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Corpus-based models of lexical strength, such as contextual and semantic diversity, challenge traditional word frequency measures as the main organising principle of the lexicon. Diversity models, which capitalise on language usage, consistently outperform word frequency in predicting lexical behaviour. However, most evidence for this theoretical position comes from “shallow” tasks, like lexical decision or naming, with long stimulus presentation times. We conducted exploratory secondary analyses using data from Antal & de Almeida (2024) to investigate the time-course of language use on lexical-semantic access in a semantically “deep” task. We modeled behavioural data from a masked picture-word congruency task with “brief” (60 ms) and “long” (200 ms) presentation durations with contextual and semantic diversity measures from a 55-billion-word corpus from Reddit. Results suggest that lexical and conceptual access are driven by a shared mechanism operating based on word usage context, advancing our understanding of the organisation of conceptual knowledge in semantic memory. [ABSTRACT FROM AUTHOR]

Different experiential traces (i.e., linguistic, motor, and perceptual) are likely contributing to the organization of human semantic knowledge. Here, we aimed to address this issue by investigating whether visual experience may affect the sensitivity to distributional priors from natural language. We conducted an independent reanalysis of data from Bottini et al., in which early blind and sighted participants performed an auditory lexical decision task. Since previous research has shown that semantic neighborhood density—the mean distance between a target word and its closest semantic neighbors—can influence performance in lexical decision tasks, we investigated whether vision may alter the reliance on this semantic index. We demonstrate that early blind participants are more sensitive to semantic neighborhood density than sighted participants, as indicated by the significantly faster response times for words with higher levels of semantic neighborhood density shown by the blind group. These findings suggest that an early lack of visual experience may lead to enhanced sensitivity to the distributional history of words in natural language, deepening in turn our understanding of the strict interplay between linguistic and perceptual experience in the organization of conceptual knowledge. [ABSTRACT FROM AUTHOR]

Following Ariel (2021. Why it's hard to construct ad hoc number concepts. In Caterina Mauri, Ilaria Fiorentini, & Eugenio Goria (eds.), Building categories in interaction: Linguistic resources at work, 439–462. Amsterdam: John Benjamins), we argue that number words manifest distinct distributional patterns from open-class lexical items. When modified, open-class words typically take selectors (as in kinda table), which select a subset of their potential denotations (e.g., "nonprototypical table"). They are typically not modified by loosening operators (e.g., approximately), since even if bare, typical lexemes can broaden their interpretation (e.g., table referring to a rock used as a table). Number words, on the other hand, have a single, precise meaning and denotation and cannot take a selector, which would need to select a subset of their (single) denotation (??kinda seven). However, they are often overtly broadened (approximately seven), creating a range of values around N. First, we extend Ariel's empirical examination to the larger COCA and to Hebrew (HeTenTen). Second, we propose that open-class and number words belong to sparse versus dense lexical domains, respectively, because the former exhibit prototypicality effects, but the latter do not. Third, we further support the contrast between sparse and dense domains by reference to: synchronic word2vec models of sparse and dense lexemes, which testify to their differential distributions, numeral use in noncounting communities, and different renewal rates for the two lexical types. [ABSTRACT FROM AUTHOR]

The degree of semantic equivalence of translation pairs is typically measured by asking bilinguals to rate the semantic similarity of them or comparing the number and meaning of dictionary entries. Such measures are subjective, labor-intensive, and unable to capture the fine-grained variation in the degree of semantic equivalence. Thompson et al. (in Nature Human Behaviour, 4(10), 1029–1038, 2020) propose a computational method to quantify the extent to which translation equivalents are semantically aligned by measuring the contextual use across languages. Here, we refine this method to quantify semantic alignment of English–Chinese translation equivalents using word2vec based on the proposal that the degree of similarity between the contexts associated with a word and those of its multiple translations vary continuously. We validate our measure using semantic alignment from GloVe and fastText, and data from two behavioral datasets. The consistency of semantic alignment induced across different models confirms the robustness of our method. We demonstrate that semantic alignment not only reflects human semantic similarity judgment of translation equivalents but also captures bilinguals’ usage frequency of translations. We also show that our method is more cognitively plausible than Thompson et al.’s method. Furthermore, the correlations between semantic alignment and key psycholinguistic factors mirror those between human-rated semantic similarity and these variables, indicating that computed semantic alignment reflects the degree of semantic overlap of translation equivalents in the bilingual mental lexicon. We further provide the largest English–Chinese translation equivalent dataset to date, encompassing 50,088 translation pairs for 15,734 English words, their dominant Chinese translation equivalents, and their semantic alignment Rc values. [ABSTRACT FROM AUTHOR]

