Papers reviewed and determined not to be word norm studies. Use the flag icon to report errors or suggest re-inclusion.
16504 papers
The difference between children’s and adults’ speech consists in unusual lexemes or forms of familiar words as well as non-standard meanings of common words occurring in the former. The term error is not appropriate for such cases since these are legitimate elements of the child’s emerging language system. Despite their uniqueness, they do not hinder children’s communication with grown-ups. These units violating the linguistic norm are called innovations. The analysis of children’s innovations enables linguists to explore the nature of language rules and their hierarchical structure as well as to identify accidental gaps (or lacunae) in the language. The article presents a typology of children’s speech innovations from the standpoint of the language norm.
Abstract Chunks are multi-word sequences that constitute an important component of the mental lexicon. In second language (L2) acquisition, chunking is essential for attaining fluency and idiomaticity. In the present study, in order to examine whether chunks provide a processing advantage over non-chunks for L2 learners at different levels of proficiency, three groups (beginner, intermediate, and advanced) English-speaking learners of Chinese participated in an online acceptability judgment task and a familiarity rating task. Our results revealed that the participants in all three groups processed chunks faster and with fewer errors than they did non-chunks. It was also found that the observed processing advantage of chunks could not be explained by a familiarity effect alone, thus suggesting that L2 learners across the board store chunks as holistic units. The implications of chunk instruction in relation to input frequency and variability in L2 settings are also discussed.
Abstract This paper argues that Wittgenstein does not assimilate certainties to either linguistic norms or empirical propositions but assigns them to a liminal space between rule and experience. This liminal space is also brought into play in remarks written at the same time as those compiled in On Certainty, but attributed to different bodies of text ( Remarks on Colour, Last Writings on the Philosophy of Psychology ). The paper maintains that certainties express the agreement and constancy in judgements without which – as Wittgenstein contends in his Philosophical Investigations – rule-following would not be possible. It is shown that this intrinsic relation between rule-following and certainties can explain the liminal status of the latter.
The semantic variant of a primary progressive aphasia (svPPA) is characterized by progressive disruption of semantic knowledge. This study aimed to compare the semantic features of words produced during a narrative speech in svPPA and the logopenic variant of PPA (lvPPA) and to explore their neuroanatomical correlates. Six patients with svPPA and sixteen with lvPPA underwent narrative speech tasks. For all the content words, a semantic depth index (SDI) was determined based on the taxonomic structure of a large lexical database. Study participants underwent an MRI examination. Cortical thickness measures were extracted according to the Desikan atlas. Correlations were computed between SDI and the thickness of cortical regions. Mean SDI was lower for svPPA than for lvPPA. Correlation analyses showed a positive association between the SDI and the cortical thickness of the bilateral temporal pole, parahippocampal and entorhinal cortices, and left middle and superior temporal cortices. Disruption of semantic knowledge observed in svPPA leads to the production of generic terms in narrative speech, and the SDI may be useful for quantifying the level of semantic impairment. The measure was associated with the cortical thickness of brain regions associated with semantic memory.
We introduce a graph polynomial that distinguishes tree structures to represent dependency grammar and a measure based on the polynomial representation to quantify syntax similarity. The polynomial encodes accurate and comprehensive information about the dependency structure and dependency relations of words in a sentence. We apply the polynomial-based methods to analyze sentences in the Parallel Universal Dependencies treebanks. Specifically, we compare the syntax of sentences and their translations in different languages, and we perform a syntactic typology study of available languages in the Parallel Universal Dependencies treebanks. We also demonstrate and discuss the potential of the methods in measuring syntax diversity of corpora.
Objectives/research questions: We examined stative and eventive passive bilingual compound verbs (BCVs) in Spanish/English code-switching. Of particular interest to us was the availability of passivization in bilingual eventive passive hacer “do” constructions, purportedly banned in bilingual speech due to a universal syntactic restriction. Methodology: A total of 119 bilinguals from Northern Belize and 36 from Southwest United States completed a two-alternative forced-choice acceptability task and a language background questionnaire. Data and analysis: The analysis was conducted using Thurstone’s Law of Comparative Judgment. Conclusion: For stative passive BCVs, results revealed that Spanish/English bilinguals from both contexts gave the highest ratings to code-switched constructions without the light verb hacer. For eventive passive BCVs, however, Belize bilinguals gave preferential ratings to passive constructions with the light verb hacer. Conversely, US bilinguals rejected them. Notably, among Belize bilinguals, eventive passive BCVs that were rated as most acceptable were constructions with no gender agreement between the light verb and the feminine antecedent noun. Originality: This is the first cross-community analysis that investigates stative and eventive passive BCVs in Spanish/English code-switching. Implications: Our findings show that the light verb hacer is compatible with both stative and eventive passive BCVs. Crucially, context-specific linguistic norms and social factors rather than a universal syntactic restriction primarily determine the availability of passivization in eventive passive BCVs. Our theorizing of code-switching grammars, thus, necessitates careful consideration of invariant and variable production patterns that are profoundly shaped by historical and sociolinguistic conditions.
We created a Slovak language model in the Spacy library. When creating the model, we used pretraining and training in the Spacy library. During the pretraining, we used Fasttext vectors and the text we collected. We used data from Slovak Dependency Treebank during the training. We created a model with an accuracy of 86.249%. We also examined the effect of pretraining on the model. All achieved results are described in the final part.
The main metadata projects for linguistic (language) resources developed over the past 20 years are described. These include the IMDI initiative, the OLAC metadata system. the META-SHARE meta-model, the International Standard Number of Language resources, the evaluation map of language resources, and the CLARIN component metadata model. The content of the ISO metadata standard is described. Projects for creating dictionaries, ontologies, and lexical databases for metadata of language resources are described.
