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description Publicationkeyboard_double_arrow_right Article 2021Publisher:Walter de Gruyter GmbH Funded by:EC | WIDEEC| WIDEAuthors: Karlina Denistia; Elnaz Shafaei-Bajestan; R. Harald Baayen;Karlina Denistia; Elnaz Shafaei-Bajestan; R. Harald Baayen;AbstractIndonesian has two prefixes,PE-andPEN-, 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 withPE-and a word withPEN-were reduced compared to pairs of onlyPE-words, or of onlyPEN-words. Furthermore, nouns withPE-were more similar to their base words than was the case for words withPEN-. The specialized use ofPE-for words denoting agents, and the specialized use ofPEN-for denoting instruments, was also visible in the semantic vector space. These differences in the semantics ofPE-andPEN-thus provide further quantitative support for the independent status ofPE-as opposed toPEN-.
ZENODO; Corpus Lingu... arrow_drop_down ZENODO; Corpus Linguistics and Linguistic TheoryOther literature type . Article . 2021 . Peer-reviewedLicense: CC BYadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1515/cllt-2020-0023&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euAccess Routeshybrid 4 citations 4 popularity Top 10% influence Average impulse Average Powered by BIP!more_vert ZENODO; Corpus Lingu... arrow_drop_down ZENODO; Corpus Linguistics and Linguistic TheoryOther literature type . Article . 2021 . Peer-reviewedLicense: CC BYadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1515/cllt-2020-0023&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article 2018Publisher:Wiley Funded by:EC | WIDEEC| WIDEAuthors: Sering, K.; Milin, P.; Baayen, R.H.;Sering, K.; Milin, P.; Baayen, R.H.;doi: 10.1111/stan.12134
The initial stage of language comprehension is a multi-label\ud classification problem. Listeners or readers, presented with\ud an utterance, need to discriminate between the intended\ud words and the tens of thousands of other words they know.\ud We propose to address this problem by pairing a network\ud trained with the learning rule of Rescorla andWagner (1972)\ud with a second network trained independently with the learning\ud rule of Widrow and Hoff (1960). The first network has\ud to recover from sublexical input features the meanings encoded\ud in the language signal, resulting in a vector of activations\ud over the lexicon. The second network takes this\ud vector as input and further reduces uncertainty about the\ud intended message. Classification performance for a lexicon\ud with 52,000 entries is good. The model also correctly predicts\ud several aspects of human language comprehension. By\ud rejecting the traditional linguistic assumption that language\ud is a (de)compositional system, and by instead espousing a\ud discriminative approach (Ramscar, 2013), a more parsimonious\ud yet highly effective functional characterization of the\ud initial stage of language comprehension is obtained.
CORE (RIOXX-UK Aggre... arrow_drop_down ZENODO; Statistica NeerlandicaOther literature type . Article . 2018 . Peer-reviewedLicense: CC BYadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1111/stan.12134&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euAccess Routesbronze 8 citations 8 popularity Top 10% influence Average impulse Average Powered by BIP!visibility 35visibility views 35 download downloads 86 Powered bymore_vert CORE (RIOXX-UK Aggre... arrow_drop_down ZENODO; Statistica NeerlandicaOther literature type . Article . 2018 . Peer-reviewedLicense: CC BYadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1111/stan.12134&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article 2019 United KingdomPublisher:Hindawi Limited Funded by:EC | WIDEEC| WIDEAuthors: Harald Baayen, R; Chuang, YY; Shafaei-Bajestan, E; Blevins, James;Harald Baayen, R; Chuang, YY; Shafaei-Bajestan, E; Blevins, James;© 2019 R. Harald Baayen et al. The discriminative lexicon is introduced as a mathematical and computational model of the mental lexicon. This novel theory is inspired by word and paradigm morphology but operationalizes the concept of proportional analogy using the mathematics of linear algebra. It embraces the discriminative perspective on language, rejecting the idea that words' meanings are compositional in the sense of Frege and Russell and arguing instead that the relation between form and meaning is fundamentally discriminative. The discriminative lexicon also incorporates the insight from machine learning that end-to-end modeling is much more effective than working with a cascade of models targeting individual subtasks. The computational engine at the heart of the discriminative lexicon is linear discriminative learning: simple linear networks are used for mapping form onto meaning and meaning onto form, without requiring the hierarchies of post-Bloomfieldian 'hidden' constructs such as phonemes, morphemes, and stems. We show that this novel model meets the criteria of accuracy (it properly recognizes words and produces words correctly), productivity (the model is remarkably successful in understanding and producing novel complex words), and predictivity (it correctly predicts a wide array of experimental phenomena in lexical processing). The discriminative lexicon does not make use of static representations that are stored in memory and that have to be accessed in comprehension and production. It replaces static representations by states of the cognitive system that arise dynamically as a consequence of external or internal stimuli. The discriminative lexicon brings together visual and auditory comprehension as well as speech production into an integrated dynamic system of coupled linear networks.