Theories of word processing propose that readers are sensitive to statistical co-occurrences between spelling and meaning. Orthographic–semantic consistency (OSC) measures provide a continuous estimate of the statistical regularities between spelling and meaning. Here we examined Malay, an Austronesian language that is agglutinative. In Malay, stems are often repeated in other words that share a related meaning (e.g., sunyi / quiet; ke-sunyi-an / silence; makan / eat; makan - an / foods). The first goal was to expand an existing large Malay database by computing OSC estimates for 2,287 monomorphemic words. The second goal was to explore the impact of root family size and OSC on lexical decision latencies for monomorphemic words. Decision latencies were collected for 1,280 Malay words of various morphological structures. Of these, data from 1,000 monomorphemic words were analysed in a series of generalised additive mixed models (GAMMs). Root family size and OSC were significant predictors of decision latencies, particularly for lower frequency words. We found a facilitative effect of root family size and OSC. Furthermore, we observed an interaction between root family size and OSC in that an effect of OSC was only apparent in words with larger root families. This interaction has not yet been explored in English but has the potential to be a new benchmark effect to test distributional models of word processing. [ABSTRACT FROM AUTHOR]

Are distributional learning mechanisms capable of complex linguistic inferences requiring compositional generalisation? This question has become contentious with the development of large language models, which mimic human language abilities in many ways, but which struggle with compositional generalisation. We investigated a set of qualitatively different distributional models (word co-occurrence models, graphical models, recurrent neural networks, and Transformers), by training them on a carefully controlled artificial language containing combinatorial dependencies involving multiple words, and then testing them on novel sequences containing distributionally overlapping combinatorial dependencies. In this work, we show that graphical network models and Transformers, but not co-occurrence space models and recurrent neural networks, were able to perform compositional generalisation. This work demonstrates that the kinds of distributional models that can perform compositional generalisation are those that can represent words both individually and as a part of the phrases in which they participate. [ABSTRACT FROM AUTHOR]

While research using behavioural methods has repeatedly shown that generic masculines in German come with a male bias, computational methods only entered this area of research very recently. The present paper shows that some assumptions made by these recent computational studies – treating genericity as an inflectional function and computing a vector for generic usage strongly correlated with the grammatical masculine – are not without issue, and offers the use of semantic instance vectors as a possible solution to these issues. Beyond this methodological improvement, the present paper finds that generic masculines are indeed semantically more similar to specific masculines than to specific feminines – results that are in line with findings by the recent computational studies and the majority of previous behavioural studies.

Survey studies show that popular support for democracy is strong in democratic and non-democratic countries. Naturally, the question is if democracy actually means the same thing in different linguistic, cultural, and political contexts. Mass media is often mentioned as decisive in forming citizens' understandings of democracy, but the media discourse is rarely in focus in comparative studies on popular conceptions of democracy. This article contributes to the debate by analysing data collected from online media in 93 countries. By utilizing tools from natural language processing, we provide new insights based on methods that are both extensive, flexible and cost-efficient. Our analysis shows that the media discourse revolves around democracy as governance, as outcomes and as values, but that these abstract understandings have additional dimensions. Our main contributions are three: (i) we show that the media discourse is related to popular understandings of democracy; (ii) our results indicate that there are common denominators of how the D-word is discussed in media across the globe, but when analysing the dimensions in more detail, common denominators are few and (iii) by relating democracy to everyday politics, media seems to legitimize any regime as democratic rather than being a beacon for liberal democracy. [ABSTRACT FROM AUTHOR]

This paper presents a large and systematic assessment of distributional models for Galician. To this end, we have first trained and evaluated static word embeddings (e.g., word2vec, GloVe), and then compared their performance with that of current contextualised representations generated by neural language models. First, we have compiled and processed a large corpus for Galician, and created four datasets for word analogies and concept categorisation based on standard resources for other languages. Using the aforementioned corpus, we have trained 760 static vector space models which vary in their input representations (e.g., adjacency-based versus dependency-based approaches), learning algorithms, size of the surrounding contexts, and in the number of vector dimensions. These models have been evaluated both intrinsically, using the newly created datasets, and on extrinsic tasks, namely on POS-tagging, dependency parsing, and named entity recognition. The results provide new insights into the performance of different vector models in Galician, and about the impact of several training parameters on each task. In general, fastText embeddings are the static representations with the best performance in the intrinsic evaluations and in named entity recognition, while syntax-based embeddings achieve the highest results in POS-tagging and dependency parsing, indicating that there is no significant correlation between the performance in the intrinsic and extrinsic tasks. Finally, we have compared the performance of static vector representations with that of BERT-based word embeddings, whose fine-tuning obtains the best performance on named entity recognition. This comparison provides a comprehensive state-of-the-art of current models in Galician, and releases new transformer-based models for NER. All the resources used in this research are freely available to the community, and the best models have been incorporated into SemantiGal, an online tool to explore vector representations for Galician. [ABSTRACT FROM AUTHOR]

