descriptors, and find that it is captured by four clusters that we name Offensive, Malodorous, Fragrant, and Edible. The semantic space derived from our model primarily differentiates descriptors in terms of pleasantness and edibility along which our four clusters are positioned, and is similar to a space derived from perceptual data. The semantic organization of odor vocabulary can thus be mapped using natural language data (e.g., online text), without the limitations of odor‐perceptual data and preselected descriptors. Our method may thus facilitate research on olfaction, a sensory system known to often elude verbal description. [ABSTRACT FROM AUTHOR]

Modelling semantic similarity plays a fundamental role in lexical semantic applications. A natural way of calculating semantic similarity is to access handcrafted semantic networks, but similarity prediction can also be anticipated in a distributional vector space. Similarity calculation continues to be a challenging task, even with the latest breakthroughs in deep neural language models. We first examined popular methodologies in measuring taxonomic similarity, including edge-counting that solely employs semantic relations in a taxonomy, as well as the complex methods that estimate concept specificity. We further extrapolated three weighting factors in modelling taxonomic similarity. To study the distinct mechanisms between taxonomic and distributional similarity measures, we ran head-to-head comparisons of each measure with human similarity judgements from the perspectives of word frequency, polysemy degree and similarity intensity. Our findings suggest that without fine-tuning the uniform distance, taxonomic similarity measures can depend on the shortest path length as a prime factor to predict semantic similarity; in contrast to distributional semantics, edge-counting is free from sense distribution bias in use and can measure word similarity both literally and metaphorically; the synergy of retrofitting neural embeddings with concept relations in similarity prediction may indicate a new trend to leverage knowledge bases on transfer learning. It appears that a large gap still exists on computing semantic similarity among different ranges of word frequency, polysemous degree and similarity intensity. [ABSTRACT FROM AUTHOR]

The present study is part of a larger research project developing computational tools for large-scale corpus-based semantic analyses. One such tool represents semantic structure with vector space models (VSMs). The paper shows that this tool and the models built require a deeper understanding, especially with a view to how its results relate to cognitive theories of meaning. Although token-based VSMs are increasingly used in corpus-based cognitive semantics, we believe it is insufficiently appreciated how alternative parameter settings deal with a range of semantic issues, such as granularity of meaning, prototypicality of the domain of application and interaction with syntactic patterns. For the purpose of this paper, we will focus on only one of those issues, viz. the prototypicality of the domain of application, presenting the results of three of our case studies on the Dutch adjectives heilzaam, hoekig, hachelijk and geldig. The models presented are built from a 520MW corpus of contemporary Dutch and Flemish newspapers and by varying parameters such as window size, part-of-speech and frequency thresholds in the selection of features. The resulting VSMs are evaluated through visual analytics: although multidimensional, they can be reduced to 2D and represented in scatterplots where more similar tokens appear closer to each other. The color-coding with manual sense tags employed here makes it possible to compare the groupings provided by human annotators with those of the computational models in a way consistent with the cognitive approach to meaning and categorization. [ABSTRACT FROM AUTHOR]

In structuralist linguistics, compounds are argued not to constitute morphological categories, due to the absence of systematic form-meaning correspondences. This study investigates subsets of compounds for which systematic form-meaning correspondences are present: adjective–noun compounds in Mandarin. We show that there are substantial differences in the productivity of these compounds. One set of productivity measures (the count of types, the count of hapax legomena, and the estimated count of unseen types) reflect compounds' profitability. By contrast, the category-conditioned degree of productivity is found to correlate with the internal semantic transparency of the words belonging to a morphological category. Greater semantic transparency, gauged by distributional semantics, predicts greater category-conditioned productivity. This dovetails well with the hypothesis that semantic transparency is a prerequisite for a word formation process to be productive. [ABSTRACT FROM AUTHOR]

Indonesian has two prefixes, PE- and PEN-, that are similar in form and meaning, but are probably not allomorphs. In this study, we applied a distributional vector space model to clarify whether these prefixes have discriminable semantics. Comparisons of pairs of words within and across morphologically defined sets of words revealed that cosine similarities of pairs consisting of a word with PE- and a word with PEN- were reduced compared to pairs of only PE- words, or of only PEN- words. Furthermore, nouns with PE- were more similar to their base words than was the case for words with PEN-. The specialized use of PE- for words denoting agents, and the specialized use of PEN- for denoting instruments, was also visible in the semantic vector space. These differences in the semantics of PE- and PEN- thus provide further quantitative support for the independent status of PE- as opposed to PEN-. [ABSTRACT FROM AUTHOR]

