Word Frequency Data Research Articles

Analysis and Optimization of Residential Elements from the Perspective of Multi-Child Families in the Yangtze River Delta Region

Over the last few decades, policy changes have resulted in changes in family structure and cyclical changes within families. The structure of multi-child families will have a significant influence on housing demand and supply, necessitating a thorough demand study. This study examines the requirements of 739 multi-child families in the Yangtze River Delta (YRD) region at different stages and graphically displays the variables influencing their level of housing satisfaction, offering a scientific reference for the design and optimization of housing. Firstly, the residential elements that impact families with multiple children were categorized into 3 categories, 14 subcategories, and 65 influencing elements based on expert consultation and word frequency data. Secondly, 739 families in the YRD region were chosen for investigation, and importance–performance analysis (IPA) was employed to study and assess the residential elements of multi-child families. The IPA study findings were compared to those obtained from on-site surveys and network data crawling. Finally, the residential elements to be optimized were quantitatively determined, and the relevant optimization techniques were provided in conjunction with door-to-door interviewing. This study examines the needs of multi-child families at various phases, as well as the factors that impact their residential satisfaction, and provides optimization solutions for the long-term use and renewal of residential structures.

Assessing Word Frequency by Rating Method

One of the major challenges faced by researchers in linguistics and psycholinguistics in India is the lack of corpora of linguistic information such as word frequency, letter frequency, phonological and phonetic data on sounds, and morphological data on words, etc, in Indian languages. When they do not get the stimulus material readily available in any corpus, researchers resort to preparing a stimulus set that meets the criteria for their specific study. This paper presents an attempt of preparing a list of words for an ongoing psycholinguistic study on lexical access in Kannada by a method of expert rating on selected words' frequency. A total of 60 Kannada words were selected as stimuli, and seven native Kannada experts rated them on frequency on a six-point scale. Descriptive statistics and Pearson's correlation were used for data analysis. Results revealed that the selected Kannada words have high-frequency ratings with good inter-rater reliability.

Puzzling Patterns: Exploring the Relationship between Confidence and Filler Words in Teenage Speech

The present study investigates whether an adolescent’s confidence in their knowledge correlates to the frequency of filler words in their speech. High school participants (N=31) were presented with two mazes, Maze A to evoke more confidence and obstacle-ridden Maze B to evoke less confidence. Participants submitted Likert Scales regarding their confidence in completing each maze, then completed the mazes. They were then recorded while discussing each maze. Finally, their speech was transcribed and annotated for filler words, marking distinctions between filled pauses and discourse markers. Using this data, a Spearman Rank Correlation Coefficient test and a paired t-test were conducted to test the strength of correlation between filler word frequency and confidence. The results of the Spearman Rank test implied negligible correlation between the variables, r(29) = -.1, p = .184. The paired t-test, using an accepted significance level of 0.05, indicated no difference in the means of filler word frequency data before confidence is decreased (M=5.5, SD=2.8) and filler word frequency after confidence is decreased (M=5.7, SD=3.1), t(30) = .3, p = .740). Additional Spearman Rank tests and paired t-tests results on filled pause and discourse marker usage before and after variation in confidence implied that an adolescent’s confidence does not influence the type of the filler word they use. Due to sample size limitations, these conclusions cannot be generalized, so further research is needed, but this data lays the foundation for adaptations to school curriculums and societal perceptions of adolescents.

Why is Semantic Change Asymmetric? The Role of Concreteness and Word Frequency and Metaphor and Metonymy

ABSTRACT Metaphors and other tropes are commonly thought to reflect asymmetries in concreteness, with concrete sources being used to talk about relatively more abstract targets. Similarly, originating senses in diachronic semantic change have often been argued to be more concrete than extended senses. In this paper, we use a dataset of cross-linguistically attested semantic changes to empirically test the idea that asymmetries in figurative language are predicted by asymmetries in concreteness. We find only weak evidence for the role of concreteness and argue that concreteness is not a helpful notion when it comes to describing changes where both originating and extended senses are highly concrete (e.g., skin > bark, liver > lungs). Moreover, we find that word frequency data from English and other languages is a stronger predictor of these typologically common semantic changes. We discuss the implications of our findings for metaphor theory and theories of semantic change.

