Groundhog Day is Not a Good Model for Corpus Dispersion

<p><strong>This study aims to capture the image of Muslim youth in the national media Jawa Pos online using corpus-based linguistic analysis. The tendency and alignment of the media in portraying Muslim youth can lead to positive, negative, and neutral public opinion. This research is a text study using a quantitative and qualitative descriptive approach, by collecting news with the keywords Muslim/Islamic youth on Jawapos online and obtaining 39 news stories with 12,636 words. The news text is then analyzed and applied using Antcont to obtain linguistic features of word frequency, collocation, and concordance with descriptive linguistic analysis. The research results show that; the frequency of words that are significant means that the Jawa Pos online in reporting on Muslim youth tends to be general, with the involvement of government activities in places of worship, namely masjid. The emergence of collocations tends to be more about reporting on mosque youth, although there is a tendency for political elements and implies messages and actions that must be carried out by youth. Whereas with the emergence of concordance, this media provides definitions to opinions about the meaning of Muslim youth in general, both negative, neutral, and positive, and more often generates news with a positive and balanced tendency. From this result, the Indonesian national media should report in a balanced manner in constructing the image of Muslim youth in Indonesia, where the majority of the population is Muslim.</strong></p><p><strong><em>Keywords - </em></strong> <em>Corpus Analysis, Imaging, Muslim Youth, National Online Media.</em></p>

Linguistic gender research has originated from Western academia and gone through centennial and continual development throughout the birth and growth of linguistics. Lately, the interjection words, serving as an observable but marginal lexical index, have played an increasingly significant role in many aspects of linguistic studies for recent decades, for example, in the way the experts analyze the vocal expression of emotion, study the cross-lingual analysis in Japanese and English, and many other applications. This paper adopts three classifications of interjection words noise-like primary interjections, word-like primary interjections, and secondary interjections to further explore the relationship between gender and each classification of interjections on the platform of YouTube. The randomly chosen videos of YouTubers, fifty male YouTubers, and fifty female ones, are transcribed into words and analyzed systematically with the assistance of the structured questionnaire and the corpus analysis. Consequently and generally, male YouTubers using the interjections are significantly different from those female YouTubers on word frequency (p<0.05) which agreed with the data from noise-like primary interjections and secondary interjections but disagreed with word-like primary interjections. It suggests that gender does influence the common usage of the interjections significantly, but there are still some exceptions, like the word-like primary interjections.

Unadapted English loanwords have become part of informal communication in many languages, including Croatian. Their use is often motivated by the lack of adequate native equivalents, exposure to English through the media, but also by the prestigious status of the English language. A vast body of research has been dedicated to lexical borrowing, especially from English. At the same time, corpus analyses have mostly been conducted on smaller, ad hoc corpora. Therefore, the goal of this paper is to present the database of English loanwords in Croatian. The database was developed by algorithmic and manual classification of words from the Corpus of Croatian news portals, ENGRI, and provides a list of 9,452 unadapted English loanwords together with the data on their absolute and relative frequencies. The analysis showed that most loanwords (75.85%) appear less than 50 times, while a total of 44.78% of words appear 10 times or less. The biggest drop in the number of loanwords is observed in the categories of occurrence above 500, while only 27 words appear 5,000 times or more. The most frequent English loanword in the corpus is ‘show’ with 80,805 occurrences, which is 0.0122% of all words in the corpus. The analysis of loanwords that occur more than 5,000 times showed that most of them have Croatian translation equivalents,which confirms the role of the media in the introduction of new words. In addition to providing an insight into the occurrence of English loanwords in Croatian, this database also represents a valuable contribution to Croatian computational linguistics resources and enables future experimental research by providing the data on word frequency.

Given inequitable access to learning resources caused by socioeconomic and historical issues, many Indigenous students have fallen behind grade expectations. This is in particular critical at the high school level, with increased academic English language demands in their learning of content areas. This paper reports on an exploratory study using corpus analysis that examined Indigenous students’ vocabulary use in verbal narratives, a spoken corpus—the representation of adolescent oral language competence at the school. It evaluated the feasibility, in terms of vocabulary coverages, of using both human-created and AI-generated narrative curriculum materials with enriched vocabulary to support culturally responsive vocabulary instruction. The study was conducted in an Indigenous high school in Canada, located in a remote First Nation community where most students spoke Ojibwe as their first language. The results found that Indigenous students’ narratives used a significant percentage of the first 1000 high frequency words and a small percentage of the second 1000 high frequency and academic words. Human-created and AI-generated narratives had a significantly higher percentage of the second 1000 high frequency words than Indigenous students’ narratives. Finally, AI-generated narratives contained a significantly higher percentage of academic words than both Indigenous youth’s and human-created narratives. The implications of the findings indicate that a data-driven corpus-based language pedagogy can be effective developing in innovative vocabulary instructional materials for future CALL interventions to support Indigenous youths. This can be achieved in light of culturally responsive pedagogy by leveraging Indigenous oral literacy traditions of storytelling, the youths’ narrative skills and their interests and aspirations. The present study has shown that some AI tools, along with carefully piloted prompts, have the capacity to efficiently co-develop vocabulary instructional content.

