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Categorization and identification of typical exemplars within semantic categories is a universal skill of human cognition which is involved in language development. However, cultural, and experiential aspects might influence typicality effects. This paper examines the role of native language and culture on that categorization process and on typicality effects. Towards that objective, we had Spanish native speakers and Spanish FL learners whose mother tongue is Greek complete a category generation word association task. Data were analyzed within a network and graph theory framework as the best fitting for this type of data, bearing in mind previous descriptions of semantic memory. Results showed how, indeed, native speakers and learners of varying proficiency levels differ in their availability and production of typical exemplars, especially in slot-filler categories versus taxonomic categories. Lexical access during category generation might be determined by native language and culture. Additionally, natives’ mental lexicon seems to feature denser connections responsible for more efficient access.

The paper describes a semi-automatic procedure introducing semantic labels into the DeriNet network, which is a large, freely available resource modeling derivational relations in the lexicon of Czech. The data were assigned labels corresponding to five semantic categories (diminutives, possessives, female nouns, iteratives, and aspectual meanings) by a machine learning model, which achieved excellent results in terms of both precision and recall.

This study aims to add further pedagogical knowledge on students’ academic writing by investigating the semantic patterns of reporting verbs (RVs) in L1 Chinese undergraduate English majors’ theses in a southern Chinese university based on the semantic categories by Hunston et al. (1996) and Charles (2006a). A comparative analysis was conducted across L2 and L1 students’ academic writing in the discipline of applied linguistics. The study yielded two major findings: 1) there was a significantly insufficient employment of RVs in general, particularly among three categories (Argue, Show, Find) by L2 students, who also presented a strong reliance on argumentation by intuition; 2) L2 students illustrated a restricted vocabulary repertoire of colloquial RVs and their usage of RVs was misrepresented in context, diverging from the intended rhetorical functions. These findings indicate that evidence-based argumentative writing practice and targeted lexical and rhetorical instructions on vocabulary knowledge require further promotion in L2 English learners’ academic writing training.

Three experiments investigated memory for semantic information with the goal of determining boundary conditions for the manifestation of semantic auditory distraction. Irrelevant speech disrupted the free recall of semantic category- exemplars to an equal degree regardless of whether the speech coincided with presentation or test phases of the task (Experiment 1 ), and this occurred regardless of whether it comprised random words or coherent sentences (Experiment 2 ). The effects of background speech were greater when the irrelevant speech was semantically related to the to-be-remembered material, but only when the irrelevant words were high in output dominance (Experiment 3 ). The implications of these findings in relation to the processing of task material and the processing of background speech are discussed.

In the present study, we showed evidence of an integration between two unconscious semantic representations. In experiment 1, two masked Chinese words of the same or different categories (“orange apple” or “grape hammer”) were simultaneously presented in the prime, followed by two Chinese words also of same or different categories in the target. We examined possible prime/target visual feature priming, semantic category priming and motor response priming effects. Moreover, two ISI intervals (53, 163 ms) between the prime and the target words were used to examine the positive and negative priming. The results revealed a negative motor response priming and a positive semantic category priming effect independent of the ISI when the target words were of the same category. Experiment 2 eliminated an alternative interpretation of the effect based on different number of category words changed across the prime and the target. Experiment 3 eliminated a potential confound of unequal numbers of trials for motor congruent and incongruent conditions in Experiment 1. Overall, these results indicated an integration between the meanings of the two subliminally perceived words in the prime. The difference between simultaneous and sequential presentations, and the reason why positive priming was not observed when the interval between the prime and the target was short were discussed in the context of unconscious semantic integration.

Semantic image segmentation, which becomes one of the key applications in image processing and computer vision domain, has been used in multiple domains such as medical area and intelligent transportation. Lots of benchmark datasets are released for researchers to verify their algorithms. Semantic segmentation has been studied for many years. Since the emergence of Deep Neural Network (DNN), segmentation has made a tremendous progress. In this paper, we divide semantic image segmentation methods into two categories: traditional and recent DNN method. Firstly, we briefly summarize the traditional method as well as datasets released for segmentation, then we comprehensively investigate recent methods based on DNN which are described in the eight aspects: fully convolutional network, up-sample ways, FCN joint with CRF methods, dilated convolution approaches, progresses in backbone network, pyramid methods, Multi-level feature and multi-stage method, supervised, weakly-supervised and unsupervised methods. Finally, a conclusion in this area is drawn.

The reference theory popular in 1970-1990s, which aimed at integrating various forms of semantic determination of lexical units of the language analysed from the syntax perspective, provides the starting point for the author. Quantification and definiteness/indefiniteness were treated in numerous publications from that period as varieties of the same semantic category, i.e. determination of the referential status of nominal groups. The author assumes that both categories demonstrate functional independence, therefore their exponents form opposites. Consequently, the quantification model of definiteness/indefiniteness, cultivated by some researchers, is not supported in the language material, being rather a strictly logical structure. At the same time, the author demonstrates that certain relations and dependencies occur between the two categories. Four aspects of such dependencies: selection, implication, collocation and derivation, are examined in the paper using Polish language materials and materials of other European languages.