The wider availability of large-scale datasets and reproducible algorithms has boosted the application of NLP to living languages. On the other hand, dead languages benefit from the availability of curated resources both to offset the sparseness of available data and to make data accessible to researchers. We present here AGVaLex, a computational valency lexicon automatically extracted from the Ancient Greek Dependency Treebank. It contains quantitative corpus-driven morphological, syntactic and lexical information about verbs and their direct and indirect arguments and has a wide range of applications for the study of Ancient Greek. To illustrate these applications, we offer a case study that compares the semantic flexibility of transitive verb formulae in archaic Greek epic to a non-formulaic corpus, with the goal of detecting unique patterns of variation. We also illustrate the possibilities afforded by AGVaLex to scholars with a less extensive background in computational corpus-based research. [ABSTRACT FROM AUTHOR]

Using distributional semantics, we show that English nominal pluralization exhibits semantic clusters. For instance, the change in semantic space from singulars to plurals differs depending on whether a word denotes, e.g., a fruit, or an animal. Languages with extensive noun classes such as Swahili and Kiowa distinguish between these kind of words in their morphology. In English, even though not marked morphologically, plural semantics actually also varies by semantic class. A semantically informed method, CosClassAvg, is introduced that is compared to two other methods, one implementing a fixed shift from singular to plural, and one creating plural vectors from singular vectors using a linear mapping (FRACSS). Compared to FRACSS, CosClassAvg predicted plural vectors that were more similar to the corpus-extracted plural vectors in terms of vector length, but somewhat less similar in terms of orientation. Both FRACSS and CosClassAvg outperform the method using a fixed shift vector to create plural vectors, which does not do justice to the intricacies of English plural semantics. A computational modeling study revealed that the observed difference between the plural semantics generated by these three methods carries over to how well a computational model of the listener can understand previously unencountered plural forms. Among all methods, CosClassAvg provides a good balance for the trade-off between productivity (being able to understand novel plural forms) and faithfulness to corpus-extracted plural vectors (i.e., understanding the particulars of the meaning of a given plural form). [ABSTRACT FROM AUTHOR]

In morphology, a distinction is commonly drawn between inflection and derivation. However, a precise definition of this distinction which reflects the way it manifests across languages remains elusive within linguistic theory, typically being based on subjective tests. In this study, we present 4 quantitative measures which use the statistics of a raw text corpus in a language to estimate to what extent a given morphological construction changes the form and distribution of lexemes. In particular, we measure both the average and the variance of this change across lexemes. Crucially, distributional information captures syntactic and semantic properties and can be operationalised by word embeddings. Based on a sample of 26 languages, we find that we can reconstruct 89±1% of the classification of constructions into inflection and derivation in UniMorph using our 4 measures, providing large-scale cross-linguistic evidence that the concepts of inflection and derivation are associated with measurable signatures in terms of form and distribution that behave consistently across a variety of languages. We also use our measures to identify in a quantitative way whether categories of inflection which have been considered noncanonical in the linguistic literature, such as inherent inflection or transpositions, appear so in terms of properties of their form and distribution. We find that while combining multiple measures reduces the amount of overlap between inflectional and derivational constructions, there are still many constructions near the model's decision boundary between the two categories. This indicates a gradient, rather than categorical, distinction. [ABSTRACT FROM AUTHOR]

While the two types of intransitive verbs, i.e. unergative and unaccusative, are hypothesised to be syntactically represented, many have proposed a semantic account where abstract properties related to agentivity and telicity, often conceptualised as binary properties, determine the classification. Here we explore the extent to which graded, embodied features rooted in neurobiological systems contribute to the distinction, representing verb meanings as continuous human ratings over various experiential dimensions. Unlike prior studies that classified verbs based on categorical intuition, we assessed the degree of unaccusativity by acceptability of the prenominal past participle construction, one of the unaccusativity diagnostics. Five models were constructed to explain these data: categorical syntactic/semantic, feature-based event-semantic, experiential, and distributional models. The experiential model best explained the diagnostic test data, suggesting that the unaccusative/unergative distinction may be an emergent phenomenon related to differences in underlying experiential content. The experiential model's advantages, including interpretability and scalability, are also discussed. [ABSTRACT FROM AUTHOR]