Thai Undergraduate Student Awareness of Regular and Irregular Grapheme-Phoneme Correspondence

<p>Recent events in Thailand in reference to the teaching of phonics for better comprehension of English vocabulary have highlighted the overuse of identifying letter-sound relationships in English by utilizing the familiar Thai orthography to assist developing Thai EFL learners. This paper investigated the long-term effects of using such pedagogy on recognizing regular and irregular Grapheme-Phoneme Correspondences (GPC) in English by Thai undergraduate students. To address this matter, the study used a convenience sampling of 373 first-year university students from 11 general education English classes at a mid-sized private university near Bangkok, Thailand. The familiar English poem <em>I Take It You Already Know</em> was employed for data collection, for it consists of a practical ratio of frequent and infrequent English grapheme-phoneme correspondences. Extensive lists of recognized grapheme-phoneme correspondences were used to identify the frequent or regular, and the infrequent or irregular main phoneme present in each of the 60 most frequently queried content words of the poem. Point-Biserial Correlation was employed to measure the strength of association between the frequency occurrence of the most queried content words from the data set, and the examined weighted word frequency data. The findings suggest that, in general, the Thai undergraduate students demonstrated an overall lack of recognition of regular and irregular Grapheme-Phoneme Correspondences of English. </p>

텍스트 마이닝 기법을 활용한 프로야구 인식변화에 관한 빅데이터 분석

The purpose of this study is to analyze the change of perception about professional baseball by using text mining technique. For this purpose, used websites, blogs, news, and cafes provided by Naver and Daum. Data analysis period was selected from January 1, 2014 to December 31, 2018 for a total of 5 years. For data collection and analysis, we extracted word frequency and metric data through Textom, a social metric program, and analyzed connection accuracy, network centrality, and network density using Ucinet 6.0 to understand the connection structure between words. And quantified. In order to visualize the quantified data, we used the NetDraw function of Ucinet 6.0 to visualize the network of words related to professional baseball. In addition, CONCOR analysis was performed to identify the clusters formed between each word. First, the results of the frequency analysis of the resulting words based on keywords, 2014 Players, Game, Nexen Heroes, 2015 Player, Samsung Lions, Game, 2016 player, Doosan Bears, Game, 2017 Player, Game, Asian professional baseball Key keywords followed in the Championship, 2018 athletes, Doosan Bears, and Nexen Heroes. Second, the result of conducting CONCOR analysis by collecting the result words that appeared specifically based on keywords. Best Player, Mobile Game, Intuition, 3rd, 2016 Event & Accident, All-Star Game, Opening, Contract, Sexual Abuse, Choi Hyung-woo, 4th, U + App in 2017, Fifth, the group was formed in 2018, Korean Series, Winning Team, Mobile Game, U+ App, Yang, Ui-ji, and Post Season.

The prevalence and frequency of Japanese-English cognates: Recommendations for future research in applied linguistics

Abstract Research has demonstrated that cognates are processed and acquired more readily than noncognates regardless of whether the languages share a common script or etymological background (e. g., Japanese and English). Very little research, however, has focused on the prevalence and frequency of cognates in orthographically distinct languages. Using Japanese word frequency data, the present study demonstrates that between 49 % and 22 % of the most common 10000 words in English are cognate in Japanese, depending on the frequency threshold used. The analysis is extended to the Academic Word List (Coxhead 2000), which is shown to be between 59 % and 30 % cognate. Finally, a lexical familiarity study revealed that Japanese cognate frequency was a reliable indicator of whether the word was known to the majority of Japanese speakers. Based on the findings and drawing upon research in psycholinguistics, a number of recommendations are put forward for future studies in applied linguistics.