Anxiety disorders are characterized by cognitive dysfunctions which contribute to the patient's profound disabilities. The threat of shock paradigm represents a validated psychopathological model of anxiety to measure the impact of anxiety on cognitive processes. We have developed an online version of the threat of scream paradigm (ToSP) to investigate the impact of experimental anxiety on recognition memory. Two animated passive walkthrough videos (either under threat of scream or safety conditions) were shown to healthy participants. Recognition memory, primacy vs. recency effects, and subjective estimations of the length of encoding sessions were assessed. Subjective anxiety, stress, and emotional arousal ratings indicated that experimental anxiety could successfully be induced (Safe-Threat) or reversed (Threat-Safe) between the two passive walkthrough sessions. Participants exposed to distress screams showed impaired retrieval of complex information that has been presented in an animated environment. In the threat condition, participants failed to recognize details related to the persons encountered, their spatial locations, as well as information about the temporal order and sequence of encounters. Participant groups, which received a threat announcement prior to the first walkthrough session (Threat-Threat vs. Safety-Safety and Threat-Safety vs. Safety-Threat) showed poorer recognition memory as compared to the groups that received a safety announcement (P = 0.0468 and P = 0.0426, respectively; Mann-Whitney U test, Cohen's d = 0.5071; effect size r = 0.2458). In conclusion, experimental anxiety induced by the online version of the ToSP leads to compromised recognition memory for complex multi-dimensional information. Our results indicate that cognitive functions of vulnerable populations (with limited mobility) can be evaluated online by means of the ToSP.
Sequence labeling, in which a class or label is assigned to each token in a given input order, is a fundamental task in natural language processing. Many advanced neural network architectures have recently been proposed to solve the sequential labeling problem affecting this task. By contrast, only a few approaches have been proposed to address the sequential ensemble problem. In this paper, we resolve the sequential ensemble problem by applying the sequential alignment method in a proposed ensemble framework. Specifically, we propose a simple but efficient ensemble candidate generation framework with which multiple heterogeneous systems can easily be prepared from a single neural sequence labeling network. To evaluate the proposed framework, experiments were conducted with part-of-speech (POS) tagging and dependency label prediction problems. The results indicate that the proposed framework achieved accuracy values that were higher by 0.19 and 0.33 than those achieved by the hard-voting method on the Penn-treebank POS-tagged and Universal dependency-tagged datasets, respectively.
Persistent homology is an important methodology in topological data analysis which adapts theory from algebraic topology to data settings. Computing persistent homology produces persistence diagrams, which have been successfully used in diverse domains. Despite its widespread use, persistent homology is simply impossible to compute when a dataset is very large. We study a statistical approach to the problem of computing persistent homology for massive datasets using a multiple subsampling framework and extend it to three summaries of persistent homology: Hölder continuous vectorizations of persistence diagrams; the alternative representation as persistence measures; and standard persistence diagrams. Specifically, we derive finite sample convergence rates for empirical means for persistent homology and practical guidance on interpreting and tuning parameters. We validate our approach through extensive experiments on both synthetic and real-world data. We demonstrate the performance of multiple subsampling in a permutation test to analyze the topological structure of Poincaré embeddings of large lexical databases.
Fully data-driven, deep learning-based models are usually designed as language-independent and have been shown to be successful for many natural language processing tasks. However, when the studied language is not high-resource and the amount of training data is insufficient, these models can benefit from the integration of natural language grammar-based information.We propose two approaches to dependency parsing especially for languages with restricted amount of training data. Our first approach combines a state-of-the-art deep learning-based parser with a rule-based approach and the second one incorporates morphological information into the parser. In the rule-based approach, the parsing decisions made by the rules are encoded and concatenated with the vector representations of the input words as additional information to the deep network. The morphology-based approach proposes different methods to include the morphological structure of words into the parser network. Experiments are conducted on three different Turkish treebanks and the results suggest that integration of explicit knowledge about the target language to a neural parser through a rule-based parsing system and morphological analysis leads to more accurate annotations and hence, increases the parsing performance in terms of attachment scores. The proposed methods are developed for Turkish, but can be adapted to other languages as well.
The term “transgression” is traditionally associated with the infringement of what is prescribed. However, a closer look at its nature suggests that it is an integral part of the norm, as well as a starting point for innovation, in this case linguistic. The study focuses on the linguistic landscape (LL) of Spain, where five official languages share regional official status with Castilian Spanish. Further, these languages coexist in the LL with immigrant ones and English as an international language. In this environment, the article explores how linguistic transgression is reflected in the LL and what motivations underlie such non-normative uses. Given the spatial and grammatical limitations of the texts in the LL, the study focuses on aspects of code preference and orthography. To this end, we work on photographs taken in different Spanish regions which reflect the range of transgressive linguistic practices present in the public space. The evidence gathered allows us to suggest the grouping of these techniques under the categories of code (or variant) choice/elimination, exoticisation, re-representation and re-signification. The subsequent analysis presents linguistic transgression in LL as a voluntary, motivated and intentional social act that reflects identity, socio-cultural, but also commercial motivations. These motivations lead street-text authors to force the linguistic norm in their texts in order to claim their identity, show their solidarity with ideologies, resist linguistic policies or seek identification with their audience’s sensitivities for trade purposes.
RST-style discourse parsing plays a vital role in many NLP tasks, revealing the underlying semantic/pragmatic structure of potentially complex and diverse documents. Despite its importance, one of the most prevailing limitations in modern day discourse parsing is the lack of large-scale datasets. To overcome the data sparsity issue, distantly supervised approaches from tasks like sentiment analysis and summarization have been recently proposed. Here, we extend this line of research by exploiting distant supervision from topic segmentation, which can arguably provide a strong and oftentimes complementary signal for high-level discourse structures. Experiments on two human-annotated discourse treebanks confirm that our proposal generates accurate tree structures on sentence and paragraph level, consistently outperforming previous distantly supervised models on the sentence-to-document task and occasionally reaching even higher scores on the sentence-to-paragraph level.