Megastudies with processing efficiency measures for thousands of words allow researchers to assess the quality of the word features they are using. In this article, we analyse reading aloud and lexical decision reaction times and accuracy rates for 2,336 words to assess the influence of subjective frequency and age of acquisition on performance. Specifically, we compare newly presented word frequency measures with the existing frequency norms of Kucera and Francis (1967), HAL (Burgess & Livesay, 1998), Brysbaert and New (2009), and Zeno, Ivens, Millard, and Duvvuri (1995). We show that the use of the Kucera and Francis word frequency measure accounts for much less variance than the other word frequencies, which leaves more variance to be "explained" by familiarity ratings and age-of-acquisition ratings. We argue that subjective frequency ratings are no longer needed if researchers have good objective word frequency counts. The effect of age of acquisition remains significant and has an effect size that is of practical relevance, although it is substantially smaller than that of the first phoneme in naming and the objective word frequency in lexical decision. Thus, our results suggest that models of word processing need to utilize these recently developed frequency estimates during training or setting baseline activation levels in the lexicon.

Considering that the analysis of any specialized language should have a representative set of terms, this paper describes the selection of specific Romanian business terms, extracted and processed from a self-compiled corpus, to carry out a quantitative and qualitative analysis of Romanian business terminology in order to build an updated Romanian-English glossary of business terms and collocations. The analysis of these linguistic structures is based on the use of analytical techniques that belong to corpus linguistics as it would be impossible to detect them intuitively. The study focuses on the design of a real corpus extracted from the Romanian business environment. The objective of this paper is to describe the methodology followed in order to compile a corpus for Romanian business language and propose a list of 100 most frequent business words in Romanian. This proposal is made after the compilation and analysis of a study corpus of approx. 1 million words extracted from 8 genres belonging to written business communication. The present paper is part of a larger study that carried out a contrastive analysis of the Romanian and English business languages in order to find similarities and differences between the two target languages.

This research focused on the description of lexical sophistication in LLT Journal: A Journal on Language and Language Teaching. To obtain the description of lexical sophistication in LLT Journal: A Journal on Language and Language Teaching, the researcher used a corpus software called Tool for the Automatic Analysis of Lexical Sophistication (TAALES). Sixty-six articles published in 2016, 2017 and 2018 were used as the data of this study. There were five descriptions of lexical sophistication in LLT Journal: A Journal on Language and Language Teaching, namely word frequency, word range, n-gram, academic list and word information. The word frequency (87%) showed the good amount of percentage in the articles. Word range (87%) showed the use of words in the articles were delivered properly. N-gram (35%) showed the sign of low lexical sophistication scores. Academic list (15%) showed low lexical sophistication scores. Word information (60%) showed high lexical sophistication scores. The percentage of each description of lexical sophistication showed that the academic list gained the lowest percentage among the other descriptions of lexical sophistication. It can be inferred that the academic list aspect in LLT Journal: A Journal on Language and Language Teaching needs to be upgraded.

In nuclear accidents, source estimation methods can be used to assess the amount and status of radioactive materials emission, which is important for nuclear emergency response. Current studies primarily focus on source estimation of a constant release mode, which may not match real nuclear accident scenario. To solve this problem, a fast on-line source term estimation method for non-constant release mode was proposed in this study. Based on the well-established RIMPUFF dispersion model, the extended Kalman filter algorithm was utilized to perform an online estimation of the total amount and height of the release source simultaneously. To simulate the accidental radioactive release mode, a non-constant function was used as the state description of the release source. Both air dispersion model error and measurement error were considered. To demonstrate the feasibility of the proposed method, twin experiments were performed with release profiles. A sensitivity study was also performed to assess the robustness of the algorithm. The experiment results demonstrate that the proposed method can provide robust source term estimation for both constant and non-constant release. The estimation accuracy depends on the steepness of the curve in the growth of the release mode and the distribution of simulated monitors.

Listening comprehension is a real-time process, yet very little is known about the variables affecting real-time second language (L2) listening. The present study aimed at investigating the effects of syntactic complexity and word frequency on L2 listening. Furthermore, the role of the listener’s working memory capacity in listening comprehension was investigated. Fifty-three Dutch learners of French were tested using a working memory task and self-paced listening technique. Working memory scores and reaction times were collected as a measure of processing efficiency. Contrast analyses were run on the reaction times per variable. Correlation tests were run between the working memory scores and reaction times. The results showed that syntactic complexity affects L2 listening comprehension. However, no effect of word frequency on L2 listening and no correlation between working memory capacity and L2 listening in any of the conditions was found. We conclude that linguistic factors shown to affect offline L2 listening are not always generalizable to online L2 listening. We also conclude that working memory considered as a cognitive system separate from other cognitive processes involved in language comprehension does not explain individual differences in real-time L2 listening. Further research needs to be done to better understand (real-time) L2 listening comprehension.