Search Intention Analysis for Task- and User-Centered Visualization in Big Data Applications

A new approach for classifying users’ search intentions is described in this paper. The approach uses the parameters: word frequency, query length and entity matching for distinguishing the user's query into exploratory, targeted and analysis search. The approach focuses mainly on word frequency analysis, where different sources for word frequency data are considered such as the Wortschatz frequency service by the University of Leipzig and the Microsoft Ngram service (now part of the Microsoft Cognitive Services). The model is evaluated with the help of a survey tool and few machine learning techniques. The survey was conducted with more than one hundred users and on evaluating the model with the collected data, the results are satisfactory. In big data applications the search intention analysis can be used to identify the purpose of a performed search, to provide an optimal initially set of visualizations that respects the intended task of the user to work with the result data.

The Big Five Factor Marker Adjectives Are Not Especially Popular Words. Are They Superior Descriptors?

Vocabularies of natural languages evolve over time. Useful words become more popular and useless concepts disappear. In this study, the frequency of the use of 295 English, 100 German, and 114 French personality adjectives in book texts and Twitter messages as qualifiers of the words person, woman, homme, femme, and Person was studied. Word frequency data were compared to factor loadings from previous factor analytic studies on personality terms. The correlation between the popularity of an adjective and its highest primary loading in five- and six-factor models was low (-0.12 to 0.17). The Big five (six) marker adjectives were not more popular than "blended" adjectives that had moderate loadings on several factors. This finding implies that laymen consider "blended" adjectives as equally useful descriptors compared to adjectives that represent core features of the five (six) factors. These results are compatible with three hypotheses: 1) laymen are not good at describing personality, 2) the five (six) factors are artifacts of research methods, 3) the interaction of the five (six) factors is not well understood.

A prototype of a receptive lexical test for a polysynthetic heritage language: The case of Inuttitut in Labrador

This paper describes the process of designing, administering, and assessing a language-sensitive and culture-specific lexical test of Labrador Inuttitut (a dialect of Inuktitut, an Eskimo-Aleut language). This process presented numerous challenges, from choosing citation forms in a polysynthetic language to dealing with a lack of word frequency data. Twenty heritage receptive bilinguals (RBs) with very limited production skills in Inuttitut (their first language) and a comparison group of eight fluent bilinguals (FBs) participated in our study. Since the RBs lacked production skills in Inuttitut, the lexical test required participants to translate a carefully compiled list of Inuttitut nouns and verbs into English. The results revealed that RBs had good comprehension of basic vocabulary (85% accuracy), but differed significantly from FBs, mostly because the RBs had a number of partially accurate translations. The three lowest scoring RBs had the highest number of such translations as well as inaccurate translations based on phonological associations, as is common in emergent lexicons. This lexical test correlates with grammatical proficiency measures, pointing to its potential value as a quick placement and diagnostic test in revitalization programs for Inuttitut as well as other languages in a language loss situation.

Frequency in Incidental Vocabulary Acquisition Research: An Undefined Concept and Some Consequences

Frequency in Incidental Vocabulary Acquisition Research: An Undefined Concept and Some Consequences

Network-based modeling and intelligent data mining of social media for improving care.

Intelligently extracting knowledge from social media has recently attracted great interest from the Biomedical and Health Informatics community to simultaneously improve healthcare outcomes and reduce costs using consumer-generated opinion. We propose a two-step analysis framework that focuses on positive and negative sentiment, as well as the side effects of treatment, in users' forum posts, and identifies user communities (modules) and influential users for the purpose of ascertaining user opinion of cancer treatment. We used a self-organizing map to analyze word frequency data derived from users' forum posts. We then introduced a novel network-based approach for modeling users' forum interactions and employed a network partitioning method based on optimizing a stability quality measure. This allowed us to determine consumer opinion and identify influential users within the retrieved modules using information derived from both word-frequency data and network-based properties. Our approach can expand research into intelligently mining social media data for consumer opinion of various treatments to provide rapid, up-to-date information for the pharmaceutical industry, hospitals, and medical staff, on the effectiveness (or ineffectiveness) of future treatments.

