Frequency Of Word Usage Research Articles

Language production and implicit statistical learning in typical development and children with acquired language disorders: an exploratory study

ABSTRACT Statistical properties of language provide important cues for language learning and may be processed by domain-general cognitive systems. We investigated the relationship between implicit statistical learning (the unconscious detection of statistical regularities in input) and language production. Twenty typically developing (TD) children and nine children with acquired language disorders (ALD) (aged 6–18 years) took part in a Boston Cookie Theft picture description task. Using a computerized analysis, we investigated statistical properties, such as usage frequency of words and collocation strength of word combinations. Participants also completed a non-linguistic serial reaction time (SRT) task, which tested non-verbal, implicit statistical learning in the visual-motor modality. We determined age effects, and compared language production and SRT performance between both groups. Older TD children produced more connected language, more words, less frequent function words, more rare or novel combinations, and showed better statistical learning. Children with ALD produced less connected language, more weakly collocated combinations, displayed less lexical diversity and showed poorer statistical learning. Post-hoc analyses found correlations between statistical learning and statistical properties of spoken language. Given the rarity and heterogeneity of children with ALD, group size was small and the study should be considered exploratory. However, we note that results are compatible with the view that language production draws on statistical learning and that impairment of statistical learning can be related to language disorders.

Increase of Collectivistic Expression in China During the COVID-19 Outbreak: An Empirical Study on Online Social Networks.

The pathogen-prevalence hypothesis postulates that collectivism would be strengthened in the long term in tandem with recurrent attacks of infectious diseases. However, it is unclear whether a one-time pathogen epidemic would elevate collectivism. The outbreak of COVID-19 and the widespread prevalence of online social networks have provided researchers an opportunity to explore this issue. This study sampled and analyzed the posts of 126,165 active users on Weibo, a leading Chinese online social network. It used independent-sample t-tests to examine whether COVID-19 had an impact on Chinese collectivistic value-related behaviors by comparing the usage frequency of personal pronouns, group-related words, and relationship-related words before and after the outbreak. Overall, most collectivist words exhibited a significant upward trend after the outbreak. In turn, this tendency pointed to a rising sense of collectivism (versus individualism). Hence, this study confirmed the pathogen-prevalence hypothesis in real settings, finding that an outbreak of an infectious disease such as COVID-19 could exert an impact on collectivism and may deliver a theoretical basis for psychological protection against the threat of COVID-19. However, further evaluation is required to ascertain whether this trend is universal or culture-specific.

Sociolinguistic Monitoring of Word Usage Frequency of Youth Sociolect

The current paper describes the lexical units of youth sociolect and is aimed at monitoring youth speech. Special attention is given to heterogeneous social factors that influence the word usage in youth sociolect and involve micro-and macrosociolinguistic pilot projects on frequency occurrence of English words in a multicultural society. Miscrosociolinguistic analysis was focused on societal and gender differences in youth sociolect. The initial stage of the macrosociolinguistic monitoring included the questionnaire development, setting the boundaries of a sociolinguistic survey and the number of lexical items under consideration. The second step in the project was monitoring itself. Finally, the results of the monitoring were analyzed. The study was carried out through an online questionnaire, face-to-face and remote interviewing, with native English speakers and speakers of other languages from different countries and regions. The monitoring of usage frequency demonstrated that a limited number of English lexical units are used in interpersonal and professional communication; the other part of words presents a diverse picture. The respondents involved in professional activity tend to demonstrate more tolerance to the youth sociolect than ordinary people. The analysis proved the point about the heterogeneous character of sociolect usage by youth and its dependence on the sphere of communication.

Context- and Subgroup-Specific Language Changes in Individuals Who Develop PTSD After Trauma.

