Word Frequency Distributions Research Articles

A Quantitative Study on the Linguistic Features of the Tujia Epic Sheba Songs

Previous research on the Tujia epic Sheba Songs has primarily relied on qualitative methods, which are less conducive to objectively examining the texts features. This paper introduces quantitative research methods to explore the textual characteristics of Sheba Songs and compares its features with those of regulated verse, modern Chinese poetry, and Chinese folk songs. The structure of the study is as follows: (1) From the perspective of word frequency distribution, Zipfs law is employed to verify the naturalness of the Sheba Songs text. (2) From the perspective of part-of-speech probability distribution, clustering indices reveal that, compared to regulated verse, the stylistic features of Sheba Songs are closer to those of modern Chinese poetry. (3) Clustering experiments using relevant clustering indices confirm the inclination of Sheba Songs toward the characteristics of folk balladization.

MAFD: Multiple Adversarial Features Detector for Enhanced Detection of Text-Based Adversarial Examples

Adversarial attacks in the field of Natural Language Processing greatly undermine the effectiveness and safety of models, raising significant challenges when it comes to real-world implementation. The researchers suggested using detection methods to identify and reject hostile samples while maintaining the accuracy of the original model. Nevertheless, current detection methods depend on analyzing a single characteristic, resulting in restricted resilience and flexibility. To address these constraints, we proposed the Multiple Adversarial Features Detector (MAFD), an innovative detection technique that utilizes a wide range of adversarial features, such as segmented perplexity, word frequency, and probability distribution, to enhance the effectiveness of detecting adversarial examples. Our comprehensive experiments shows that MAFD outperforms existing advanced methods in terms of detection accuracy and displays significant robustness and adaptability when applied to various base detectors and attack scenarios. In addition, the design of MAFD facilitates the seamless integration of further adversarial features, hence enhancing its detection capabilities.

Text mining approach in chicken meat welfare

The aim of the study was to analyze the aspects affecting broiler welfare with the use of Text Mining technique. This approach converts text into numerical data and analyzes word frequency distributions, enabling the extraction of useful information and the identification of relationships between elements. Text mining has limitations, i.e. ambiguity and context sensitivity, making it difficult to capture nuanced meanings. The search was carried out with Scopus using the term "Welfare" with the keywords "Chicken", "Broiler", "Broiler chicken", and "Chicken meat", to create a database of abstracts. Text Mining and Topic Analysis were performed on the abstracts (1228 documents) using the Software R 4.3.1., analyzing also the weight of bigram and trigram. Publications on broiler welfare are present in the bibliography since 1990's, but in the last 10 years, for the interest of public opinion, the numbers of publications significantly increased (76.5% of all documents published). USA, Brazil, and Europe-27 published 60% of the documents found. The works were published in a high number of journals, but 37% of them are published in only 4 journals (Poultry Science, Animals, Applied Animal Behavior Science and Animal Welfare). Text Mining analysis identified key terms related to the slaughter phase, housing management, and environmental conditions such as light quality and quantity. Moreover, a high correlation was found between some terms, underlying the importance of the effects of rearing, slaughter phases and litter management on broiler welfare. Most of the countries focused their research on some specific topics identified by Topic Analysis, mainly genetic selection, feeding, stocking density, slaughter, and consumer perceptions. Poultry Science published the highest number of papers (18%) and the topics more investigated were growing performance, transport and slaughter, and litter management. In conclusion, the high number of publications on chicken welfare underlines the importance of broiler welfare both in Europe and in other countries, even if it is difficult to identify common research topics among the geographic areas and the evolution over the time.

Zipfian distributions facilitate children's learning of novel word-referent mappings

The word-frequency distributions children hear during language learning are highly skewed (Zipfian). Previous studies suggest that such skewed environments confer a learnability advantage in tasks that require the learner to discover the units that have to be learned, as in word-segmentation or cross-situational learning. This facilitative effect has been attributed to contextual facilitation from high frequency items in learning lower frequency items, and to better learning under the increased predictability (lower entropy) of skewed distributions. Here, we ask whether Zipfian distributions facilitate learning beyond the discovery of units, as expected under the predictability account. We tested children's learning of novel word-referent mappings in a learning task where each mapping was presented in isolation during training, and did not need to be dicovered. We compared learning in a uniform environment to two skewed environments with different entropy levels. Children's learning was overall better in the two skewed environments, even for low frequency items. These results extend the facilitative effect of Zipfian distributions to additional learning tasks and show they can facilitate language learning beyond the discovery of units.

Frequency Analysis, Distribution, and Coverage of Academic Words in Materials Science Research Articles: A Corpus-Based Study

This study explored words in materials science research articles using New Academic Word List (NAWL) and New General Service List (NGSL) created by Browne at al. (2013) as reference lists. The objectives of this research were to explore the frequency and coverage of the NAWL and the NGSL in materials science research articles and identify frequently occurring academic words in materials science research articles that were not included in the NAWL and the NGSL. The corpus used for the analysis was compiled from 115 research articles published in five international journals related to the field of materials science. The findings revealed that the coverage of the NAWL words in the corpus of the present study was at 6.18 percent, together with the NGSL, the cumulative coverage of both word lists in the corpus of the present study was at 75.97 percent, and that there were 356 content words which did not appear in both the NAWL and the NGSL but did occur frequently in the corpus of materials science research articles. The pedagogical implications of these findings are discussed in relation to teaching academic vocabulary to EFL learners of materials science who read and publish academic articles in English.