Word frequency of written Urdu

Performance on word processing tasks is known to be influenced by the frequency with which words occur in a language. Large and robust effects of word frequency occur across languages and the processes thought to be sensitive to word frequency are considered fundamentally important characteristics of the mental lexicon. To our knowledge, word frequency data is non-existent for Urdu. This important language has characteristics that make it appealing to psycholinguists. Unfortunately, most of the Urdu published electronically is in the form of image files rather than text and therefore, has been largely inaccessible by programs designed to generate word counts. Consequently, unlike other important orthographies (e.g., English) orthographic word frequencies in Urdu are not readily available. We describe here a database that addresses this methodological gap. We have constructed a word frequency database for written Urdu and describe that development. We also describe data from simple tests of the effects of Urdu word frequency to demonstrate that our measure results in effects considered to be the hallmark of frequency effects. The frequency counts from this database will help psycholinguists and cognitive psychologists conduct and control future studies on the mental lexicon using Urdu. This database can be downloaded from http://web2.uwindsor.ca/psychology/urdufrequency/

A Preliminary Inquiry Into Using Corpus Word Frequency Data in the Automatic Generation of English Language Cloze Tests

This paper outlines how a multiple choice vocabulary cloze test can be produced from a text. The process described involves assigning word class tags to the text and then retrieving word frequencies for the words in the text from an analyzed corpus. The system allows for the creation of three types of test--one based on the "nth-word deletion" principle, one based on user-specified frequency ranges, and one based on a particular word class. After the user's selection, word class and word frequency of each test item key are matched with similar word class and word frequency options to construct the test items. Analysis of tests produced by the system and administered to students indicates the potential of the computer aided test system, although the three test production modes are not equally successful in their production of "acceptable" test items with the nth-word deletion mode producing considerably fewer acceptable items than the two language oriented test production modes of specified word frequency ranges and particular word classes. The paper concludes with a discussion of the extent to which good test material can be realistically produced by computer aided systems and the different computer tools which may be of use in the process.

A Bayesian analysis of frequency count data

The zero truncated inverse Gaussian–Poisson model, obtained by first mixing the Poisson model assuming its expected value has an inverse Gaussian distribution and then truncating the model at zero, is very useful when modelling frequency count data. A Bayesian analysis based on this statistical model is implemented on the word frequency counts of various texts, and its validity is checked by exploring the posterior distribution of the Pearson errors and by implementing posterior predictive consistency checks. The analysis based on this model is useful because it allows one to use the posterior distribution of the model mixing density as an approximation of the posterior distribution of the density of the word frequencies of the vocabulary of the author, which is useful to characterize the style of that author. The posterior distribution of the expectation and of measures of the variability of that mixing distribution can be used to assess the size and diversity of his vocabulary. An alternative analysis is proposed based on the inverse Gaussian-zero truncated Poisson mixture model, which is obtained by switching the order of the mixing and the truncation stages. Even though this second model fits some of the word frequency data sets more accurately than the first model, in practice the analysis based on it is not as useful because it does not allow one to estimate the word frequency distribution of the vocabulary.

Stylometric analyses using Dirichlet process mixture models

Stylometry refers to the statistical analysis of literary style of authors based on the characteristics of expression in their writings. We propose an approach to stylometry based on a Bayesian Dirichlet process mixture model using multinomial word frequency data. The parameters of the multinomial distribution of word frequency data are the “word prints” of the author. Our approach is based on model-based clustering of the vectors of probability values of the multinomial distribution. The resultant clusters identify different writing styles that assist in author attribution for disputed works in a corpus. As a test case, the methodology is applied to the problem of authorship attribution involving the Federalist papers. Our results are consistent with previous stylometric analyses of these papers.