Apollo arrow_drop_down ZENODO; Hindawi Publishing Corporation; ComplexityOther literature type . Article . 2019 . Peer-reviewedLicense: CC BYadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1155/2019/4895891&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euAccess RoutesGreen gold 111 citations 111 popularity Top 1% influence Top 10% impulse Top 1% Powered by BIP!visibility 63visibility views 63 download downloads 160 Powered bymore_vert Apollo arrow_drop_down ZENODO; Hindawi Publishing Corporation; ComplexityOther literature type . Article . 2019 . Peer-reviewedLicense: CC BYadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1155/2019/4895891&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Preprint , Article 2019 Netherlands, BelgiumPublisher:Center for Open Science Funded by:EC | WIDEEC| WIDEAuthors: Cassani, Giovanni; Chuang, Yu-Ying; Baayen, R.;Cassani, Giovanni; Chuang, Yu-Ying; Baayen, R.;Using computational simulations, this work demonstrates that it is possible to learn a systematic relation between words' sound and their meanings. The sound-meaning relation was learned from a corpus of phonologically transcribed child-directed speech by using the linear discriminative learning (LDL) framework (Baayen, Chuang, Shafaei-Bajestan, & Blevins, 2019), which implements linear mappings between words' form vectors and semantic vectors. Presented with the form vectors of 16 nonwords, taken from a study on word learning (Fitneva, Christiansen, & Monaghan, 2009), the network generated the estimated semantic vectors of the nonwords. As half of these nonwords were created to phonologically resemble English nouns and the other half were phonologically similar to English verbs, we assessed whether the estimated semantic vectors for these nonwords reflect this word category difference. In 7 different simulations, linear discriminant analysis (LDA) successfully discriminated between noun-like nonwords and verb-like nonwords, based on their semantic relation to the words in the lexicon. Furthermore, how well LDA categorized a nonword correlated well with a phonological typicality measure (i.e., the degree of its form being noun-like or verb-like) and with children's performance in an entity/action discrimination task. On the one hand, the results suggest that children can infer the implicit meaning of a word directly from its sound. On the other hand, this study shows that nonwords do land in semantic space, such that children can capitalize on their semantic relations with other elements in the lexicon to decide whether a nonword is more likely to denote an entity or an action. (PsycInfo Database Record (c) 2020 APA, all rights reserved).