Objective: To assess the impact of nasal deviation on the perception of the maxillary dental centreline position as judged by orthodontists, dentists and laypersons. Design: Cross-sectional study. Setting: Barts and the London School of Medicine and Dentistry, Queen Mary University of London, UK. Participants: Three groups of raters comprising 30 orthodontists, 30 dentists and 30 laypersons. Methods: A frontal photograph of a smiling white woman was captured and digitally manipulated with varying degrees of nasal deviation and dental centreline (DC) position in increments of 1.5 mm and 3 mm to the right and left. Three rater groups assessed the attractiveness of images using a visual analogue scale (VAS). Multiple regression analysis was undertaken, and images were compared using the Tukey HSD method. Results: Using a mixed linear model, the intraclass correlation coefficient (ICC) was estimated in the range of 69%–86%, indicating good inter-rater reliability. The interaction between image rating and nasal position ( P < 0.001), DC position ( P < 0.001) and the relationship between nose and DC position ( P < 0.001) were found to be statistically significant with symmetrical upper midline and nasal tip position, both considered to be most aesthetically pleasing. Image rating was not influenced by rater group type ( P = 0.995), age ( P = 0.983) or sex ( P = 0.476). Conclusion: There was a preference for a central and coincident nose and maxillary DC position uniformly across the rater groups. Deviations of the nose, DC and their interactions negatively impacted on perceived smile aesthetics with increasing extent and opposing direction of deviations rated progressively more unaesthetic. No differences were observed between orthodontists, general dental practitioners and lay people with respect to perceived impact on smile aesthetics.
Sentiment Analysis is very important for many applications and tasks. It became very useful tool in market trends predictions, social media monitoring, sufficient predictor for election and products. Many approaches, by many researchers, were used for this task starting from rules-based to machine learning and deep learning approaches on different types of data. All of these methods suffer from a lack of accuracy as a result of not achieving the correct understanding of the texts. In this work, an approach for extracting and using the exact meaning of the word within the context is used as part of initialization data representation. Three well-known classifiers were used (Narve Bayes, Support Vector Machine, and Maximum entropy) for testing this approach on two languages (Arabic and English). The system was evaluated using precision, recall, f-measure, and accuracy on two different datasets (Stanford Sentiment Treebank for English language and LABR-V2 for Arabic language). The results showed that the exact meaning of the words is very useful and the accuracy was increased by range 7.3% for best cases and increased by range of 4% in worst cases. The contributions of this work can be summarized by: (i) using exact meaning of the word for two languages, (ii) using new approach for adding synonyms and antonyms for exact meaning of the word in context, and (iii) better accuracy was received compared with the baseline classifiers.
<span lang="EN-US">Language identification (LI) in textual documents is the process of automatically detecting the language contained in a document based on its content. The present language identification techniques presume that a document contains text in one of the fixed set of languages. However, this presumption is incorrect when dealing with multilingual document which includes content in more than one possible language. Due to the unavailability of standard corpora for Hindi-English mixed lingual language processing tasks, we propose the language lexicons, a novel kind of lexical database that augments several bilingual language processing tasks. These lexicons are built by learning classifiers over English and transliterated Hindi vocabulary. The designed lexicons possess condensed quantitative characteristics which reflect their linguistic strength in respect of Hindi and English language. On evaluating the lexicons, it is observed that words of the same language tend to cluster together and are separable over language classes. On comparing the classifier performance with existing works, the proposed lexicon models exhibit the better performance.</span>
Phrase-level sentiment intensity prediction is difficult due to the inclusion of linguistic modifiers (e.g., negators, degree adverbs, and modals) potentially resulting in an intensity shift or polarity reversal for the modified words. This study develops a graph-based Chinese parser based on the deep biaffine attention model to obtain dependency structures and relations. These obtained dependency features are then used in our proposed Weighted-sum Tree GRU network to predict phrase-level sentiment intensity in the valence-arousal dimensions. Dependency parsing results using the Sinica Treebank indicate that our graph-based model outperforms transition-based methods such as MLP and stack-LSTM with identical findings for English dependency parsing. Experimental results on the Chinese EmoBank indicate that our Weighted-sum Tree GRU network model outperforms other transformer-based neural networks such as BERT, ALBERT, XLNET and ELECTRA, reflecting the effectiveness of linguistic dependencies in phrase-level sentiment intensity predication tasks. In addition, our proposed model requires fewer parameters and less inference time for quantitative analysis, making the proposed model is relatively lightweight and efficient.
Background/Aims: Exposure toward positive emotional cues with – and without – reproductive significance plays a crucial role in daily life and regarding well-being as well as mental health. While possible adverse effects of oral contraceptive (OC) use on female mental and sexual health are widely discussed, neural processing of positive emotional stimuli has not been systematically investigated in association with OC use. Considering reported effects on mood, well-being and sexual function, and proposed associations with depression, it was hypothesized that OC users showed reduced neural reactivity toward positive and erotic emotional stimuli during early as well as later stages of emotional processing and also rated these stimuli as less pleasant and less arousing compared to naturally cycling (NC) women. Method: Sixty-two female subjects (29 NC and 33 OC) were assessed at three time points across the natural menstrual cycle and corresponding time points of the OC regimen. Early (early posterior negativity, EPN) and late (late positive potential, LPP) event-related potentials in reaction to positive, erotic and neutral stimuli were collected during an Emotional Picture Stroop Paradigm (EPSP). At each appointment, subjects provided saliva samples for analysis of gonadal steroid concentration. Valence and arousal ratings were collected at the last appointment. Results: Oral contraceptive users had significantly lower endogenous estradiol and progesterone concentrations compared to NC women. No significant group differences in either subjective stimulus evaluations or neural reactivity toward positive and erotic emotional stimuli were observed. For the OC group, LPP amplitudes in reaction to erotic vs. neutral pictures differed significantly between measurement times across the OC regimen. Discussion: In this study, no evidence regarding alterations of neural reactivity toward positive and erotic stimuli in OC users compared to NC was found. Possible confounding factors and lines for future research are elaborated and discussed.
