Zoekresultaten

Is beta in agreement with the relatives? Using relative clause sentences to investigate MEG beta power dynamics during sentence comprehension.

There remains some debate about whether beta power effects observed during sentence comprehension reflect ongoing syntactic unification operations (beta-syntax hypothesis), or instead reflect maintenance or updating of the sentence-level representation (beta-maintenance hypothesis). In this study, we used magnetoencephalography to investigate beta power neural dynamics while participants read relative clause sentences that were initially ambiguous between a subject- or an object-relative reading. An additional condition included a grammatical violation at the disambiguation point in the relative clause sentences. The beta-maintenance hypothesis predicts a decrease in beta power at the disambiguation point for unexpected (and less preferred) object-relative clause sentences and grammatical violations, as both signal a need to update the sentence-level representation. While the beta-syntax hypothesis also predicts a beta power decrease for grammatical violations due to a disruption of syntactic unification operations, it instead predicts an increase in beta power for the object-relative clause condition because syntactic unification at the point of disambiguation becomes more demanding. We observed decreased beta power for both the agreement violation and object-relative clause conditions in typical left hemisphere language regions, which provides compelling support for the beta-maintenance hypothesis. Mid-frontal theta power effects were also present for grammatical violations and object-relative clause sentences, suggesting that violations and unexpected sentence interpretations are registered as conflicts by the brain's domain-general error detection system.

You've got to crack a few eggs to make an omelette.

In this epilogue, I take a teaching practice and teacher education perspective on complexity in Instructed Second Language Acquisition. I take the stance that it is essential to understand if and how linguistic complexity relates to learning challenges, what the implications are for language pedagogy, and how this challenges the role of the teacher. Research shows that differences in task complexity may lead to differences in linguistic complexity in language learners’ speech or writing. Different tasks (e.g. descriptive vs narrative) and different modes (oral vs written) may lead to different types and levels of complexity in language use. On the one hand, this is a challenge for language assessment, as complexity in language performance may be affected by task characteristics. On the other hand, it is an opportunity for language teaching: using a diversity of tasks, modes and text types may evoke and stretch lexically and syntactically complex language use. I maintain that it is essential for teachers to understand that it is at least as important to aim for development in complexity as it is to aim for development in accuracy. Namely, that ‘errors’ in language learning are part of the deal: complex tasks lead to complex language use, including lexical and syntactical errors, but they are a necessary prerequisite for language development.

Grammaticale ontwikkeling van zich normaal ontwikkelende kinderen en kinderen met TOS in de leeftijd van 7 tot 10 jaar

Dit artikel beschrijft de bevindingen uit een explorerend literatuuronderzoek naar de grammaticale ontwikkeling van Nederlandstalige kinderen tussen 7 en 10 jaar met en zonder een taalontwikkelingsstoornis (TOS). De resultaten worden ook in een klinische context geplaatst, met als doel logopedisten te ondersteunen in de behandeling van grammaticale problemen bij kinderen met TOS in deze leeftijdsgroep. Grammaticale problemen zijn een kernprobleem van kinderen metTOSen behandeling van deze grammaticale problemen is daarmee een kerntaak van de logopedist. De verwervingsvolgorde van grammaticale structuren bij Nederlandstalige kinderen met een normale taalontwikkeling is redelijk duidelijk tot een leeftijd van 5 á 6 jaar. Voor oudere kinderen is dit veel minder het geval. Deze informatie is echter wel van belang om passende grammaticale behandeldoelen te kunnen selecteren en prioriteren.

Cluttering identified: differential diagnostics between cluttering, stuttering and speech impairment related to learning difficulties

Handreiking Spontane Taalanalyse van Kindertaal

Een spontane taalanalyse (STA) wordt gezien als de gouden standaard voor de analyse van grammaticale problemen en het stellen van behandeldoelen bij kinderen met TOS. Logopedisten erkennen het belang van STA maar geven ook aan deze analyses weinig uit te voeren. Genoemde redenen hiervoor zijn een gebrek aan tijd, kennis en vaardigheden. Er is veel behoefte aan meer kennis over grammaticale ontwikkeling bij kinderen in de leeftijd van 7-10 jaar, het slim en snel uitvoeren van STA en het selecteren van geschikte behandeldoelen. Deze handreiking heeft als doel in deze behoefte te voorzien door kennis en vaardigheden over STA toegankelijker en beter toepasbaar te maken. Omdat logopedisten vooral veel vragen hebben over kinderen met TOS van 7-10 jaar, ligt de focus in deze handreiking op deze leeftijdsgroep. De handreiking is ontwikkeld binnen het onderzoeksproject ZINnig van het Lectoraat Logopedie van Hogeschool Utrecht.

Automatic Measurement of Corporate Reputation for Retail Companies from Online Public Data on the Web

Retail industry consists of the establishment of selling consumer goods (i.e. technology, pharmaceuticals, food and beverages, apparels and accessories, home improvement etc.) and services (i.e. specialty and movies) to customers through multiple channels of distribution including both the traditional brickand-mortar and online retailing. Managing corporate reputation of retail companies is crucial as it has many advantages, for instance, it has been proven to impact generated revenues (Wang et al., 2016). But, in order to be able to manage corporate reputation, one has to be able to measure it, or, nowadays even better, listen to relevant social signals that are out there on the public web. One of the most extensive and widely used frameworks for measuring corporate reputation is through conducting elaborated surveys with respective stakeholders (Fombrun et al., 2015). This approach is valuable but deemed to be laborious and resource-heavy and will not allow to generate automatic alerts and quick and live insights that are extremely needed in this era of internet. For these purposes a social listening approach is needed that can be tailored to online data such as consumer reviews as the main data source. Online review datasets are a form of electronic Word-of-Mouth (WOM) that, when a data source is picked that is relevant to retail, commonly contain relevant information about customers’ perceptions regarding products (Pookulangara, 2011) and that are massively available. The algorithm that we have built in our application provides retailers with reputation scores for all variables that are deemed to be relevant to retail in the model of Fombrun et al. (2015). Examples of such variables for products and services are high quality, good value, stands behind, and meets customer needs. We propose a new set of subvariables with which these variables can be operationalized for retail in particular. Scores are being calculated using proportions of positive opinion pairs such as <fast, delivery> or <rude, staff> that have been designed per variable. With these important insights extracted, companies can act accordingly and proceed to improve their corporate reputation. It is important to emphasize that, once the design is complete and implemented, all processing can be performed completely automatic and unsupervised. The application makes use of a state of the art aspect-based sentiment analysis (ABSA) framework because of ABSA’s ability to generate sentiment scores for all relevant variables and aspects. Since most online data is in open form and we deliberately want to avoid labelling any data by human experts, the unsupervised aspectator algorithm has been picked. It employs a lexicon to calculate sentiment scores and uses syntactic dependency paths to discover candidate aspects (Bancken et al., 2014). We have applied our approach to a large number of online review datasets that we sampled from a list of 50 top global retailers according to National Retail Federation (2020), including both offline and online operation, and that we scraped from trustpilot, a public website that is well-known to retailers. The algorithm has carefully been evaluated by manually annotating a randomly sampled subset of the datasets for validation purposes by two independent annotators. The Kappa’s score on this subset was 80%.