Post-traumatic stress disorder (PTSD) is a very common condition with more than 3 million new cases per year in the US alone. The right diagnosis in a timely manner is key to ensuring a prompt treatment that could lead to a full recovery. Unfortunately, avoidance of trauma reminders, social stigma, self-presentation, and self-assessment biases often prevent individuals from seeking timely evaluation, leading to delays in treatment and suboptimal outcomes. Previous studies show that various mental health conditions are associated with distinct patterns of language use. Analyzing language use may also help to avoid response bias in self-reports. In this study, we analyze text data from online forum users, showing that language use differences between PTSD sufferers and controls. In all groups of PTSD sufferers, the usage of singular first-person pronouns was higher and that of plural first-person pronouns was lower than in control groups. However, the analysis of other word categories suggests that subgroups of people with the same mental health disorder (here PTSD) may have salient differences in their language use, particularly in word usage frequencies. Additionally, we show that word usage patterns may vary depending on the type of the text analyzed. Nevertheless, more studies will be needed to increase precision by further examine a variety of text types and different comorbidities. If properly developed, such tools may facilitate earlier PTSD diagnosis, leading to timely support and treatment, which are associated with better outcomes.

Lexical Landscapes as large in silico data for examining advanced properties of fitness landscapes.

In silico approaches have served a central role in the development of evolutionary theory for generations. This especially applies to the concept of the fitness landscape, one of the most important abstractions in evolutionary genetics, and one which has benefited from the presence of large empirical data sets only in the last decade or so. In this study, we propose a method that allows us to generate enormous data sets that walk the line between in silico and empirical: word usage frequencies as catalogued by the Google ngram corpora. These data can be codified or analogized in terms of a multidimensional empirical fitness landscape towards the examination of advanced concepts—adaptive landscape by environment interactions, clonal competition, higher-order epistasis and countless others. We argue that the greater Lexical Landscapes approach can serve as a platform that offers an astronomical number of fitness landscapes for exploration (at least) or theoretical formalism (potentially) in evolutionary biology.

Improving semantic change analysis by combining word embeddings and word frequencies

Language is constantly evolving. As part of diachronic linguistics, semantic change analysis examines how the meanings of words evolve over time. Such semantic awareness is important to retrieve content from digital libraries. Recent research on semantic change analysis relying on word embeddings has yielded significant improvements over previous work. However, a recent, but somewhat neglected observation so far is that the rate of semantic shift negatively correlates with word-usage frequency. In this article, we therefore propose SCAF, Semantic Change Analysis with Frequency. It abstracts from the concrete embeddings and includes word frequencies as an orthogonal feature. SCAF allows using different combinations of embedding type, optimization algorithm and alignment method. Additionally, we leverage existing approaches for time series analysis, by using change detection methods to identify semantic shifts. In an evaluation with a realistic setup, SCAF achieves better detection rates than prior approaches, 95% instead of 51%. On the Google Books Ngram data set, our approach detects both known and yet unknown shifts for popular words.

Proactive Suicide Prevention Online (PSPO): Machine Identification and Crisis Management for Chinese Social Media Users With Suicidal Thoughts and Behaviors.