Valence is a dominant semantic dimension, and it is fundamentally linked to basic approach-avoidance behavior within a broad range of contexts. Previous studies have shown that it is possible to approximate the valence of existing words based on several surface-level and semantic components of the stimuli. Parallelly, recent studies have shown that even completely novel and (apparently) meaningless stimuli, like pseudowords, can be informative of meaning based on the information that they carry at the subword level. Here, we aimed to further extend this evidence by investigating whether humans can reliably assign valence to pseudowords and, additionally, to identify the factors explaining such valence judgments. In Experiment 1, we trained several models to predict valence judgments for existing words from their combined form and meaning information. Then, in Experiment 2 and Experiment 3, we extended the results by predicting participants' valence judgments for pseudowords, using a set of models indexing different (possible) sources of valence and selected the best performing model in a completely data-driven procedure. Results showed that the model including basic surface-level (i.e., letters composing the pseudoword) and orthographic neighbors information performed best, thus tracing back pseudoword valence to these components. These findings support perspectives on the nonarbitrariness of language and provide insights regarding how humans process the valence of novel stimuli. [ABSTRACT FROM AUTHOR]

This paper presents a cross-language study of lexical semantics within the framework of distributional semantics. We used a wide range of predefined semantic categories in Mandarin and English and compared the clusterings of these categories using FastText word embeddings. Three techniques of dimensionality reduction were applied to mapping 300-dimensional FastText vectors into two-dimensional planes: multidimensional scaling, principal components analysis, and t-distributed stochastic neighbor embedding. The results show that t-SNE provides the clearest clustering of semantic categories, improving markedly on PCA and MDS. In both languages, we observed similar differentiation between verbs, adjectives, and nouns as well as between concrete and abstract words. In addition, the methods applied in this study, especially Procrustes analysis, make it possible to trace subtle differences in the structure of the semantic lexicons of Mandarin and English.

Besides explicit inference of word meanings, associating words with diverse contexts may be a key mechanism underlying vocabulary learning through reading. Drawing from distributional semantic theory, we developed a text modification method called reflash to facilitate both word-context association and explicit inference. Using a set of left and right arrows, learners can jump to a target word’s previous or subsequent occurrences in digital books to synthesize clues across contexts. Participants read stories with target words modified by reflash-only, gloss-only, gloss + reflash, or unmodified. Learning outcomes were measured via Vocabulary Knowledge Scale and a researcher-developed interview to probe word-context association. We modeled the learning trajectories of words across five weeks among three adolescent L2 English learners (113 word-learner pairings) using Bayesian multilevel models. We found that reflash-only words made more gains than words in other conditions on both outcomes, controlling for key covariates such as types of existing knowledge. Our analysis also revealed that context synthesis may be particularly useful for learning specific types of words like homonyms, which has significant pedagogical implications.

Word affective ratings are important tools in psycholinguistic research, natural language processing, and many other fields. However, even for well-studied languages, such norms are usually limited in scale. To extrapolate affective (i.e., valence and arousal) values for words in the SUBTLEX-CH database (Cai & Brysbaert, 2010, PLoS ONE, 5(6):e10729), we implemented a computational neural network which captured how words' vector-based semantic representations corresponded to the probability densities of their valence and arousal. Based on these probability density functions, we predicted not only a word's affective values, but also their respective degrees of variability that could characterize individual differences in human affective ratings. The resulting estimates of affective values largely converged with human ratings for both valence and arousal, and the estimated degrees of variability also captured important features of the variability in human ratings. We released the extrapolated affective values, together with their corresponding degrees of variability, for over 38,000 Chinese words in the Open Science Framework (https://osf.io/s9zmd/). We also discussed how the view of embodied cognition could be illuminated by this computational model. [ABSTRACT FROM AUTHOR]