ZENODO; Journal of E... arrow_drop_down ZENODO; Journal of Experimental Psychology Learning Memory and CognitionOther literature type . Article . 2020 . 2019 . Peer-reviewedLicense: CC BYJournal of Experimental Psychology Learning Memory and CognitionArticle . PreprintData sources: UnpayWallInstitutional Repository Universiteit AntwerpenArticle . 2020Data sources: Institutional Repository Universiteit AntwerpenJournal of Experimental Psychology Learning Memory and CognitionArticle . 2019Data sources: Europe PubMed Centraladd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.31234/osf.io/qgsef&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euAccess Routeshybrid 8 citations 8 popularity Top 10% influence Average impulse Top 10% Powered by BIP!visibility 26visibility views 26 download downloads 15 Powered bymore_vert ZENODO; Journal of E... arrow_drop_down ZENODO; Journal of Experimental Psychology Learning Memory and CognitionOther literature type . Article . 2020 . 2019 . Peer-reviewedLicense: CC BYJournal of Experimental Psychology Learning Memory and CognitionArticle . PreprintData sources: UnpayWallInstitutional Repository Universiteit AntwerpenArticle . 2020Data sources: Institutional Repository Universiteit AntwerpenJournal of Experimental Psychology Learning Memory and CognitionArticle . 2019Data sources: Europe PubMed Centraladd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.31234/osf.io/qgsef&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article 2021Publisher:Elsevier BV Funded by:EC | WIDEEC| WIDEAuthors: Sun, Kun; Baayen, R. Harald;Sun, Kun; Baayen, R. Harald;Abstract Hyphenated compounds have largely been neglected in the studies of compounding, which have seldom analysed compounds in context. In this study, we argue that the hyphen use in compounds is strongly motivated. Hyphenation is used when words form a unit, which reduces the possibility of parsing them into separate units or other forms. The current study adopts a new perspective on contextual factors, namely, which part of speech (PoS) the compound as a whole belongs to and how people correctly parse a compound into a unit. This process can be observed and analysed by considering examples. This study therefore holds that hyphenation might have gradually become a compounding technique that differs from general compounding principles. To better understand hyphenated compounds and the motivation for using hyphenation, we conduct a quantitative investigation into their distribution frequency to explore how English hyphenated compounds have been used in over the last 200 years. Diachronic change in the frequency of the distribution for compounds has seldom been considered. This question is explored by using frequency data obtained from the three databases that contain hyphenated compounds. Diachronic analysis shows that the frequencies of tokens and types in hyphenated compounds have been increasing, and changes in both frequencies follow the S-curve model. Historical evidence shows that hyphenation in compounds, as an orthographic form, does not seem to disappear easily. Familiarity and economy, as suggested in the cognitive studies of compounding, cannot adequately explain this phenomenon. The three databases that we used provide cross-verification that suggests that hyphenation has evolved into a compounding technique. Language users probably unconsciously take advantage of the discriminative learning model to remind themselves that these combinations should be parsed differently. Thus the hyphenation compounding technique facilitates communication efficiency. Overall, this study significantly enhances our understanding of the nature of compounding, the motivations for using hyphenation, and its cognitive processing.
ZENODO; Language Sci... arrow_drop_down ZENODO; Language SciencesOther literature type . Article . 2021 . 2020 . Peer-reviewedLicense: Elsevier TDMadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1016/j.langsci.2020.101326&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euAccess RoutesGreen hybrid 2 citations 2 popularity Average influence Average impulse Average Powered by BIP!visibility 25visibility views 25 download downloads 34 Powered bymore_vert ZENODO; Language Sci... arrow_drop_down ZENODO; Language SciencesOther literature type . Article . 2021 . 2020 . Peer-reviewedLicense: Elsevier TDMadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1016/j.langsci.2020.101326&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article , Preprint 2020 United StatesPublisher:Center for Open Science Funded by:EC | WIDEEC| WIDEYu-Ying Chuang; Marie-lenka Voller; Elnaz Shafaei-Bajestan; Susanne Gahl; Peter Hendrix; R. H. Baayen;Pseudowords have long served as key tools in psycholinguistic investigations of the lexicon. A common assumption underlying the use of pseudowords is that they are devoid of meaning: Comparing words and pseudowords may then shed light on how meaningful linguistic elements are processed differently from meaningless sound strings. However, pseudowords may in fact carry meaning. On the basis of a computational model of lexical processing, linear discriminative learning (LDL Baayen et al., Complexity, 2019, 1–39, 2019), we compute numeric vectors representing the semantics of pseudowords. We demonstrate that quantitative measures gauging the semantic neighborhoods of pseudowords predict reaction times in the Massive Auditory Lexical Decision (MALD) database (Tucker et al., 2018). We also show that the model successfully predicts the acoustic durations of pseudowords. Importantly, model predictions hinge on the hypothesis that the mechanisms underlying speech production and comprehension interact. Thus, pseudowords emerge as an outstanding tool for gauging the resonance between production and comprehension. Many pseudowords in the MALD database contain inflectional suffixes. Unlike many contemporary models, LDL captures the semantic commonalities of forms sharing inflectional exponents without using the linguistic construct of morphemes. We discuss methodological and theoretical implications for models of lexical processing and morphological theory. The results of this study, complementing those on real words reported in Baayen et al., (Complexity, 2019, 1–39, 2019), thus provide further evidence for the usefulness of LDL both as a cognitive model of the mental lexicon, and as a tool for generating new quantitative measures that are predictive for human lexical processing. Electronic supplementary material The online version of this article (10.3758/s13428-020-01356-w) contains supplementary material, which is available to authorized users.