The COVID-19 pandemic is impacting mental health, but it is not clear how people with different types of mental health problems were differentially impacted as the initial wave of cases hit. The aim of this study is to leverage natural language processing (NLP) with the goal of characterizing changes in 15 of the world's largest mental health support groups (eg, r/schizophrenia, r/SuicideWatch, r/Depression) found on the website Reddit, along with 11 non-mental health groups (eg, r/PersonalFinance, r/conspiracy) during the initial stage of the pandemic. We created and released the Reddit Mental Health Dataset including posts from 826,961 unique users from 2018 to 2020. Using regression, we analyzed trends from 90 text-derived features such as sentiment analysis, personal pronouns, and semantic categories. Using supervised machine learning, we classified posts into their respective support groups and interpreted important features to understand how different problems manifest in language. We applied unsupervised methods such as topic modeling and unsupervised clustering to uncover concerns throughout Reddit before and during the pandemic. We found that the r/HealthAnxiety forum showed spikes in posts about COVID-19 early on in January, approximately 2 months before other support groups started posting about the pandemic. There were many features that significantly increased during COVID-19 for specific groups including the categories \"economic stress,\" \"isolation,\" and \"home,\" while others such as \"motion\" significantly decreased. We found that support groups related to attention-deficit/hyperactivity disorder, eating disorders, and anxiety showed the most negative semantic change during the pandemic out of all mental health groups. Health anxiety emerged as a general theme across Reddit through independent supervised and unsupervised machine learning analyses. For instance, we provide evidence that the concerns of a diverse set of individuals are converging in this unique moment of history; we discovered that the more users posted about COVID-19, the more linguistically similar (less distant) the mental health support groups became to r/HealthAnxiety (ρ=-0.96, P<.001). Using unsupervised clustering, we found the suicidality and loneliness clusters more than doubled in the number of posts during the pandemic. Specifically, the support groups for borderline personality disorder and posttraumatic stress disorder became significantly associated with the suicidality cluster. Furthermore, clusters surrounding self-harm and entertainment emerged. By using a broad set of NLP techniques and analyzing a baseline of prepandemic posts, we uncovered patterns of how specific mental health problems manifest in language, identified at-risk users, and revealed the distribution of concerns across Reddit, which could help provide better resources to its millions of users. We then demonstrated that textual analysis is sensitive to uncover mental health complaints as they appear in real time, identifying vulnerable groups and alarming themes during COVID-19, and thus may have utility during the ongoing pandemic and other world-changing events such as elections and protests.

In this paper, a database of semantic features is presented. 104 nominal concepts from 13 semantic categories were described by young Czech school children. They were asked to respond to the question “what is it, what does it mean?” by listing different kinds of properties for concepts in writing. Their responses were broken down into semantic features and the database was prepared using a set of pre-established rules. The method of database design, with an emphasis on the way features were recorded, is described in detail within this article. The data were statistically analysed and interpreted and the results along with database usage methodologies are discussed. The goal of this research is to produce a complex database to be used in future research relating to semantic features and therefore it has been published online for use by the wider academic community. At present, databases have been published based on data gathered from adult English and Czech speakers; however participation in this study was limited specifically to young Czech-speaking children. Thus, this database is characteristically unique as it provides important insight into this specific age and language group’s conceptual knowledge. The research is inspired primarily by research papers concerning semantic feature production obtained from adult English speakers (McRae, de Sa, and Seidenberg, 1997; McRae, Cree, Seidenberg, and McNorgan, 2005; Vinson and Vigliocco, 2008).

Purpose: The present study elaborates on the linguistic highlights in poems by Seyed Hamidreza Burqa’i (1983) based on the linguistic model of Jeffrey Leach (1936-2014). Burqa’i’s ritual poems have a distinguished status compared to contemporary poets due to their prominent linguistic factors, including deviation, and regulatory enhancement. Methods: This study used an analytical-descriptive method and aimed at answering the following three research questions: 1) Which factors of highlighting are seen in Burqa’i's pomes? 2) Which elements is he most skilled at using? And 3) How effective is his linguistic highlighting for conveying the poet's emotions and thoughts? To answer these questions, the researchers have deployed Jeffrey Leach's linguistic model. Findings: This study showed that Burqa’i used both deviation and regulatory enhancement (balance) elements in his poetry. Semantic deviation and semantic regulatory enhancement in Burqa’i’s poetry were more than other categories of highlighting. Among semantic elements metaphors, kenning, and similes respectively have the higher use. In terms of regulatory enhancement elements, semantic similarity factors have a higher frequency. Conclusions: Therefore, it is argued that the factors of linguistic highlights, along with the eloquent and fluent language of the poet, indicate the poet's ability to use various linguistic capabilities and elevate his position.