Background: This study aims to facilitate the identification of similar devices for both, the European Medical Device Regulation (MDR) and the US 510(k) equivalence pathway by leveraging existing data. Both are related to the regulatory pathway of read across for chemicals, where toxicological data from a known substance is transferred to one under investigation, as they aim to streamline the accreditation process for new devices and chemicals. Research design and methods: This study employs latent semantic analysis to generate similarity values, harnessing the US Food and Drug Administration 510k-database, utilizing their 'Device Descriptions' and 'Intended Use' statements. Results: For the representative inhaler cluster, similarity values up to 0.999 were generated for devices within a 510(k)-predicate tree, whereas values up to 0.124 were gathered for devices outside this group. Conclusion: Traditionally, MDR equivalence involves manual review of many devices, which is laborious. However, our results suggest that the automated calculation of similarity coefficients streamlines this process, thus reducing regulatory effort, which can be beneficial for patients needing medical devices. Although this study is focused on the European perspective, it can find application within 510(k) equivalence regulation. The conceptual approach is reminiscent of chemical fingerprint similarity analysis employed in read-across. Plain Language Summary: This study addresses improvement of the registration process for medical devices by using automated methods to determine how similar they are to existing devices. Such a process is already used in chemistry for analysis of related substances. In the context of Medical Device Regulation (MDR), which sets standards for these devices, this process might be applicable in device equivalence evaluation. Traditionally, proving equivalence involves manually finding devices that are similar, but this is time-consuming, repetitive and labor-intensive. This study proposes a new approach, using advanced computer methods and a database from the US Food and Drug Administration (FDA) to automatically identify similar devices. This could make the process much quicker and more accurate and furthermore reduce bias. The study suggests that by applying these automated methods, the impact of recent regulatory changes could be reduced. This means that proving equivalence, a critical step to facilitate device accreditation, could be done more efficiently. The study shows potential for a significant transformation in compliance processes within the medical device industry, making them more streamlined and automated. [ABSTRACT FROM AUTHOR]

AI language models are increasingly transforming language research in various ways. How can language educators and researchers respond to the challenge posed by these AI models? Specifically, how can we embrace this technology to inform and enhance second language learning and teaching? In order to quantitatively characterize and index second language writing, the current work proposes the use of similarities derived from contextualized meaning representations in AI language models. The computational analysis in this work is hypothesis-driven. The current work predicts how similarities should be distributed in a second language learning setting. The results suggest that similarity metrics are informative of writing proficiency assessment and interlanguage development. Statistically significant effects were found across multiple AI models. Most of the metrics could distinguish language learners' proficiency levels. Significant correlations were also found between similarity metrics and learners' writing test scores provided by human experts in the domain. However, not all such effects were strong or interpretable. Several results could not be consistently explained under the proposed second language learning hypotheses. Overall, the current investigation indicates that with careful configuration and systematic metrics design, AI language models can be promising tools in advancing language education. [ABSTRACT FROM AUTHOR]

Do large language models like Chat-GPT or Claude meaningfully use the words they produce? Or are they merely clever prediction machines, simulating language use by producing statistically plausible text? There have already been some initial attempts to answer this question by showing that these models meet the criteria for entering meaningful states according to metasemantic theories of mental content. In this paper, I will argue for a different approach—that we should instead consider whether language models meet the criteria given by our best metasemantic theories of linguistic content. In that vein, I will illustrate how this can be done by applying two such theories to the case of language models: Gareth Evans’ (1982) account of naming practices and Ruth Millikan’s (1984, 2004, 2005) teleosemantics. In doing so, I will argue that it is a mistake to think that the failure of LLMs to meet plausible conditions for mental intentionality thereby renders their outputs meaningless, and that a distinguishing feature of linguistic intentionality—dependency on a pre-existing linguistic system—allows for the plausible result that LLM outputs are meaningful. [ABSTRACT FROM AUTHOR]

In morphology, a distinction is commonly drawn between inflection and derivation. However, a precise definition of this distinction which reflects the way it manifests across languages remains elusive within linguistic theory, typically being based on subjective tests. In this study, we present 4 quantitative measures which use the statistics of a raw text corpus in a language to estimate to what extent a given morphological construction changes the form and distribution of lexemes. In particular, we measure both the average and the variance of this change across lexemes. Crucially, distributional information captures syntactic and semantic properties and can be operationalised by word embeddings. Based on a sample of 26 languages, we find that we can reconstruct 89±1% of the classification of constructions into inflection and derivation in UniMorph using our 4 measures, providing large-scale cross-linguistic evidence that the concepts of inflection and derivation are associated with measurable signatures in terms of form and distribution that behave consistently across a variety of languages. We also use our measures to identify in a quantitative way whether categories of inflection which have been considered noncanonical in the linguistic literature, such as inherent inflection or transpositions, appear so in terms of properties of their form and distribution. We find that while combining multiple measures reduces the amount of overlap between inflectional and derivational constructions, there are still many constructions near the model’s decision boundary between the two categories. This indicates a gradient, rather than categorical, distinction.