Europe PubMed Centra... arrow_drop_down Europe PubMed CentralArticle . 2020Full-Text: http://europepmc.org/articles/PMC8219637Data sources: PubMed CentralZENODO; Behavior Research MethodsOther literature type . Article . 2020 . Peer-reviewedLicense: CC BYeScholarship - University of CaliforniaArticle . 2021Data sources: eScholarship - University of Californiaadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.31234/osf.io/byrux&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euAccess RoutesGreen hybrid 29 citations 29 popularity Top 10% influence Average impulse Top 10% Powered by BIP!visibility 20visibility views 20 download downloads 46 Powered bymore_vert Europe PubMed Centra... arrow_drop_down Europe PubMed CentralArticle . 2020Full-Text: http://europepmc.org/articles/PMC8219637Data sources: PubMed CentralZENODO; Behavior Research MethodsOther literature type . Article . 2020 . Peer-reviewedLicense: CC BYeScholarship - University of CaliforniaArticle . 2021Data sources: eScholarship - University of Californiaadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.31234/osf.io/byrux&type=result"></script>'); --> </script>
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description Publicationkeyboard_double_arrow_right Article 2021Publisher:Walter de Gruyter GmbH Funded by:EC | WIDEEC| WIDEAuthors: Karlina Denistia; Elnaz Shafaei-Bajestan; R. Harald Baayen;Karlina Denistia; Elnaz Shafaei-Bajestan; R. Harald Baayen;AbstractIndonesian has two prefixes,PE-andPEN-, 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 withPE-and a word withPEN-were reduced compared to pairs of onlyPE-words, or of onlyPEN-words. Furthermore, nouns withPE-were more similar to their base words than was the case for words withPEN-. The specialized use ofPE-for words denoting agents, and the specialized use ofPEN-for denoting instruments, was also visible in the semantic vector space. These differences in the semantics ofPE-andPEN-thus provide further quantitative support for the independent status ofPE-as opposed toPEN-.
ZENODO; Corpus Lingu... arrow_drop_down ZENODO; Corpus Linguistics and Linguistic TheoryOther literature type . Article . 2021 . Peer-reviewedLicense: CC BYadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1515/cllt-2020-0023&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euAccess Routeshybrid 4 citations 4 popularity Top 10% influence Average impulse Average Powered by BIP!more_vert ZENODO; Corpus Lingu... arrow_drop_down ZENODO; Corpus Linguistics and Linguistic TheoryOther literature type . Article . 2021 . Peer-reviewedLicense: CC BYadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1515/cllt-2020-0023&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article 2018Publisher:Wiley Funded by:EC | WIDEEC| WIDEAuthors: Sering, K.; Milin, P.; Baayen, R.H.;Sering, K.; Milin, P.; Baayen, R.H.;doi: 10.1111/stan.12134
The initial stage of language comprehension is a multi-label\ud classification problem. Listeners or readers, presented with\ud an utterance, need to discriminate between the intended\ud words and the tens of thousands of other words they know.\ud We propose to address this problem by pairing a network\ud trained with the learning rule of Rescorla andWagner (1972)\ud with a second network trained independently with the learning\ud rule of Widrow and Hoff (1960). The first network has\ud to recover from sublexical input features the meanings encoded\ud in the language signal, resulting in a vector of activations\ud over the lexicon. The second network takes this\ud vector as input and further reduces uncertainty about the\ud intended message. Classification performance for a lexicon\ud with 52,000 entries is good. The model also correctly predicts\ud several aspects of human language comprehension. By\ud rejecting the traditional linguistic assumption that language\ud is a (de)compositional system, and by instead espousing a\ud discriminative approach (Ramscar, 2013), a more parsimonious\ud yet highly effective functional characterization of the\ud initial stage of language comprehension is obtained.