BackgroundSuicide is a great public health challenge. Two hundred million people attempt suicide in China annually. Existing suicide prevention programs require the help-seeking initiative of suicidal individuals, but many of them have a low motivation to seek the required help. We propose that a proactive and targeted suicide prevention strategy can prompt more people with suicidal thoughts and behaviors to seek help.ObjectiveThe goal of the research was to test the feasibility and acceptability of Proactive Suicide Prevention Online (PSPO), a new approach based on social media that combines proactive identification of suicide-prone individuals with specialized crisis management.MethodsWe first located a microblog group online. Their comments on a suicide note were analyzed by experts to provide a training set for the machine learning models for suicide identification. The best-performing model was used to automatically identify posts that suggested suicidal thoughts and behaviors. Next, a microblog direct message containing crisis management information, including measures that covered suicide-related issues, depression, help-seeking behavior and an acceptability test, was sent to users who had been identified by the model to be at risk of suicide. For those who replied to the message, trained counselors provided tailored crisis management. The Simplified Chinese Linguistic Inquiry and Word Count was also used to analyze the users’ psycholinguistic texts in 1-month time slots prior to and postconsultation.ResultsA total of 27,007 comments made in April 2017 were analyzed. Among these, 2786 (10.32%) were classified as indicative of suicidal thoughts and behaviors. The performance of the detection model was good, with high precision (.86), recall (.78), F-measure (.86), and accuracy (.88). Between July 3, 2017, and July 3, 2018, we sent out a total of 24,727 direct messages to 12,486 social media users, and 5542 (44.39%) responded. Over one-third of the users who were contacted completed the questionnaires included in the direct message. Of the valid responses, 89.73% (1259/1403) reported suicidal ideation, but more than half (725/1403, 51.67%) reported that they had not sought help. The 9-Item Patient Health Questionnaire (PHQ-9) mean score was 17.40 (SD 5.98). More than two-thirds of the participants (968/1403, 69.00%) thought the PSPO approach was acceptable. Moreover, 2321 users replied to the direct message. In a comparison of the frequency of word usage in their microblog posts 1-month before and after the consultation, we found that the frequency of death-oriented words significantly declined while the frequency of future-oriented words significantly increased.ConclusionsThe PSPO model is suitable for identifying populations that are at risk of suicide. When followed up with proactive crisis management, it may be a useful supplement to existing prevention programs because it has the potential to increase the accessibility of antisuicide information to people with suicidal thoughts and behaviors but a low motivation to seek help.

НЕМОТИВОВАНІ СКЛАДНОНУЛЬСУФІКСАЛЬНІ АД’ЄКТИВИ З ДРУГИМ НЕСОМАТИЧНИМ КОМПОНЕНТОМ НА ОЗНАЧЕННЯ ЛЮДИНИ

The article outlines the derivational, structural-semantic and functional properties of usual and individual-authorial adjectival composites with a materially non-marked suffix with the first adjectival and the second substantive component, denoting a characteristic of a person and having basically non-somatic notion of anthroponymic and non-anthroponymic character. Adjectival composites with the first adjectival and the second substantive component occupy a noticeable place among adjectival derivatives formed with the participation of a zero-suffix formant. So far, composites of the investigated word-formative type in general and complex-zero-suffixal adjectives with a materially non-marked formant in particular have never been the subject of a special study, which determines the relevancy of the chosen topic. The purpose of the article is to define the word-usage frequency of the corresponding complex-zero-suffixal adjectival derivatives, to find out their place, role, specificity of functioning in the modern Ukrainian language, to elucidate their derivational, structural-semantic and functional properties. The achievement of the stated goal involves solving of the following tasks: to determine the peculiar properties of the motivation of the derivatives under study, to analyze the external valency, as well as to find out their pragmatic potential. The conducted analysis of the complex-zero-suffixal adjectival derivatives has revealed the following tendencies: among the composites of the lexical-semantic group being studied, only opaque (non-motivated) composites function actively, while we have not noticed motivated and semi-motivated lexical items among them. Complex-zero-suffixal adjectives have broad co-occurrence and extensively implement their figurative (allegoric) meanings. On the whole, the corresponding composites, entering into distribution with the nouns denoting a person, gain pejorative meaning, becoming the markers of negative evaluation and disparagement. Sometimes it happens that the units under study come into distribution with the nouns denoting the phenomena of nature, realizing a new figurative idiomatic meaning – “the revival” of the nonliving, thus replenishing the corpus of epithets and contributing to the enrichment of the poetic language imagery.

Optimization of site content in the learning process

The aim of the study is to optimize the content in the learning process to increase the effectiveness of learning by matching it with perception, attention, and reading. The results obtained indicate that the format of the visual display of text affects the characteristics of reading. In addition, the influence of font images, word length, and frequency of word usage on oculomotor activity (number of fixations, number of views, and view length) are shown. Differences in the visual formats of texts are also evident in reading comprehension and when comparing both the indirect evaluations (the shingles method) and the self-evaluations of the subjects. Furthermore, according to the indicators of self-evaluation and reading speed, the standard text format shows greater efficiency. Moreover, it is shown that the coloring of fragments of the text accelerates the reading process. This is consistent with the data indicating that the isolation of syllables (coloring) makes the task of reading easier for children with dyslexia.