In recent years, cross-lingual transfer learning has been gaining positive trends across NLP tasks. This research aims to develop a dependency parser for Indonesian using cross-lingual transfer learning. The dependency parser uses a Transformer as the encoder layer and a deep biaffine attention decoder as the decoder layer. The model is trained using a transfer learning approach from a source language to our target language with fine-tuning. We choose four languages as the source domain for comparison: French, Italian, Slovenian, and English. Our proposed approach is able to improve the performance of the dependency parser model for Indonesian as the target domain on both same-domain and cross-domain testing. Compared to the baseline model, our best model increases UAS up to 4.31% and LAS up to 4.46%. Among the chosen source languages of dependency treebanks, French and Italian that are selected based on LangRank output perform better than other languages selected based on other criteria. French, which has the highest rank from LangRank, performs the best on cross-lingual transfer learning for the dependency parser model.
We construct a Chinese Economic Event Treebank (CEETB), focusing on revealing economic and finance events and their relations. Investigating economic event relations will benefit academic research and practice in not just economics but many other scientific areas. The characteristics of economic-related texts (e.g., abundant longer enterprises names and terms) and the Chinese language speciality (e.g., component ellipsis in long sentences) have resulted in challenges in the event relation extraction task. Existing Chinese corpora containing economic event relations mainly focused on finance areas (e.g., the equity market) and only covered a few event types. To support research that may involve economic text analysis in Chinese, our CEETB is constructed following a carefully designed process. First, based on practical and research requirements, we summarize nine different types of event relations and four types of component ellipses in economic texts. Then, an excellent annotation scheme is presented to hyalinize the model, strategy, and process in annotation, followed by statistical analysis and quality evaluation for the CEETB corpus. Finally, to demonstrate the strengths of the constructed corpus in practical applications, we conduct experiments on five SOTA models for event relation extraction.
PURPOSE: The graphic symbol is the foundation of augmentative and alternative communication (AAC) for many preliterate individuals; however, research has focused primarily on static graphic symbol sequences despite mainstream commercial technologies such as animation. The goal of this study was to compare static and animated symbol sequences across receptive communication outcome measures and psycholinguistic features (e.g., word frequency). METHOD: A counterbalanced, 2 × 2 × 2 mixed design was used to investigate the effects of symbol format (animated and static), first condition (animated or static), and first experimental task (identification or labeling) on identification accuracy and labeling accuracy of graphic symbol sequences (five symbols) in 24 children with typical development ages 7 and 8 years old. Additionally, three 2 × 2 repeated-measures analyses of variance were conducted using symbol format (animated and static) and (a) word frequency (low, high), (b) imageability (low, high), and (c) concreteness (low, high). RESULTS: In addition to superior identification and labeling accuracy of animated sequences, a significant interaction between symbol format and the first condition was observed for both experimental tasks. When the animation format was the first condition, then the children's performance improved in the subsequent static condition. Finally, word frequency, imageability, and concreteness ratings for all verbs and prepositions had significant effects on labeling accuracy of verbs and prepositions. Significant interactions between symbol format and psycholinguistic features were also found. For example, highly imageable, animated verbs were labeled with greater accuracy when compared with all other variables. CONCLUSIONS: Animation technology appears to alleviate some of the burden associated with word- and sentence-level outcomes in children with typical development. Moreover, animation appears to reduce the effects of psycholinguistic features such as word frequency and imageability by increasing the transparency of the symbol. Given the increase in research in this area, speech-language pathologists may consider adopting animated graphic symbols on a case-by-case basis as a tool to augment the learning of word classes in which movement is integral to comprehension.
In this paper, we investigate to which extent contextual neural language models (LMs) implicitly learn syntactic structure. More concretely, we focus on constituent structure as represented in the Penn Treebank (PTB). Using standard probing techniques based on diagnostic classifiers, we assess the accuracy of representing constituents of different categories within the neuron activations of a LM such as RoBERTa. In order to make sure that our probe focuses on syntactic knowledge and not on implicit semantic generalizations, we also experiment on a PTB version that is obtained by randomly replacing constituents with each other while keeping syntactic structure, i.e., a semantically ill-formed but syntactically well-formed version of the PTB. We find that 4 pretrained transfomer LMs obtain high performance on our probing tasks even on manipulated data, suggesting that semantic and syntactic knowledge in their representations can be separated and that constituency information is in fact learned by the LM. Moreover, we show that a complete constituency tree can be linearly separated from LM representations.
The article considers the role of tax administration digitalization bodies in the context of current challenges and threats caused by the pandemic in the world and war in Ukraine. The importance of developing the electronic interaction channels between the State Tax Service of Ukraine, tax administrations of OECD countries and taxpayers in the context of Covid-19 and war action is outlined. The focus is on expanding the list of electronic services (for business, private entrepreneurs, IT services), which will increase opportunities for the implementation of the principle of convenience in fulfilling the tax obligation to the state. Continuous development and improvement of services provided by state tax authorities increase the image rating of state institutions, in particular, carry out electronic taxation, which allows: automate internal tax functions; to build electronic information interaction between taxpayers and state tax authorities in the field of taxation; to form effective online communication and ensure fast and secure data exchange between government agencies in the field of taxation; to ensure effective international cooperation in electronic format. The directions for the tax authorities digitalization strategy change for the purpose of effective taxes and fees administration process are offered: granting equal access of citizens, business representatives, tax administrators to digital technologies and new opportunities (to reduce digital gaps); advanced training of personnel for the full development of digitalization of the State Tax Service; increasing the level of automation and digitalization of public services together with the motivation of government agencies.