CORE (RIOXX-UK Aggre... arrow_drop_down ZENODO; Statistica NeerlandicaOther literature type . Article . 2018 . Peer-reviewedLicense: CC BYadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1111/stan.12134&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euAccess Routesbronze 8 citations 8 popularity Top 10% influence Average impulse Average Powered by BIP!visibility 35visibility views 35 download downloads 86 Powered bymore_vert CORE (RIOXX-UK Aggre... arrow_drop_down ZENODO; Statistica NeerlandicaOther literature type . Article . 2018 . Peer-reviewedLicense: CC BYadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1111/stan.12134&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article 2019 United KingdomPublisher:Hindawi Limited Funded by:EC | WIDEEC| WIDEAuthors: Harald Baayen, R; Chuang, YY; Shafaei-Bajestan, E; Blevins, James;Harald Baayen, R; Chuang, YY; Shafaei-Bajestan, E; Blevins, James;© 2019 R. Harald Baayen et al. The discriminative lexicon is introduced as a mathematical and computational model of the mental lexicon. This novel theory is inspired by word and paradigm morphology but operationalizes the concept of proportional analogy using the mathematics of linear algebra. It embraces the discriminative perspective on language, rejecting the idea that words' meanings are compositional in the sense of Frege and Russell and arguing instead that the relation between form and meaning is fundamentally discriminative. The discriminative lexicon also incorporates the insight from machine learning that end-to-end modeling is much more effective than working with a cascade of models targeting individual subtasks. The computational engine at the heart of the discriminative lexicon is linear discriminative learning: simple linear networks are used for mapping form onto meaning and meaning onto form, without requiring the hierarchies of post-Bloomfieldian 'hidden' constructs such as phonemes, morphemes, and stems. We show that this novel model meets the criteria of accuracy (it properly recognizes words and produces words correctly), productivity (the model is remarkably successful in understanding and producing novel complex words), and predictivity (it correctly predicts a wide array of experimental phenomena in lexical processing). The discriminative lexicon does not make use of static representations that are stored in memory and that have to be accessed in comprehension and production. It replaces static representations by states of the cognitive system that arise dynamically as a consequence of external or internal stimuli. The discriminative lexicon brings together visual and auditory comprehension as well as speech production into an integrated dynamic system of coupled linear networks.