This study investigates the phenomenon of defectiveness in Russian case and number noun paradigms from the perspective of distributional semantics. We made use of word embeddings, high-dimensional vectors trained from large text corpora, and compared the observed paradigms of nouns that are defective in the genitive plural, as suggested by Zaliznjak (1977), with the observed paradigms for non-defective nouns. When the embeddings of about 20,000 inflected forms were projected onto a two-dimensional space, clusters of case and number within case were found, suggesting global semantic similarity for words with the same inflectional features. Moreover, defective lexemes were characterized by lower semantic transparency, in that inflected forms of the same lexeme are semantically less similar to each other, and their meanings are also more idiosyncratic. Furthermore, compared to non-defective lexemes, inflected forms from defective lexemes are further away from the idealized average case-number meanings, obtained by averaging over the vectors of all inflected forms of the same case-number combination. As a consequence, the semantics of defective forms are predicted less precisely by a simple model of conceptualization that assumes that the meaning of a given Russian inflected form is approximated well by the sum of pertinent embeddings of the lexeme, case, and number within case. We conclude that the relationship between defectiveness and semantics, at least the kind captured by word embeddings, is stronger than has been anticipated previously. [ABSTRACT FROM AUTHOR]

Distributional semantics offers new ways to study the semantics of morphology. This study focuses on the semantics of noun singulars and their plural inflectional variants in English. Our goal is to compare two models for the conceptualization of plurality. One model (FRACSS) proposes that all singular-plural pairs should be taken into account when predicting plural semantics from singular semantics. The other model (CCA) argues that conceptualization for plurality depends primarily on the semantic class of the base word. We compare the two models on the basis of how well the speech signal of plural tokens in a large corpus of spoken American English aligns with the semantic vectors predicted by the two models. Two measures are employed: the performance of a form-to-meaning mapping and the correlations between form distances and meaning distances. Results converge on a superior alignment for CCA. Our results suggest that usage-based approaches to pluralization in which a given word's own semantic neighborhood is given priority outperform theories according to which pluralization is conceptualized as a process building on high-level abstraction. We see that what has often been conceived of as a highly abstract concept, [+plural], is better captured via a family of mid-level partial generalizations. [ABSTRACT FROM AUTHOR]

We used word embeddings to study the relation between productivity and semantic transparency. We compiled a dataset with around 2700 two-syllable compounds that shared position-specific constituents (henceforth pivots) and some 1100 suffixed words. For each pivot and suffix, we calculated measures of productivity as well as measures of semantic transparency. For compounds, productivity (P) was negatively correlated with the number of types (V) and with the semantic similarity between non-pivot constituents and their compounds. Conversely, the greater semantic similarity of the pivot with either the compound or the non-pivot constituent predicted higher degrees of productivity. Visualization with t-SNE revealed clustering of suffixed words' embeddings, but no by-pivot clustering for compounds, except for a minority of pivots whose regions in semantic space did not contain intruding unrelated compounds. A subset of these pivots was found to realize a fixed shift in semantic space from the base word to the corresponding compound, a property that also emerged for several suffixes. For these pivots, no correlation between P and V was present. Thus, Mandarin compounds appear to realize, at one extreme, motivated but unsystematic concept formation (where other pivots could just as well have been used), and at the other extreme, systematic suffix-like semantics. [ABSTRACT FROM AUTHOR]