Liking and pleasantness are common concepts in psychological emotion theories and in everyday language related to emotions. Despite obvious similarities between the terms, several empirical and theoretical notions support the idea that pleasantness and liking are cognitively different phenomena, becoming most evident in the context of emotion regulation and art enjoyment. In this study it was investigated whether liking and pleasantness indicate behaviourally measurable differences, not only in the long timespan of emotion regulation, but already within the initial affective responses to visual and auditory stimuli. A cross-modal affective priming protocol was used to assess whether there is a behavioural difference in the response time when providing an affective rating to a liking or pleasantness task. It was hypothesized that the pleasantness task would be faster as it is known to rely on rapid feature detection. Furthermore, an affective priming effect was expected to take place across the sensory modalities and the presentative and non-presentative stimuli. A linear mixed effect analysis indicated a significant priming effect as well as an interaction effect between the auditory and visual sensory modalities and the affective rating tasks of liking and pleasantness: While liking was rated fastest across modalities, it was significantly faster in vision compared to audition. No significant modality dependent differences between the pleasantness ratings were detected. The results demonstrate that liking and pleasantness rating scales refer to separate processes already within the short time scale of one to two seconds. Furthermore, the affective priming effect indicates that an affective information transfer takes place across modalities and the types of stimuli applied. Unlike hypothesized, liking rating took place faster across the modalities. This is interpreted to support emotion theoretical notions where liking and disliking are crucial properties of emotion perception and homeostatic self-referential information, possibly overriding pleasantness-related feature analysis. Conclusively, the findings provide empirical evidence for a conceptual delineation of common affective processes.
University level second language (L2) creative writing courses are widespread around the world. However, little empirical research has been done to document teachers and students’ experiences with courses whose main focus is to develop students’ creativity. In response, this case study was conducted to explore how an English native-speaking (NS) creative writing teacher and non-English majors at a Taiwanese public university evaluated an elective L2 creative writing course through the investigation of the teachers’ language ideology, assessment strategies, and learning activities. Data included the course syllabus, the teacher’s and the students’ reflection reports, and the students’ L2 creative writing portfolios. Through qualitative content analysis, it was found that this creative writing course did focus on teaching creative writing skills. Despite the NS identity of the teacher, in resonance with the spirit of the world Englishes pedagogical paradigm, the notion of conforming to NS linguistic norms and accuracy was not introduced to students. Lecturing, sharing, and workshopping were found to be effective learning and assessment activities, while students experienced the most difficulty with keeping a creative writing journal. We argue that creativity should and can be pursued as the main objective in English creative writing courses even by NS teachers. Lastly, implications for research and pedagogy were considered.
BACKGROUND: Fluorescence angiography in colorectal surgery is a technique that may lead to lower anastomotic leak rates. However, the interpretation of the fluorescent signal is not standardised and there is a paucity of data regarding interobserver agreement. The aim of this study is to assess interobserver variability in selection of the transection point during fluorescence angiography before anastomosis. METHODS: An online survey with still images of fluorescence angiography was distributed through colorectal surgery channels containing images from 13 patients where several areas for transection were displayed to be chosen by raters. Agreement was assessed overall and between pre-planned rater cohorts (experts vs non-experts; trainees vs consultants; colorectal specialists vs non colorectal specialists), using Fleiss' kappa statistic. RESULTS: 101 raters had complete image ratings. No significant difference was found between raters when choosing a point of optimal bowel transection based on fluorescence angiography still images. There was no difference between pre-planned cohorts analysed (experts vs non-experts; trainees vs consultants; colorectal specialists vs non colorectal specialists). Agreement between these cohorts was poor (<.26). CONCLUSION: Whilst there is no learning curve for the technical adoption of FA, understanding the fluorescent signal characteristics is key to successful use. We found significant variation exists in interpretation of static fluorescence angiography data. Further efforts should be employed to standardise fluorescence angiography assessment.
I here argue that Corpus Linguistic (CL) investigations can show evidence that renewable energy has become increasingly important in the last 20 years as shown in the Brown Family corpus, a linguistic database of both British and American English whose diachronic data span from 1930s to 2000s. I use collocation analysis, a well-known CL technique, to discover collocates (accompanying words) that significantly associate with energy. The significance is statistically calculated using Log Likelihood (LL). No content word is found up to 1930s data. Some content words related to the categorization of energy are found in 1960s data. In 1990s data renewable is within top-3. In 2006 data, renewable is found to rank first, showing a very strong significance with energy.
Abstract Formal constraints on crossing dependencies have played a large role in research on the formal complexity of natural language grammars and parsing. Here we ask whether the apparent evidence for constraints on crossing dependencies in treebanks might arise because of independent constraints on trees, such as low arity and dependency length minimization. We address this question using two sets of experiments. In Experiment 1, we compare the distribution of formal properties of crossing dependencies, such as gap degree, between real trees and baseline trees matched for rate of crossing dependencies and various other properties. In Experiment 2, we model whether two dependencies cross, given certain psycholinguistic properties of the dependencies. We find surprisingly weak evidence for constraints originating from the mild context-sensitivity literature (gap degree and well-nestedness) beyond what can be explained by constraints on rate of crossing dependencies, topological properties of the trees, and dependency length. However, measures that have emerged from the parsing literature (e.g., edge degree, end-point crossings, and heads’ depth difference) differ strongly between real and random trees. Modeling results show that cognitive metrics relating to information locality and working-memory limitations affect whether two dependencies cross or not, but they do not fully explain the distribution of crossing dependencies in natural languages. Together these results suggest that crossing constraints are better characterized by processing pressures than by mildly context-sensitive constraints.