PEREMPUAN ATAU WANITA? PERBANDINGAN BERBASIS KORPUS TENTANG LEKSIKON BERBIAS GENDER

<p class="TeksAbstrak">Among the debates on the choice of Indonesian term for woman, the present research examines the use of the gendered lexicon <em>perempuan </em>and <em>wanita </em>with the aim to reveal which term is more preferable and how the terms are used to talk about woman. Using corpus-based approach, the study compares frequency and pattern of word usage of <em>perempuan </em>and <em>wanita </em>from two corpora, namely IndonesianWac and ind_mixed_2013. The research uses a mixed-method design in which quantitative analysis is used to identify word frequency and to measure significant collocation, while the qualitative analysis is used to determine meaning on the basis of semantic preference. The study reveals that the word <em>wanita </em>tends to be more frequently used than the word <em>perempuan </em>to signify woman. In terms of meaning, both of the words are used to talk about woman in relation to body, especially their reproductive function, people particularly men, family, and age. The difference is that <em>perempuan </em>tends to relate woman with the issues of gender equality and organization, while <em>wanita </em>with sexual relationship. Furthermore, it strongly indicates that woman in relation to their public domain is denoted with the term <em>perempuan, </em>whereas the relation with domestic domain is denoted with <em>wanita. </em></p>

THE USE OF FOREIGN WORDS FROM TRANSPORT PUBLICATIONS OF THE END OF THE 19TH CENTURY AND THE FIRST HALF OF THE 20TH CENTURY

The article analyses foreign words (their origin, meaning and equivalents), selected from the transport publications of the end of the 19th century and the first half of the 20th century: the conducted questionnaire method assesses the relevancy of foreign words nowadays, i.e. whether the respondents are aware of the equivalents of foreign words, as well as examines the usage frequency of foreign words. The research defines the respondents’ attitudes towards changing foreign words into their equivalents. It also examines the current usage of the foreign words in different language functional styles. The results of the research are structured and provided in the table and figures.

Differential diagnosis of jaw pain using informatics technology.

This study aimed to deduce evidence-based clinical clues that differentiate temporomandibular disorders (TMD)-mimicking conditions from genuine TMD by text mining using natural language processing (NLP) and recursive partitioning. We compared the medical records of 29 patients diagnosed with TMD-mimicking conditions and 290 patients diagnosed with genuine TMD. Chief complaints and medical histories were preprocessed via NLP to compare the frequency of word usage. In addition, recursive partitioning was used to deduce the optimal size of mouth opening, which could differentiate TMD-mimicking from genuine TMD groups. The prevalence of TMD-mimicking conditions was more evenly distributed across all age groups and showed a nearly equal gender ratio, which was significantly different from genuine TMD. TMD-mimicking conditions were caused by inflammation, infection, hereditary disease and neoplasm. Patients with TMD-mimicking conditions frequently used "mouth opening limitation" (P<.001), but less commonly used words such as "noise" (P<.001) and "temporomandibular joint" (P<.001) than patients with genuine TMD. A diagnostic classification tree on the basis of recursive partitioning suggested that 12.0mm of comfortable mouth opening and 26.5mm of maximum mouth opening were deduced as the most optimal mouth-opening cutoff sizes. When the combined analyses were performed based on both the text mining and clinical examination data, the predictive performance of the model was 96.6% with 69.0% sensitivity and 99.3% specificity in predicting TMD-mimicking conditions. In conclusion, this study showed that AI technology-based methods could be applied in the field of differential diagnosis of orofacial pain disorders.