Apollo arrow_drop_down ZENODO; Hindawi Publishing Corporation; ComplexityOther literature type . Article . 2019 . Peer-reviewedLicense: CC BYadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1155/2019/4895891&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euAccess RoutesGreen gold 111 citations 111 popularity Top 1% influence Top 10% impulse Top 1% Powered by BIP!visibility 63visibility views 63 download downloads 160 Powered bymore_vert Apollo arrow_drop_down ZENODO; Hindawi Publishing Corporation; ComplexityOther literature type . Article . 2019 . Peer-reviewedLicense: CC BYadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1155/2019/4895891&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Preprint , Article 2019 Netherlands, BelgiumPublisher:Center for Open Science Funded by:EC | WIDEEC| WIDEAuthors: Cassani, Giovanni; Chuang, Yu-Ying; Baayen, R.;Cassani, Giovanni; Chuang, Yu-Ying; Baayen, R.;Using computational simulations, this work demonstrates that it is possible to learn a systematic relation between words' sound and their meanings. The sound-meaning relation was learned from a corpus of phonologically transcribed child-directed speech by using the linear discriminative learning (LDL) framework (Baayen, Chuang, Shafaei-Bajestan, & Blevins, 2019), which implements linear mappings between words' form vectors and semantic vectors. Presented with the form vectors of 16 nonwords, taken from a study on word learning (Fitneva, Christiansen, & Monaghan, 2009), the network generated the estimated semantic vectors of the nonwords. As half of these nonwords were created to phonologically resemble English nouns and the other half were phonologically similar to English verbs, we assessed whether the estimated semantic vectors for these nonwords reflect this word category difference. In 7 different simulations, linear discriminant analysis (LDA) successfully discriminated between noun-like nonwords and verb-like nonwords, based on their semantic relation to the words in the lexicon. Furthermore, how well LDA categorized a nonword correlated well with a phonological typicality measure (i.e., the degree of its form being noun-like or verb-like) and with children's performance in an entity/action discrimination task. On the one hand, the results suggest that children can infer the implicit meaning of a word directly from its sound. On the other hand, this study shows that nonwords do land in semantic space, such that children can capitalize on their semantic relations with other elements in the lexicon to decide whether a nonword is more likely to denote an entity or an action. (PsycInfo Database Record (c) 2020 APA, all rights reserved).
ZENODO; Journal of E... arrow_drop_down ZENODO; Journal of Experimental Psychology Learning Memory and CognitionOther literature type . Article . 2020 . 2019 . Peer-reviewedLicense: CC BYJournal of Experimental Psychology Learning Memory and CognitionArticle . PreprintData sources: UnpayWallInstitutional Repository Universiteit AntwerpenArticle . 2020Data sources: Institutional Repository Universiteit AntwerpenJournal of Experimental Psychology Learning Memory and CognitionArticle . 2019Data sources: Europe PubMed Centraladd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.31234/osf.io/qgsef&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euAccess Routeshybrid 8 citations 8 popularity Top 10% influence Average impulse Top 10% Powered by BIP!visibility 26visibility views 26 download downloads 15 Powered bymore_vert ZENODO; Journal of E... arrow_drop_down ZENODO; Journal of Experimental Psychology Learning Memory and CognitionOther literature type . Article . 2020 . 2019 . Peer-reviewedLicense: CC BYJournal of Experimental Psychology Learning Memory and CognitionArticle . PreprintData sources: UnpayWallInstitutional Repository Universiteit AntwerpenArticle . 2020Data sources: Institutional Repository Universiteit AntwerpenJournal of Experimental Psychology Learning Memory and CognitionArticle . 2019Data sources: Europe PubMed Centraladd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.31234/osf.io/qgsef&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article 2021Publisher:Elsevier BV Funded by:EC | WIDEEC| WIDEAuthors: Sun, Kun; Baayen, R. Harald;Sun, Kun; Baayen, R. Harald;Abstract Hyphenated compounds have largely been neglected in the studies of compounding, which have seldom analysed compounds in context. In this study, we argue that the hyphen use in compounds is strongly motivated. Hyphenation is used when words form a unit, which reduces the possibility of parsing them into separate units or other forms. The current study adopts a new perspective on contextual factors, namely, which part of speech (PoS) the compound as a whole belongs to and how people correctly parse a compound into a unit. This process can be observed and analysed by considering examples. This study therefore holds that hyphenation might have gradually become a compounding technique that differs from general compounding principles. To better understand hyphenated compounds and the motivation for using hyphenation, we conduct a quantitative investigation into their distribution frequency to explore how English hyphenated compounds have been used in over the last 200 years. Diachronic change in the frequency of the distribution for compounds has seldom been considered. This question is explored by using frequency data obtained from the three databases that contain hyphenated compounds. Diachronic analysis shows that the frequencies of tokens and types in hyphenated compounds have been increasing, and changes in both frequencies follow the S-curve model. Historical evidence shows that hyphenation in compounds, as an orthographic form, does not seem to disappear easily. Familiarity and economy, as suggested in the cognitive studies of compounding, cannot adequately explain this phenomenon. The three databases that we used provide cross-verification that suggests that hyphenation has evolved into a compounding technique. Language users probably unconsciously take advantage of the discriminative learning model to remind themselves that these combinations should be parsed differently. Thus the hyphenation compounding technique facilitates communication efficiency. Overall, this study significantly enhances our understanding of the nature of compounding, the motivations for using hyphenation, and its cognitive processing.