The concept of norm, developed by Eugeniu Coșeriu in 1952 as part of the trichotomy system, norm, speech, and later related to the notion of language type, was defined as a system of obligatory, common, normal actualizations and traditions of the language, which are not necessarily functional, and which vary from one community of speakers to another. In the view of the Tübingen linguist, within the same linguistic community and the same functional system more types of norms can be identified: the norm of the literary language, the norm of the vernacular, the norm of familiar language, the norm of formal language, the norm of vulgar language, etc. In what concerns norm, the Romanian linguist also makes another important distinction, namely that between social norm and individual norm. The present paper deal s with linguistic norm, as it was theorized by Eugeniu Coșeriu, and then focuses on the norm of the Romanian language in particular. Considering the “architecture” of historical language, i.e. the internal differences of the language: diatopic, diastratic and diaphasic also described and analysed by Eugeniu Coșeriu it will illustrate the division of the norm within the Romanian language.
Obo-Manobo, one of the tribes in Mindanao Philippines, inhabiting some municipalities in the Province of North Cotabato, has ethnic or indigenous language which is not sufficiently analyzed and documented. To account varieties in language and to come up with the glossary of terms, the researcher used the Metalinguistic knowledge from the interviews, focus group discussions, observations and translation of the Swadesh Word List and from the lexical databases such as the word lists of Obo-Manobo accounted by the SIL. The study revealed distinct variations in the Obo-Manobo language from the culture bearers of Kidapawan City, Magpet and President Roxas in the Province of North Cotabato. Obo-Manobo has lexical varieties in the way they name and describe things, person, places, events and concepts (noun and adjectives); use the main verb, prepositions and conjunctions. These are all accounted in the glossary of terms – the output of this study.
On the CIFAR-10 (Canadian Institute for Advanced Research-10), ImageNet, and Penn Treebank datasets, Neural Architecture Search (NAS) algorithms obtained better results by computerizing the process of architectural design on the CIFAR-10, ImageNet, and Penn Treebank datasets. Even though the search time has been simplified, search algorithms count on performance prediction or controllers. When used on a new task with a fresh dataset, this may necessitate optimal structuring. The problem of architecture search is not solved because this is done by hand. Using continuous relaxation and gradient descent methods, Differentiable Architecture Search (DARTS) [1] avoids this issue. There are, however, plenty of intriguing methods to make DARTS better. In this paper, we first split the DARTS’s supernet into three (03) sub-supernets and applied neural message passing so that each node in the graph has information from other nodes. The three (03) sub-supernets by using gradient descent to find the best graph represent the whole search space. By adding parameter sharing and transfer learning, our method enhances the final accuracy of one-shot-based DARTS systems consistently. On CIFAR-10, it achieves 98.25% test set accuracy, according to the results.
In Japan, the increase in the number of elderly people with dementia due to the aging of society has become a major social problem. This study designed a recreation using an animal robot to increase the amount of communication among elderly people with dementia. Then, we conducted the proposal recreation at a nursing home for elderly people with dementia to measure the effectiveness of the proposed recreation. The effectiveness of the recreation was measured not only by increasing the amount of communication, but also by evaluating the emotions of the elderly people with dementia while they were participating in the recreation. Lawton’s Philadelphia Geriatric Center Affect Rating Scale ("ARS") was used in the evaluation of emotions. The results confirmed that recreation using the animal robot increased the amount of communication among elderly people with dementia, and increased positive emotions among elderly people with dementia.
The beginnings of words are, in some informal sense, special. This intuition is widely shared, for example when playing word games. Less apparent is whether the intuition is substantiated empirically and what the underlying organizational principle(s) might be. Here we answer this seemingly simple question in a quantitatively clear way. Based on arguments about the interplay between lexical storage and speech processing, we examine whether the distribution of information among different speech sounds of words is governed by a critical computational unit for online speech perception and production: syllables. By analyzing lexical databases of twelve languages, we demonstrate that there is a compelling asymmetry between syllable beginnings (onsets) versus ends (codas) in their involvement in distinguishing words stored in the lexicon. In particular, we show that the functional advantage of syllable onset reflects an asymmetrical distribution of lexical informativeness within the syllable unit, but not an effect of a global decay of informativeness from the beginning to the end of a word. The converging finding across languages from a wide range of typological families supports the conjecture that the syllable unit, while being a critical primitive for both speech perception and production, is also a key organizational constraint for lexical storage. Specifically, speech sounds at the beginning of individual syllables of words are granted a privileged role in representing the sound forms for words in the lexicon.
Recurrent Neural Networks (RNNs) have become important tools for tasks such as speech recognition, text generation, or natural language processing. However, their inference may involve up to billions of operations and their large number of parameters leads to large storage size and runtime memory usage. These reasons impede the adoption of these models in real-time, on-the-edge applications. Field-Programmable Gate Arrays (FPGAs) and Application-Specific Integrated Circuits (ASICs) have emerged as promising solutions for the hardware acceleration of these algorithms, thanks to their degree of customization of compute data paths and memory subsystems, which makes them take the maximum advantage from compression techniques for what concerns area, timing, and power consumption. In contrast to the extensive study in compression and quantization for plain feed forward neural networks in the literature, little attention has been paid to reducing the computational resource requirements of RNNs. This work proposes a new effective methodology for the post-training quantization of RNNs. In particular, we focus on the quantization of Long Short-Term Memory (LSTM) RNNs and Gated Recurrent Unit (GRU) RNNs. The proposed quantization strategy is meant to be a detailed guideline toward the design of custom hardware accelerators for LSTM/GRU-based algorithms to be implemented on FPGA or ASIC devices using fixed-point arithmetic only. We applied our methods to LSTM/GRU models pretrained on the IMDb sentiment classification dataset and Penn TreeBank language modelling dataset, thus comparing each quantized model to its floating-point counterpart. The results show the possibility to achieve up to 90% memory footprint reduction in both cases, obtaining less than 1% loss in accuracy and even a slight improvement in the Perplexity per word metric, respectively. The results are presented showing the various trade-offs between memory footprint reduction and accuracy changes, demonstrating the benefits of the proposed methodology even in comparison with other works from the literature.