Contextual diversity modifies word frequency by ignoring the repetition of words in context (Adelman, Brown, & Quesada, 2006, Psychological Science, 17(9), 814–823). Semantic diversity modifies contextual diversity by taking into account the uniqueness of the contexts that a word occurs in when calculating lexical strength (Jones, Johns, & Recchia, 2012, Canadian Journal of Experimental Psychology, 66, 115–124). Recent research has demonstrated that measures based on contextual and semantic diversity provide a considerable improvement over word frequency when accounting for lexical organization data (Johns, 2021, Psychological Review, 128, 525–557; Johns, Dye, & Jones, 2020a, Quarterly Journal of Experimental Psychology, 73, 841–855). The article demonstrates that these same findings generalize to word-level episodic recognition rates, using the previously released data of Cortese, Khanna, and Hacker (Cortese et al., 2010, Memory, 18, 595–609) and Cortese, McCarty, and Schock (Cortese et al., 2015, Quarterly Journal of Experimental Psychology, 68, 1489–1501). It was found that including the best fitting contextual diversity model allowed for a very large increase in variance accounted for over previously used variables, such as word frequency, signalling commonality with results from the lexical organization literature. The findings of this article suggest that current trends in the collection of megadata sets of human behavior (e.g., Balota et al., 2007, Behavior Research Methods, 39(3), 445–459) provide a promising avenue to develop new theoretically oriented models of word-level episodic recognition data. [ABSTRACT FROM AUTHOR]

The linguistic input children receive across early childhood plays a crucial role in shaping their knowledge about the world. To study this input, researchers have begun applying distributional semantic models to large corpora of child‐directed speech, extracting various patterns of word use/co‐occurrence. Previous work using these models has not measured how these patterns may change throughout development, however. In this work, we leverage natural language processing methods—originally developed to study historical language change—to compare caregivers' use of words when talking to younger versus older children. Some words' usage changed more than others; this variability could be predicted based on the word's properties at both the individual and category levels. These findings suggest that caregivers' changing patterns of word use may play a role in scaffolding children's acquisition of conceptual structure in early development. [ABSTRACT FROM AUTHOR]

Copyright of Suvremena Lingvistika is the property of Suvremena Lingvistika and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Corpus-based models of lexical strength have called into question the role of word frequency as an organizing principle of the lexicon, revealing that contextual and semantic diversity measures provide a closer fit to lexical behavior data (Adelman et al., 2006; Jones et al., 2012). Contextual diversity measures modify word frequency by ignoring word repetition in context, while semantic diversity measures consider the semantic consistency of contextual word occurrence. Recent research has shown that a better account of lexical organization data is provided by socially based measures of semantic diversity, which encode the communication patterns of individuals across discourses (Johns, 2021b). While most research on contextual diversity has focused on single words, recent corpus-based and experimental evidence suggests that an integral part of language use involves recurrent and more structurally complex units, such as multiword phrases and idioms. The aim of the present work was to determine if contextual and semantic diversity drive lexical organization at the level of multiword units (here, operationalized as idiomatic expressions), in addition to single words. To this end, we analyzed normative ratings of familiarity for 210 English idioms (Libben & Titone, 2008) using a set of contextual, semantic, and socially based diversity measures that were computed from a 55-billion word corpus of Reddit comments. The results confirm the superiority of diversity measures over frequency for multiword expressions, suggesting that multiword units, such as idiomatic phrases, show similar lexical organization dynamics as single words. [ABSTRACT FROM AUTHOR]

Montagliani and Hockley (2019) presented evidence that item-method directed forgetting not only leads to worse recognition of forget-cued targets than remember-cued targets but also better rejection of foils associated with forget-cued targets than remember-cued targets. Based on that result, they proposed that participants elaboratively encode more category-level information about R-cued targets. We present a retrieval-based explanation of the result within an instance-based memory model. The model imports word representations from two distributional semantic models, latent semantic analysis (LSA) and random permutation model (RPM), into an instance-based model of memory, MINERVA 2. The model reproduced Montagliani and Hockley's results without requiring assumptions about elaborated encoding of category-level information at study. The simulations demonstrate that whereas Montagliani and Hockley's findings are consistent with an account grounded in elaborated encoding of words at study, the results do not force that conclusion. Instead, better encoding of remember-cued targets at study establishes the conditions for retrieval-time effects at test to produce a corresponding influence on false recognition for category-related foils. Our model can be used as a formal tool to think about and study the incidental consequences of item directed forgetting in recognition memory. [ABSTRACT FROM AUTHOR]

Distributional models of semantics assume that the meaningof a given word is a function of the contexts in which itoccurs. In line with this, prior research suggests that a word’ssemantic representation can be manipulated – pushed towarda target meaning, for example – by situating that word indistributional contexts frequented by the target. Left open toquestion is the role that order plays in the distributionalconstruction of meaning. Learning occurs in time, and it canproduce asymmetric outcomes depending on the order inwhich information is presented. Discriminative learningmodels predict that systematically manipulating a word’spreceding context should more strongly influence its meaningthan should varying what follows. We find support for thishypothesis in three experiments in which we manipulatedsubjects’ contextual experience with novel and marginallyfamiliar words, while varying the locus of manipulation.