Gender Differences of Translation Transformations Based on the Short Story of A.P. Chekhov's 'Lady with a Dog'

main idea of the paper is the influence of the linguistic personality as the author of the secondary text on the translation of a work of art in the gender aspect. article deals with the translations of Anton Pavlovich Chekhov's story The Lady with the Dog. Based on the classification of male and female speech, the linguistic personality of the English translators of Anton Chekhov's work of art is considered. It is spoken in detail that translators reproduce certain types of gender models according to the leading trends in the change of the entire socio-cultural system. analysis of the translation of various stylistic means is conducted. Female translators have transformed stylistic means more expressively. male translator is in some cases ready to step back from the original, expressing his own creativity and talent. In this case the translation does not lose its brightness, but, on the contrary, it acquires new colours of irony. Corpus linguistics is examined in our paper. In order to track the coincidence of the frequency of words in the original and in translation, we compare the usage of words statistics in the British National Corpus, and also in the National Corpus of the Russian language. method of corpus linguistics allows to analyze the frequency of word usage and years of use.

How humans transmit language: horizontal transmission matches word frequencies among peers on Twitter.

Language transmission, the passing on of language features such as words between people, is the process of inheritance that underlies linguistic evolution. To understand how language transmission works, we need a mechanistic understanding based on empirical evidence of lasting change of language usage. Here, we analysed 200 million online conversations to investigate transmission between individuals. We find that the frequency of word usage is inherited over conversations, rather than only the binary presence or absence of a word in a person's lexicon. We propose a mechanism for transmission whereby for each word someone encounters there is a chance they will use it more often. Using this mechanism, we measure that, for one word in around every hundred a person encounters, they will use that word more frequently. As more commonly used words are encountered more often, this means that it is the frequencies of words which are copied. Beyond this, our measurements indicate that this per-encounter mechanism is neutral and applies without any further distinction as to whether a word encountered in a conversation is commonly used or not. An important consequence of this is that frequencies of many words can be used in concert to observe and measure language transmission, and our results confirm this. These results indicate that our mechanism for transmission can be used to study language patterns and evolution within populations.

Sentiment analysis methods for understanding large-scale texts: a case for using continuum-scored words and word shift graphs

The emergence and global adoption of social media has rendered possible the real-time estimation of population-scale sentiment, an extraordinary capacity which has profound implications for our understanding of human behavior. Given the growing assortment of sentiment-measuring instruments, it is imperative to understand which aspects of sentiment dictionaries contribute to both their classification accuracy and their ability to provide richer understanding of texts. Here, we perform detailed, quantitative tests and qualitative assessments of 6 dictionary-based methods applied to 4 different corpora, and briefly examine a further 20 methods. We show that while inappropriate for sentences, dictionary-based methods are generally robust in their classification accuracy for longer texts. Most importantly they can aid understanding of texts with reliable and meaningful word shift graphs if (1) the dictionary covers a sufficiently large portion of a given text’s lexicon when weighted by word usage frequency; and (2) words are scored on a continuous scale.

Not all cultural values are created equal: Cultural change in China reexamined through Google books.

Given its major transformations in recent decades, China has figured prominently in research on cultural change. Previous research converges in showing a general trend towards individualism in contemporary China while noting that rising individualism tends to coexist with enduring collectivism. To further understand this, we tested whether perceived traditional importance of cultural values would modulate the trajectory of cultural change reflected in word usage frequencies in published books. We re-analysed Google's Chinese corpus since 1980 based on a broad sample of words associated with individualism-collectivism. We replicated the pattern of rising individualism and declining collectivism among words of modest and low perceived traditional importance. Most important, however, collectivistic words of high perceived traditional importance increased in usage frequencies with time, thus departing from the general trend towards individualism. Overall, our research underscores the role of core culture in cultural maintenance during times of rapid cultural change.

Comparison of Genre based Tamil Songs Classification using term Frequency and Inverse Document Frequency

Genre classification of Tamil songs is done based on mood, emotion etc. of the listener. The musical genre classification is based on three levels as base, mood and style. Our proposed methodology is comparison of genre based Tamil songs classification using Term Frequency and Inverse Document Frequency ( tf-idf ). So for this methodology has been applied for English and Korean songs only. In this paper, we are classifying the Tamil songs using the method based on tf-idf scores. The classifier establishes a relation between the features of the training samples and related categories. The frequency of word usage is identified by term frequency and inverse document frequency. Support Vector Machine (SVM) algorithm and Naive Bayes algorithm (NB) are used in Weka classification tool for analysis. We have compared the experimental results of both the algorithms in musical genre classification. From the experimental values obtained using various parameters, we have shown that the Naive Bayes algorithm has classified the genre marginally better than Support Vector Machine algorithm. We have considered 2000 Tamil songs for testing the genre classification. At present we have considered two genres for classification and the same classification can be extended to other genres in future.