Recently, AWD-LSTM (ASGD Weight-Dropped LSTM) has achieved good result in the language model, and many AWD-LSTM based models have obtained state-of-the-art perplexities. However, in fact, large-scale neural language models have been shown to be prone to overfitting. In AWD-LSTM original paper, the author decided to adopt the way of retraining calling finetune to get a better result. In this paper, we present a simple yet effective parameter rollback mechanism for neural language models. And we introduce the parameter rollback averaged stochastic gradient descent (PR-ASGD), wherein the parameter “step” in ASGD will decrease according to a certain probability. Using this strategy, we achieve better word level perplexities on Penn Treebank: 56.26 based on AWD-LSTM model and 53.57 based on AWD-LSTM-MoS (AWD-LSTM Mixture of Softmaxes) model.
BACKGROUND AND AIMS: Poor quality of life is a main complaint among individuals with irritable bowel syndrome (IBS). Self-rated health (SRH) is a powerful predictor of clinical outcomes, and also reflects psychological and social aspects of life and an overall sense of well-being. This population-based twin study evaluates how IBS affects ratings of physical and mental health, and influences perceptions of hindrance of daily activity by physical or mental health. Further, we examine how IBS is related to these SRH measures. METHODS: The sample included 5288 Norwegian twins aged 40-80, of whom 575 (10.9%) suffer from IBS. Hierarchical regressions were used to estimate the impact of IBS on perceptions of health, before and after accounting for other chronic physical and mental health conditions. Two dimensions of SRH, physical and mental, and two aspects of functional limitations, the extent to which physical or mental health interferes with daily activities, were included as outcomes in separate models. Co-twin control analyses were used to explore whether the relationships between IBS and the four measures of SRH are causal, or due to shared genetic or shared environment effects. RESULTS: IBS was an independent predictor of poor self-rated physical health (OR = 1.82 [1.41; 2.33]), the size of this effect was comparable to that predicted by chronic somatic conditions. However, in contrast to somatic diseases, IBS was associated with the perception that poorer ratings of mental health (OR = 1.45 [1.02; 2.06]), but not physical health (OR = 1.23 [0.96; 1.58]), interfered with daily activity. The co-twin control analyses suggest that causal mechanisms best explain the relationships between IBS with self-rated physical health and with hindrance of daily activities. In contrast, the relationship between IBS and self-rated mental health was consistent with shared genetic effects. CONCLUSION: IBS is predictive of poor self-rated physical health. The relationship between IBS and self-rated mental health is best explained by shared genetic effects which might partially explain why mental health interferes with daily activity to a larger degree among those with IBS.
Prior experience represents a prerequisite for memory consolidation across various memory systems. In the context of olfaction, sleep was found to enhance the consolidation of odors in adults but not in typically developing children (TDC), likely due to differences in pre-experience. Interestingly, unmedicated children with attention deficit hyperactivity disorder (ADHD), a neurodevelopmental condition related to dopamine dysfunction, showed lower perceptive thresholds for odors, potentially allowing for more odor experience compared to TDC. We investigated sleep-associated odor memory consolidation in ADHD. Twenty-eight children with ADHD and thirty age-matched TDC participated in an incidental odor recognition task. For the sleep groups (ADHD: n = 14, TDC: n = 15), the encoding of 10 target odorants took place in the evening, and the retention of odorants was tested with 10 target odorants and 10 distractor odorants the next morning. In the wake groups (ADHD: n = 14, TDC: n = 15), the time schedule was reversed. Odor memory consolidation was superior in the ADHD sleep group compared to the TDC sleep and the ADHD wake groups. Intensity and familiarity ratings during encoding were substantially higher in ADHD compared to TDC. Sleep-associated odor memory consolidation in ADHD is superior to TDC. Abundant pre-experience due to lower perceptive thresholds is suggested as a possible explanation. Olfaction might serve as a biomarker in ADHD.
Perception of passing time can be distorted1. Emotional experiences, particularly arousal, can contract or expand experienced duration, via its interactions with attentional and sensory processing mechanisms2,3. Current models suggest that perceived duration is constructed from accumulation processes4,5 and is encoded from temporally evolving neural dynamics6,7. Yet, all neural dynamics and information processing ensue at the backdrop of continuous interoceptive signals originating from within the body. Indeed, phasic fluctuations within the cardiac cycle impact neural and information processing8-15. Here, we show that these momentary cardiac fluctuations distort experienced time, and that their effect interacts with subjectively experienced arousal. In a temporal bisection task, durations (200 - 400 ms) of an emotionally neutral visual shape or auditory tone (Experiment 1) or of an image displaying happy or fearful facial expressions (Experiment 2) were categorised as short or long16. Across both experiments, stimulus presentation was time-locked to systole, when the heart contracts and baroreceptors fire signals to the brain, and to diastole, when the heart relaxes, and baroreceptors are quiescent. When participants judged the duration of emotionally neural stimuli (Experiment 1), systole led to temporal contraction, while diastole led to temporal expansion. Such cardiac-led distortions were further modulated by the arousal ratings of the perceived facial expressions (Experiment 2). At low arousal, systole contracted while diastole expanded time, but as arousal increased, this cardiac-led time distortion disappeared, shifting duration perception towards contraction. Thus, experienced time contracts and expands within each heartbeat – a balance that is disrupted under heightened arousal.