We investigate the interface between concepts and word meanings by asking English speakers to list members of superordinate categories under one of three conditions: (1) when cued by a label (e.g. animals), (2) an exemplar list (e.g. dog, cat, mouse), or (3) a definition (e.g. "living creatures that roam the Earth"). We find that categories activated by labels lead to participants listing more category-typical responses, as quantified through typicality ratings, similarity in word embedding space, and accuracy in guessing category labels. This effect is stronger for some categories than others (e.g. stronger for appetizers than animals). These results support the view that a word is not merely a label for a concept, but rather a unique way of accessing and organizing conceptual space. [ABSTRACT FROM AUTHOR]

The formation of false memories is one of the most widely studied topics in cognitive psychology. The Deese–Roediger–McDermott (DRM) paradigm is a powerful tool for investigating false memories and revealing the cognitive mechanisms subserving their formation. In this task, participants first memorize a list of words (encoding phase) and next have to indicate whether words presented in a new list were part of the initially memorized one (recognition phase). By employing DRM lists optimized to investigate semantic effects, previous studies highlighted a crucial role of semantic processes in false memory generation, showing that new words semantically related to the studied ones tend to be more erroneously recognized (compared to new words less semantically related). Despite the strengths of the DRM task, this paradigm faces a major limitation in list construction due to its reliance on human-based association norms, posing both practical and theoretical concerns. To address these issues, we developed the False Memory Generator (FMG), an automated and data-driven tool for generating DRM lists, which exploits similarity relationships between items populating a vector space. Here, we present FMG and demonstrate the validity of the lists generated in successfully replicating well-known semantic effects on false memory production. FMG potentially has broad applications by allowing for testing false memory production in domains that go well beyond the current possibilities, as it can be in principle applied to any vector space encoding properties related to word referents (e.g., lexical, orthographic, phonological, sensory, affective, etc.) or other type of stimuli (e.g., images, sounds, etc.). [ABSTRACT FROM AUTHOR]

Although models of word meanings based on distributional semantics have proved effective in predicting human judgments of similarity among individual concepts, it is less clear whether or how such models might be extended to account for judgments of similarity among relations between concepts. Here we combine an individual-differences approach with computational modeling to predict human judgments of similarity among word pairs instantiating a variety of abstract semantic relations (e.g., contrast, cause-effect, part-whole). A measure of cognitive capacity predicted individual differences in the ability to discriminate among distinct relations. The human pattern of relational similarity judgments, both at the group level and for individual participants, was best predicted by a model that takes representations of word meanings based on distributional semantics as its inputs and uses them to learn an explicit representation of relations. These findings indicate that although the meanings of abstract semantic relations are not directly coded in the meanings of individual words, important aspects of relational similarity can be derived from distributional semantics. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Language is inherently multimodal. In spoken languages, combined spoken and visual signals (e.g., co‐speech gestures) are an integral part of linguistic structure and language representation. This requires an extension of the parallel architecture, which needs to include the visual signals concomitant to speech. We present the evidence for the multimodality of language. In addition, we propose that distributional semantics might provide a format for integrating speech and co‐speech gestures in a common semantic representation. [ABSTRACT FROM AUTHOR]

We present a word similarity gold standard for Akkadian, a language documented in ancient Mesopotamian sources from the 24th century BCE until the first century CE. The gold standard comprises 300 word pairs ranked by their paradigmatic similarity by five independently working Assyriologists. We use the gold standard to tune PMI + SVD and fastText models to improve their performance. We also present a hyper-parametrized PMI + SVD model for building count-based word embeddings, that aims to deal with the data sparsity and repetition issues encountered in Akkadian texts. Our model combines Dirichlet smoothing with context distribution smoothing, and uses context similarity weighting to down-sample distortion caused by formulaic litanies and partially or fully duplicated passages. [ABSTRACT FROM AUTHOR]

Word frequency is a strong predictor in most lexical processing tasks. Thus, any model of word recognition needs to account for howword frequency effects arise. The Discriminative Lexicon Model (DLM)models lexical processing withmappings between words' forms and their meanings. Comprehension and production are modeled via linear mappings between the two domains. So far, the mappings within the model can either be obtained incrementally via error-driven learning, a computationally expensive process able to capture frequency effects, or in an efficient, but frequency-agnostic solution modeling the theoretical endstate of learning (EL) where all words are learned optimally. In the present study we show how an efficient, yet frequency-informed mapping between form and meaning can be obtained (Frequency-informed learning; FIL). We find that FIL well approximates an incremental solution while being computationallymuch cheaper. FIL shows a relatively low type-and high token-accuracy, demonstrating that the model is able to process most word tokens encountered by speakers in daily life correctly. We use FIL to model reaction times in the Dutch Lexicon Project by means of a Gaussian Location Scale Model and find that FIL predicts well the S-shaped relationship between frequency and the mean of reaction times but underestimates the variance of reaction times for low frequency words. FIL is also better able to account for priming effects in an auditory lexical decision task in Mandarin Chinese, compared to EL. Finally, we used ordered data from CHILDES to compare mappings obtained with FIL and incremental learning. We show that themappings are highly correlated, but that with FIL some nuances based on word ordering effects are lost. Our results show how frequency effects in a learning model can be simulated efficiently, and raise questions about how to best account for low-frequency words in cognitive models. [ABSTRACT FROM AUTHOR]