The challenge of validation: Assessing the performance of a test of productive vocabulary

This paper assesses the performance of a vocabulary test designed to measure second language productive vocabulary knowledge.The test, Lex30, uses a word association task to elicit vocabulary, and uses word frequency data to measure the vocabulary produced. Here we report firstly on the reliability of the test as measured by a test-retest study, a parallel test forms experiment and an internal consistency measure. We then investigate the construct validity of the test by looking at changes in test performance over time, analyses of correlations with scores on similar tests, and comparison of spoken and written test performance. Last, we examine the theoretical bases of the two main test components: eliciting vocabulary and measuring vocabulary. Interpretations of our findings are discussed in the context of test validation research literature. We conclude that the findings reported here present a robust argument for the validity of the test as a research tool, and encourage further investigation of its validity in an instructional context.

Extended truncated Inverse Gaussian–Poisson model

The inverse Gaussian–Poisson mixture model is very useful when modelling highly skewed non-negative integer data in fields as diverse as linguistics, ecology, market research, bibliometry, engineering and insurance. When using this statistical model on the frequency of word or species frequency data, one typically truncates its sample space at zero to accommodate for the ignorance about the number of words or species that are not observed. In this paper, we show that by truncating the sample space of the inverse Gaussian–Poisson model, one is allowed to extend its parameter space and in that way improve its fit when the frequency of one is larger and the right tail is heavier than is allowed by the unextended model. By fitting the extended model to word frequency count data, we find many instances where the maximum likelihood estimates fall in the extension of the parameter space.

Second language vocabulary assessment: current practices and new directions

This paper surveys some current developments in second language vocabulary assessment, with particular attention to the ways in which computer corpora can provide better quality information about the frequency of words and how they are used in specific contexts. The relative merits of different word lists are discussed, including the Academic Word List and frequency lists derived from the British National Corpus. Word frequency data is needed for measures of vocabulary size, such as the Yes/No format, which is being developed and used for a variety of purposes. The paper also reviews work on testing depth of knowledge of vocabulary, where rather less progress has been made, both in defining depth as a construct and in developing tests for practical use. Another important perspective is the use of vocabulary within particular contexts of use or registers, and recent corpus research is extending our understanding of the lexical features of academic registers. This provides a basis for assessing learners’ ability to deploy their vocabulary knowledge effectively for functional communication in specific academic contexts. It is concluded that, while current tests of vocabulary knowledge are valuable for certain purposes, they need to be complemented by more contextualised measures of vocabulary use.

Developing a Core Vocabulary for a Mandarin Chinese AAC System Using Word Frequency Data

Creating a generative language system in augmentative and alternative communication (AAC) for both literate and non-literate users requires three fundamental design steps: analyzing the grammar of the language; developing an efficient, culturally relevant pictorial representation system for encoding the language; and selecting a list of target words to encode. This paper will focus on the problem of selecting vocabulary for the AAC system. This is an important problem because the selection of the encoded vocabulary will ultimately determine the language coverage of the AAC system. The vocabulary list should provide coverage on three fronts: it should contain the most frequently used vocabulary of the spoken language; it should allow description of all the universal, fundamental semantic concepts; and it should meet users' expectations of what they should be able to say on a daily basis, in any given situation. Mandarin Chinese, as a relatively new target language for AAC, presents some unique challenges to the vocabulary designers. For example, the conceptual differences between the Chinese word and character can be difficult to pin down; this highlights the importance of a consistent and scientific approach to vocabulary selection. This paper describes the research process used in the creation of a core vocabulary list for a new icon-encoded Mandarin Chinese AAC system, starting with a basic corpus-derived word-frequency list, and continuing with the modification and supplementation of this list with other linguistic and anecdotal knowledge.