When inferring emotions, humans rely on a number of cues, including not only facial expressions, body posture, but also expressor-external, contextual information. The goal of the present study was to compare the impact of such contextual information on emotion processing in humans and two deep neural network (DNN) models. We used results from a human experiment in which two types of pictures were rated for valence and arousal: the first type depicted people expressing an emotion in a social context including other people; the second was a context-reduced version in which all information except for the target expressor was blurred out. The resulting human ratings of valence and arousal were systematically decreased in the context-reduced version, highlighting the importance of context. We then compared human ratings with those of two DNN models (one trained on face images only, and the other trained also on contextual information). Analyses of both categorical and the valence/arousal ratings showed that although there were some superficial similarities, both models failed to capture human rating patterns both in context-rich and context-reduced conditions. Our study emphasizes the importance of a more holistic, multi-modal training regime with richer human data to build better emotion-understanding systems in the area of affective computing.
Anecdotal reports indicate more severe psychological distress following technological catastrophes in comparison to natural disasters. Previous research also suggests a more negative evaluation of the outcomes of disasters if they are manmade. On the other hand, evolutionary neuroscience shows differential neural processing of ancient and modern threats. Building upon this literature, we probed valence and arousal ratings of stimuli depicting natural and technological disasters in several standardized affective stimuli datasets used in neuroscience and psychological research. Our results show that while technological disasters are rated as slightly less arousing than natural disasters they are rated as significantly more unpleasant. The evolutionary age of disasters plays an important role in emotional experiences invoked by these threats which might affect our evaluations of catastrophes. We discuss how evolutionary psychology might explain our findings and help us to better understand the biological and learned roots of our biases in risk perception.
Vocal and facial cues typically co-occur in natural settings, and multisensory processing of voice and face relies on their synchronous presentation. Psychological research has examined various facial and vocal cues to attractiveness as well as to judgements of sexual dimorphism, health, and age. However, few studies have investigated the interaction of vocal and facial cues in attractiveness judgments under naturalistic conditions using dynamic, ecologically valid stimuli. Here, we used short videos or audio tracks of females speaking full sentences and used a manipulation of voice pitch to investigate cross-modal interactions of voice pitch on facial attractiveness and related ratings. Male participants had to rate attractiveness, femininity, age, and health of synchronized audio-video recordings or voices only, with either original or modified voice pitch. We expected audio stimuli with increased voice pitch to be rated as more attractive, more feminine, healthier, and younger. If auditory judgements cross-modally influence judgements of facial attributes, we additionally expected the voice pitch manipulation to affect ratings of audiovisual stimulus material. We tested 106 male participants in a within-subject design in two sessions. Analyses revealed that voice recordings with increased voice pitch were perceived to be more feminine and younger, but not more attractive or healthier. When coupled with video recordings, increased pitch lowered perceived age of faces, but did not significantly influence perceived attractiveness, femininity, or health. Our results suggest that our manipulation of voice pitch has a measurable impact on judgements of femininity and age, but does not measurably influence vocal and facial attractiveness in naturalistic conditions.
This article juxtaposes the reflections on the expectations from plain language as declared by the participants of workshops delivered by the Jasnopis team with the status of linguistic research on phenomena such as linguistic awareness, linguistic norm, or attitudes towards language. These arguments serve the purpose of proposing a comprehensive approach to text, which positions the recipient in the centre of works on plain language, which allows for the output of other disciplines of knowledge and requires cooperation between experts and practitioners in a given field, text recipients, and linguists/editors. Such an approach is put forward also by the plain language ISO standard (due for publication), which encourages examination of a text together with its recipients and employment of non-linguistic information design techniques. Keywords: plain language – ISO 24495-1 – comprehensive approach to text.
According to the Menzerath-Altmann law, the mean word length is greater in shorter clauses than in longer ones. In Czech, negation is mostly realized by adding the prefix ne- to the beginning of the word, which makes the word longer (and, consequently, it also increases the mean word length in the clause). Therefore, we predict that clauses in which the predicate is in the affirmative form are longer than ones with the negative predicate. We test the hypothesis on a sample of 59 pairs of affirmative and negative forms of the same verb from the Prague Dependency Treebank 3.0.
Background & aims: Throughout typical development, children prioritize different perceptual, social, and linguistic cues to learn words. The earliest acquired words are often those that are perceptually salient and highly imageable. Imageability, the ease in which a word evokes a mental image, is a strong predictor for word age of acquisition in typically developing (TD) children, independent of other lexicosemantic features such as word frequency. However, little is known about the effects of imageability in children with autism spectrum disorder (ASD), who tend to have differences in linguistic processing and delayed language acquisition compared to their TD peers. This study explores the extent to which imageability and word frequency are associated with early noun and verb acquisition in children with ASD. Methods: Secondary analyses were conducted on previously collected data of 156 children (78 TD, 78 ASD) matched on sex and parent-reported language level. Total expressive vocabulary, as measured by the MacArthur Bates Communicative Development Inventory (MB-CDI), included 123 words (78 nouns, 45 verbs) that overlapped with previously published imageability ratings and word input frequencies. A two-step hierarchical linear regression was used to examine the relationship between word input frequency, imageability, and total expressive vocabulary. An F-test was then used to assess the unique contribution of imageability on total expressive vocabulary when controlling for word input frequency. Results: In both the TD and ASD groups, imageability uniquely explained a portion of the variance in total expressive vocabulary size, independent of word input frequency. Notably, imageability was significantly associated with noun vocabulary and verb vocabulary size alone, with imageability explaining a greater portion of the variance in total nouns produced than in total verbs produced. Conclusions: Imageability was identified as a significant lexicosemantic feature for describing expressive vocabulary size in children with ASD. Consistent with literature on TD children, children with ASD who have small vocabularies primarily produce words that are highly imageable. Children who are more proficient word learners with larger vocabularies produce words that are less imageable, indicating a potential shift away from reliance on perceptual-based language processing. This was consistent across both noun and verb vocabularies. Implications: Our findings contribute to a growing body of literature describing early word learning in children with ASD and provide a basis for exploring the use of multisensory language learning strategies.