ZENODO; Language Sci... arrow_drop_down ZENODO; Language SciencesOther literature type . Article . 2021 . 2020 . Peer-reviewedLicense: Elsevier TDMadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1016/j.langsci.2020.101326&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euAccess RoutesGreen hybrid 2 citations 2 popularity Average influence Average impulse Average Powered by BIP!visibility 25visibility views 25 download downloads 34 Powered bymore_vert ZENODO; Language Sci... arrow_drop_down ZENODO; Language SciencesOther literature type . Article . 2021 . 2020 . Peer-reviewedLicense: Elsevier TDMadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1016/j.langsci.2020.101326&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article , Preprint 2020 United StatesPublisher:Center for Open Science Funded by:EC | WIDEEC| WIDEYu-Ying Chuang; Marie-lenka Voller; Elnaz Shafaei-Bajestan; Susanne Gahl; Peter Hendrix; R. H. Baayen;Pseudowords have long served as key tools in psycholinguistic investigations of the lexicon. A common assumption underlying the use of pseudowords is that they are devoid of meaning: Comparing words and pseudowords may then shed light on how meaningful linguistic elements are processed differently from meaningless sound strings. However, pseudowords may in fact carry meaning. On the basis of a computational model of lexical processing, linear discriminative learning (LDL Baayen et al., Complexity, 2019, 1–39, 2019), we compute numeric vectors representing the semantics of pseudowords. We demonstrate that quantitative measures gauging the semantic neighborhoods of pseudowords predict reaction times in the Massive Auditory Lexical Decision (MALD) database (Tucker et al., 2018). We also show that the model successfully predicts the acoustic durations of pseudowords. Importantly, model predictions hinge on the hypothesis that the mechanisms underlying speech production and comprehension interact. Thus, pseudowords emerge as an outstanding tool for gauging the resonance between production and comprehension. Many pseudowords in the MALD database contain inflectional suffixes. Unlike many contemporary models, LDL captures the semantic commonalities of forms sharing inflectional exponents without using the linguistic construct of morphemes. We discuss methodological and theoretical implications for models of lexical processing and morphological theory. The results of this study, complementing those on real words reported in Baayen et al., (Complexity, 2019, 1–39, 2019), thus provide further evidence for the usefulness of LDL both as a cognitive model of the mental lexicon, and as a tool for generating new quantitative measures that are predictive for human lexical processing. Electronic supplementary material The online version of this article (10.3758/s13428-020-01356-w) contains supplementary material, which is available to authorized users.
Europe PubMed Centra... arrow_drop_down Europe PubMed CentralArticle . 2020Full-Text: http://europepmc.org/articles/PMC8219637Data sources: PubMed CentralZENODO; Behavior Research MethodsOther literature type . Article . 2020 . Peer-reviewedLicense: CC BYeScholarship - University of CaliforniaArticle . 2021Data sources: eScholarship - University of Californiaadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.31234/osf.io/byrux&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euAccess RoutesGreen hybrid 29 citations 29 popularity Top 10% influence Average impulse Top 10% Powered by BIP!visibility 20visibility views 20 download downloads 46 Powered bymore_vert Europe PubMed Centra... arrow_drop_down Europe PubMed CentralArticle . 2020Full-Text: http://europepmc.org/articles/PMC8219637Data sources: PubMed CentralZENODO; Behavior Research MethodsOther literature type . Article . 2020 . Peer-reviewedLicense: CC BYeScholarship - University of CaliforniaArticle . 2021Data sources: eScholarship - University of Californiaadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.31234/osf.io/byrux&type=result"></script>'); --> </script>
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