We offer insights on how distance-related trade costs may best be inferred from price-dispersion measures. Using a simple spatial model of price dispersion, we argue that measures of price dispersion that are not spatially informed can mislead researchers into concluding that distance-related costs are small even when such costs are the major determinant of price dispersion. With intra-United States data on eleven goods, we find that distance-related costs are large and are indeed underestimated when inferred from standard, non-spatial, price dispersion measures. Our empirical findings have implications for studies of market integration policies (such as trade liberalization and currency unions) and the significance of economic geography.

Process Capability Indices (PCIs) are very helpful to measure the manufacturing capability and production quality of the products in many manufacturing processes. These PCIs are calculated by using a relationship between process mean and standard deviation, provided that process follows a normal distribution. In case of non-normal processes many researchers recommended the use of robust PCIs by modifying the classical PCIs. In case of robust PCIs most of the work is reported on first- and second-generation PCIs but less work is reported on third generation PCIs. The objective of this work was to evaluate the efficiency of three dispersion measures, namely median absolute deviation (MAD), interquartile range (IQR), and Gini's mean difference (GMD), as a measure of dispersion in third generation PCIs and construct their bootstrap confidence intervals (CIs). The efficacy of these measures is compared with quantile-based PCIs under different asymmetric behaviour of the Weibull process. The results showed that quantile-based PCIs are strongly influenced by high asymmetry and IQR method provides a poor estimator across all sample sizes. On the other hand, the GMD method performed well under low, moderate, and high asymmetry of the Weibull process, but its irregular behavior needs to be addressed carefully. Among all selected four methods MAD-method performed better under low and moderate asymmetric conditions. In case of interval estimation, bias-corrected percentile (BCPB) CIs was recommended for quantile-based PCIs, while percentile (PB) and percentile-t (PTB) CIs were recommended for MAD-based PCIs under all asymmetric conditions. To validate the simulated findings, two real-world datasets were analyzed that supported the simulation results.

A Zipfian model of an automatic bibliographic system is developed using parameters describing the contents of it database and its inverted file. The underlying structure of the Zipf distribution is derived, with particular emphasis on its application to work frequencies, especially with regard to the inverted flies of an automatic bibliographic system. Andrew Booth developed a form of Zipf's law which estimates the number of words of a particular frequency for a given author and text. His formulation has been adopted as the basis of a model of term dispersion in an inverted file system. The model is also distinctive in its consideration of the proliferation of spelling errors in free text, and the inclusion of all searchable elements from the system's inverted file. This model is applied to the National Library of Medicine's MEDLINE. The model carries implications for the determination of database storage requirements, search response time, and search exhaustiveness.

A model of attenuation and sound speed in ocean sediments in the low- to mid-frequency range is presented. It is based on a combination of the Biot–Stoll and contact squirt and shear drag model [Chotiros and Isakson, J. Acoust. Soc. Am. 116, 2011–2022, (2004)] and the frame virtual mass model [Chotiros and Isakson, J. Acoust. Soc. Am. 121, EL70 (2007)]. This combination can match measured sound speeds below Wood’s equation lower bound and it addresses issues related to the Kramers–Kronig relationship. It is able to match both the sound speed dispersion and attenuation measurements from a large number of sites, including the sediment acoustics experiments (SAX99 and SAX04) in the Gulf of Mexico, the shallow water experiment on the Atlantic coast, and the Yellow Sea [Zhou, J. Acoust. Soc. Am. 78, 1003–1009 (1985)]. [Work supported by ONR, Ocean Acoustics.]

We report on the development of the air quality monitoring and early detection system for an enclosed environment with specific emphasis on manned spacecraft. The proposed monitoring approach is based on the distributed parameter model of contaminant dispersion and real-time contaminant concentration measurements. The implicit Kalman filtering algorithm is used to generate online estimations of the spatial contamination profile, which are used for the air quality monitoring and early detection of an air contamination event.

The aim of this study is to find such a minimal size of text samples for authorship attribution that would provide stable results independent of random noise. A few controlled tests for different sample lengths, languages, and genres are discussed and compared. Depending on the corpus used, the minimal sample length varied from 2,500 words (Latin prose) to 5,000 or so words (in most cases, including English, German, Polish, and Hungarian novels). Another observation is connected with the method of sampling: contrary to common sense, randomly excerpted ‘bags of words’ turned out to be much more effective than the classical solution, i.e. using original sequences of words (‘passages’) of desired size. Although the tests have been performed using the Delta method ( Burrows, J.F . (2002). ‘Delta’: a measure of stylistic difference and a guide to likely authorship. Literary and Linguistic Computing , 17 (3): 267–87) applied to the most frequent words, some additional experiments have been conducted for support vector machines and k -NN applied to most frequent words, character 3-grams, character 4-grams, and parts-of-speech-tag 3-grams. Despite significant differences in overall attributive success rate between particular methods and/or style markers, the minimal amount of textual data needed for reliable authorship attribution turned out to be method-independent.