Cursing as a Form of Risk-Taking

The research investigated the relationships among cursing, risk-taking (i.e., drinking, smoking, using illegal drugs, and engaging in risky sexual behavior), and personality variables that have been shown to be related to risk-taking (i.e., sensation-seeking and Big-Five personality factors). In a study with 360 participants, the results showed that for both men and women, those who took more risks in daily life also reported cursing more than others. Men and women who scored higher on disinhibition and experience-seeking reported taking more risks and cursing more than others. Men and women who scored higher in conscientiousness reported taking fewer risks and reported cursing less than others. The results of a multiple regression analyses showed that frequency of curse word usage was predicted by disinhibition and risk-taking, and cursing behavior was predicted by disinhibition, risk-taking, and emotional stability. Overall men reported taking more risks and cursing more often than women; however, in the multiple regression analyses, participant sex was not among the significant predictors. Theoretical implications and directions for future research are discussed.

Drug term trends in American hip-hop lyrics

Purpose – Many young people around the world embrace hip-hop music and culture. Since the genre’s conception in the 1970s, hip-hop music and lyrics have made regular references to drugs. Understanding the relevance of these documented trends is important, especially as adolescence is a period of high risk for substance misuse. The purpose of this paper is to explore how and possibly why different lyrical trends in hip-hop music have emerged, risen and fallen out of popularity by examining word usage frequency of drug terminology in hip-hop lyrics spanning several decades of this genre. Design/methodology/approach – Electronic searches were completed using an open source database known as Rap Genius Rap Stats, which contains verified annotations and text. Word frequency was plotted against time using data available from 1988 to 2015. Word frequency was defined as a percentage of the number of hip-hop songs containing a specific drug-term (per year) based on the number of hip-hop songs recorded/produced (that year). Standardized “medical/pharmaceutical” terminologies and common “street” terminologies were plotted independently for time series visualization. Drug terms were represented using the highest frequency search term. Generic “street” terms with multiple meanings were excluded. Findings – As might be predicted, the usage of “street” terms in hip-hop lyrics was more frequently observed than the usage of “medical/pharmaceutical” terms. An exception was the term “crack”, which was included in both plots as this word could be referenced as a “street” term and as a “medical/pharmaceutical” term. The authors observed larger fluctuations in “street” term usage across time relative to only slight fluctuations of “medical/pharmaceutical” term usage across time. Originality/value – In this study, the authors illustrate several drug terminology trends in hip-hop lyrics. The authors discuss some of the socio-political, socio-demographic and geographical implications that may have influenced these trends, such as the rise of the “street” term molly that emerged when references to molly made by hip-hop artists became increasingly popular and a more suburban demographic transpired. This preliminary work may help to enhance two-way youth-oriented communication between health care professionals and service users, possibly improving the translation of drug-related medical messages. The preliminary work may also inform future research to consider whether such lyrical trends precede or follow changes in population substance use.

Detecting near-duplicate text documents with a hybrid approach

Near duplicate data not only increase the cost of information processing in big data, but also increase decision time. Therefore, detecting and eliminating nearly identical information is vital to enhance overall business decisions. To identify near-duplicates in large-scale text data, the shingling algorithm has been widely used. This algorithm is based on occurrences of contiguous subsequences of tokens in two or more sets of information, such as in documents. In other words, if there is a slight variation among documents, the overall performance of the algorithm decreases. Therefore, to increase the efficiency and accuracy performances of the shingling algorithm, we propose a hybrid approach that embeds Jaro distance and statistical results of word usage frequency for fixing the ill-defined data. In a real text dataset, the proposed hybrid approach improved the shingling algorithm’s accuracy performance by 27% on average and achieved above 90% common shingles.