We present a computational model capable of simulating aspects of human knowledge for thousands of real-world concepts. Our approach involves a pretrained transformer network that is further fine-tuned on large data sets of participant-generated feature norms. We show that such a model can successfully extrapolate from its training data, and predict human knowledge for new concepts and features. We apply our model to stimuli from 25 previous experiments in semantic cognition research and show that it reproduces many findings on semantic verification, concept typicality, feature distribution, and semantic similarity. We also compare our model against several variants, and by doing so, establish the model properties that are necessary for good prediction. The success of our approach shows how a combination of language data and (laboratory-based) psychological data can be used to build models with rich world knowledge. Such models can be used in the service of new psychological applications, such as the modeling of naturalistic semantic verification and knowledge retrieval, as well as the modeling of real-world categorization, decision-making, and reasoning. [ABSTRACT FROM AUTHOR]

This paper explores the integration of distributional semantics, hermeneutics, and semiotic theory as a powerful framework for analysing large textual corpora in the digital age. The advent of big data and advanced computational techniques has created new opportunities for bridging quantitative and qualitative approaches to textual analysis. By combining the data-driven insights of distributional semantics with the contextual depth of hermeneutic interpretation, researchers can uncover latent semantic structures while maintaining sensitivity to cultural and historical contexts. The paper argues that this integrated approach aligns well with semiotic theory, particularly Charles Sanders Peirce’s concept of semiosis. Peirce’s triadic model of sign interpretation provides a comprehensive lens for examining texts as dynamic semiotic systems, emphasising the importance of context and interpretation in meaning-making processes. By viewing texts as complex networks of signs interacting within specific contexts, researchers can gain richer, more nuanced insights into how meanings are constructed and reconstructed across diverse cultural landscapes. The integration of these approaches offers several advantages: it allows for the systematic analysis of large-scale textual data, provides a framework for understanding the contextual complexities of meaning, and bridges the gap between computational methods and interpretive analysis. However, it also raises important epistemological and ontological questions, particularly regarding the relationship between semiotic processes and material reality. The paper concludes by highlighting how the discussed theoretical and philosophical considerations can inform a methodological framework that synergises the analytical strengths of distributional semantics, hermeneutics, and semiotics. This potential framework promises a comprehensive approach to textual analysis, adept at unravelling both the macroscopic quantitative patterns found in extensive corpora and the intricate qualitative nuances essential for deep contextual interpretation. Implementing this framework could significantly advance text analysis methodologies across various disciplines such as linguistics, cultural studies, and the social sciences, especially within the rapidly expanding realm of big data. [ABSTRACT FROM AUTHOR]

AI language models are increasingly transforming language research in various ways. How can language educators and researchers respond to the challenge posed by these AI models? Specifically, how can we embrace this technology to inform and enhance second language learning and teaching? In order to quantitatively characterize and index second language writing, the current work proposes the use of similarities derived from contextualized meaning representations in AI language models. The computational analysis in this work is hypothesis-driven. The current work predicts how similarities should be distributed in a second language learning setting. The results suggest that similarity metrics are informative of writing proficiency assessment and interlanguage development. Statistically significant effects were found across multiple AI models. Most of the metrics could distinguish language learners’ proficiency levels. Significant correlations were also found between similarity metrics and learners’ writing test scores provided by human experts in the domain. However, not all such effects were strong or interpretable. Several results could not be consistently explained under the proposed second language learning hypotheses. Overall, the current investigation indicates that with careful configuration and systematic metrics design, AI language models can be promising tools in advancing language education.

In order to study global experience for legislation changing and rule-making necessitates, tools for information retrieval of regulatory documents written in different languages become increasingly necessary. One of the aspects of information identification is retrieval of thematically similar documents for a given input document. In this context, an important task of cross-lingual search arises when the user of an information system specifies a reference document in one language, and the search results contain relevant documents in other languages. The article describes different approaches to solving this problem: from classic mediator-based methods to more modern solutions, based on distributional semantics. The test collection used in the study was taken from the United Nations Digital Library, which provides legal documents in both the original English and their Russian translations. [ABSTRACT FROM AUTHOR]