Vectors derived from patterns of co-occurrence of words inlarge bodies of text have often been used as representations ofsome aspects of the meanings of different words. Generally,the distance between such vectors is used as a measure of thesemantic similarity between the word meanings theyrepresent. One important way of evaluating the performanceof these vectors has been to use them to answer vocabularymultiple choice questions (MCQs) where the participant isasked to judge which of several choice words is closest inmeaning to a stem word. The existing vocabulary MCQ testsused in this way have been very useful but there are somepractical problems in their use as general evaluationmeasures. Here, we discuss why such tests remain usefulevaluation measures, introduce a new vocabulary test,evaluate several current sets of semantic vectors using thenew test and compare their performance to human data.

Child-directed speech (CDS) is a talking style adopted bycaregivers when they talk to toddlers (Snow, 1995). Weconsider the role of distributional semantic features of CDSin language acquisition. We view semantic structure as amanifold on which words lie. We compare the semanticstructure of verbs in CDS to the semantic structure of childspeech (CS) and adult-directed speech (ADS) by measuringhow easy it is to align the manifolds. We find that it is easierto align verbs in CS to CDS than to align CS to ADS,suggesting that the semantic structure of CDS is reflected inchild productions. We also find, by measuring verbs vertexdegrees in a semantic graph, that a mixed initialized set ofverbs with high degrees and medium degrees has the bestperformance among all alignments, suggesting that bothsemantic generality and diversity may be important fordeveloping semantic representations.

Accounts of abstract word learning suggest that learning thesewords relies primarily on access to linguistic cues, such as thestatistical co-occurrence of words with similar semanticproperties. Thus, children with language impairment (LI),who by definition have impoverished access to linguisticcontext, should have disproportionate impairments in abstractword knowledge. Here, we compared verbal definitions andlexical decisions to both abstract and concrete words ofchildren with LI (ages 8 to 13) and both age-matched andvocabulary-matched typically developing (TD) peers. Relativeto age-matched peers, children with LI had significant deficitsin both tasks. Crucially, however, there was not greaterimpairment of abstract words. We conclude that that linguisticknowledge is not a sine qua non to learning abstract wordsand concepts and other mechanisms, which are notspecifically impaired in LI, are at play.

The organization of semantic memory, including memory for word meanings, has long been a central question in cognitive science. Although there is general agreement that word meaning representations must make contact with sensory-motor and affective experiences in a non-arbitrary fashion, the nature of this relationship remains controversial. One prominent view proposes that word meanings are represented directly in terms of their experiential content (i.e., sensory-motor and affective representations). Opponents of this view argue that the representation of word meanings reflects primarily taxonomic structure, that is, their relationships to natural categories. In addition, the recent success of language models based on word co-occurrence (i.e., distributional) information in emulating human linguistic behavior has led to proposals that this kind of information may play an important role in the representation of lexical concepts. We used a semantic priming paradigm designed for representational similarity analysis (RSA) to quantitatively assess how well each of these theories explains the representational similarity pattern for a large set of words. Crucially, we used partial correlation RSA to account for intercorrelations between model predictions, which allowed us to assess, for the first time, the unique effect of each model. Semantic priming was driven primarily by experiential similarity between prime and target, with no evidence of an independent effect of distributional or taxonomic similarity. Furthermore, only the experiential models accounted for unique variance in priming after partialling out explicit similarity ratings. These results support experiential accounts of semantic representation and indicate that, despite their good performance at some linguistic tasks, the distributional models evaluated here do not encode the same kind of information used by the human semantic system. [Display omitted] • We used RSA to evaluate three major theories of word meaning representation. • Automatic semantic priming was measured item-wise with high reliability. • Results strongly support representation in terms of experiential information. • Distributional information did not independently contribute to semantic priming. • RSA and semantic priming can be used to determine the featural content of concepts. [ABSTRACT FROM AUTHOR]