Using a large data set of online media content in eight European countries, this paper broadens the empirical investigation of the online hostility hypothesis, which posits that interactions on social sites such as blogs and forums contain more hostile expressions toward minority groups than social interactions offline or in editorial news media. Overall, our results are consistent with the online hostility hypothesis when comparing news media content with social sites, but we find that negatively charged representations are common in both media types. It is instead the amount of attention to Muslims and Islam on social sites that most clearly differs and is the main driver of online hostility in the online media environment more broadly conceived. [ABSTRACT FROM AUTHOR]

Lexical ambiguity is pervasive in language, and the nature of the representations of an ambiguous word's multiple meanings is yet to be fully understood. With a special focus on Chinese characters, the present study first established that native speaker's perception about a character's number of meanings was heavily influenced by the availability of its distinct word formations, while whether these meanings would be perceived to be closely related was driven by further conceptual analysis. These notions were operationalized as two computed metrics, which assessed the degree of dispersion across individual word formations and the degree of propinquity across clusters of word formations, respectively, in a distributional semantic space. The observed correlations between the computed and the perceived metrics indicated that the utility of word formations to tap into meaning representations of Chinese characters was indeed cognitively plausible. The results have demonstrated the extent to which distributional semantics could inform about meaning representations of Chinese characters, which has theoretical implications for the representation of ambiguous words more generally. [ABSTRACT FROM AUTHOR]

Free association among words is a fundamental and ubiquitous memory task. Although distributed semantics (DS) models can predict the association between pairs of words, and semantic network (SN) models can describe transition probabilities in free association data, there have been few attempts to apply established cognitive process models of memory search to free association data. Thus, researchers are currently unable to explain the dynamics of free association using memory mechanisms known to be at play in other retrieval tasks, such as free recall from lists. We address this issue using a popular neural network model of free recall, the context maintenance and retrieval (CMR) model, which we fit using stochastic gradient descent on a large data set of free association norms. Special cases of CMR mimic existing DS and SN models of free association, and we find that CMR outperforms these models on out-of-sample free association data. We also show that training CMR on free association data generates improved predictions for free recall from lists, demonstrating the value of free association for the study of many different types of memory phenomena. Overall, our analysis provides a new account of the dynamics of free association, predicts free association with increased accuracy, integrates theories of free association with established models of memory, and shows how large data sets and neural network training methods can be used to model complex cognitive processes that operate over thousands of representations. [ABSTRACT FROM AUTHOR]

Children show a remarkable degree of consistency in learning some words earlier than others. What patterns of word usage predict variations among words in age of acquisition? We use distributional analysis of a naturalistic corpus of child-directed speech to create quantitative features representing natural variability in word contexts. We evaluate two sets of features: One set is generated from the distribution of words into frames defined by the two adjacent words. These features primarily encode syntactic aspects of word usage. The other set is generated from non-adjacent co-occurrences between words. These features encode complementary thematic aspects of word usage. Regression models using these distributional features to predict age of acquisition of 656 early-acquired English words indicate that both types of features improve predictions over simpler models based on frequency and appearance in salient or simple utterance contexts. Syntactic features were stronger predictors of children's production than comprehension, whereas thematic features were stronger predictors of comprehension. Overall, earlier acquisition was predicted by features representing frames that select for nouns and verbs, and by thematic content related to food and face-to-face play topics; later acquisition was predicted by features representing frames that select for pronouns and question words, and by content related to narratives and object play.

Abstract Unsupervised image translation (UIT) studies the mapping between two image domains. Since such mappings are under-constrained, existing research has pursued various desirable properties such as distributional matching or two-way consistency. In this paper, we re-examine UIT from a new perspective: distributional semantics consistency, based on the observation that data variations contain semantics, e.g., shoes varying in colors. Further, the semantics can be multi-dimensional, e.g., shoes also varying in style, functionality, etc. Given two image domains, matching these semantic dimensions during UIT will produce mappings with explicable correspondences, which has not been investigated previously. We propose distributional semantics mapping (DSM), the first UIT method which explicitly matches semantics between two domains. We show that distributional semantics has been rarely considered within and beyond UIT, even though it is a common problem in deep learning. We evaluate DSM on several benchmark datasets, demonstrating its general ability to capture distributional semantics. Extensive comparisons show that DSM not only produces explicable mappings, but also improves image quality in general.

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.

ABSTRACTUsing a large data set of online media content in eight European countries, this paper broadens the empirical investigation of the online hostility hypothesis, which posits that interactions on social sites such as blogs and forums contain more hostile expressions toward minority groups than social interactions offline or in editorial news media. Overall, our results are consistent with the online hostility hypothesis when comparing news media content with social sites, but we find that negatively charged representations are common in both media types. It is instead the amount of attention to Muslims and Islam on social sites that most clearly differs and is the main driver of online hostility in the online media environment more broadly conceived.