RESEARCH INTERESTS OF RUSSIAN SCIENTISTS IN THE FIELD OF LEADERSHIP IN MANAGEMENT: PUBLICATION ON CONTENT ANALYSIS

The article contains the results of a content analysis of scientific publications for 2023, dedicated to leadership in management, governance and management, related to the topic “Economics. Economic Sciences". The material for the analysis was scientific publications posted on the portal of the scientific electronic library eLIBRARY.RU, and the analysis was carried out using Python libraries. The article discusses the problem of finding the most relevant areas of research by Russian authors studying leadership in management in 2023. The idea is substantiated that with the help of statistical analysis of the frequency of use of words and phrases (bigrams and trigrams) it is possible to identify the most popular topics of leadership in management among researchers. To conduct the study, 61 scientific publications were used. The frequency of use of words and phrases (bigrams and trigrams) was analyzed to identify the most popular topics. It was revealed that the attention of researchers dealing with leadership in management was related to issues of organization, system, state, modernity and role. Determined: frequently encountered topics in research turned out to be problems in the field of studying public administration, as well as the roles and tools of leadership in management. Compiled: a graph of word frequency distribution (without the words “leadership”, “management”, “management”, “management”) and a cloud of frequently occurring words. The practical significance of the study has been proven: when reading this material, you can see on which topics on leadership in management/governance/governance the largest number of works have been published, and which topics have been studied to a lesser extent. The results of the study can serve as the basis for a number of subsequent studies related to leadership in management/management and management, reproducing the method used in the work.

Word frequency and cognitive effort in turns-at-talk: turn structure affects processing load in natural conversation.

Frequency distributions are known to widely affect psycholinguistic processes. The effects of word frequency in turns-at-talk, the nucleus of social action in conversation, have, by contrast, been largely neglected. This study probes into this gap by applying corpus-linguistic methods on the conversational component of the British National Corpus (BNC) and the Freiburg Multimodal Interaction Corpus (FreMIC). The latter includes continuous pupil size measures of participants of the recorded conversations, allowing for a systematic investigation of patterns in the contained speech and language on the one hand and their relation to concurrent processing costs they may incur in speakers and recipients on the other hand. We test a first hypothesis in this vein, analyzing whether word frequency distributions within turns-at-talk are correlated with interlocutors' processing effort during the production and reception of these turns. Turns are found to generally show a regular distribution pattern of word frequency, with highly frequent words in turn-initial positions, mid-range frequency words in turn-medial positions, and low-frequency words in turn-final positions. Speakers' pupil size is found to tend to increase during the course of a turn at talk, reaching a climax toward the turn end. Notably, the observed decrease in word frequency within turns is inversely correlated with the observed increase in pupil size in speakers, but not in recipients, with steeper decreases in word frequency going along with steeper increases in pupil size in speakers. We discuss the implications of these findings for theories of speech processing, turn structure, and information packaging. Crucially, we propose that the intensification of processing effort in speakers during a turn at talk is owed to an informational climax, which entails a progression from high-frequency, low-information words through intermediate levels to low-frequency, high-information words. At least in English conversation, interlocutors seem to make use of this pattern as one way to achieve efficiency in conversational interaction, creating a regularly recurring distribution of processing load across speaking turns, which aids smooth turn transitions, content prediction, and effective information transfer.

Harnessing Text and Web Analytics to Enhance Decision-Making in Job Opportunity Categorization

Text analytics is defined as a method of analyzing compilations of structured text such as dates, times, locations, semi structured text, such as HTML and JSON as well as unstructured text, such as word documents, videos, and images, to extract and discover trends and relationships without requiring the exact words or terms to convey those concepts. Web analytics on the other hand is the technology that collects, measures, analyses, and provides reports of data on how users use websites and web applications. It is used to track a number of aspects of direct user-website interactions, such as the number of visits, time spent on the site, and click pathway. It also aids in the identification of user interest areas and the enhancement of web application features. We used clustering techniques to categorize the job opportunities that are available for the job seekers. By implementing text analytics, text data may be grouped with the goal of providing outcomes in the form of word frequency distribution, pattern identification, and predictive analytics. Text analytics may create one-of-a-kind values to use in the improvement of decision-making and business processes, as well as the development of new business models.

Managing the E-commerce Data Deluge through Text Analytics and Web Management (Overview of Amazon.com)

Today, more than 80% of the big data handled in the e-commerce industry is text and unstructured data. Text analytics is an automated process for analyzing text and extracting useful information from it. It can discover trends and relationships in data. Web analytics is the collection, processing, and analysis of data in order to draw conclusions to optimize usability on a website. Web analytics can be used to improve the usability of a site by analyzing user behavior patterns such as time spent on the site, abandonment rates, most frequently accessed products, click-through rates, etc. It can also help analyze the interests of different user demographics, as it tracks granular details such as user demographics, age and gender, geography, and devices used as data. In order to obtain UpToDate information, the business can utilize business intelligence for real-time data processing, then they can practice stream analysis to analyse continuous flow of data. For instance, the business can collect instant information in Twitter or other social media and analyse it by using social media analysis. For website management, business can practice web analysis to analyse the customer’s behaviours. Tracking the customer’s activity, page view and conversion rate is important for business to analyse how to improve the website performance. Text analytics of comments received on Amazon can be used to group text data and produce results in terms of word frequency distribution and sentiment analysis. Text analytics could be used for decision making, improving service quality, and developing new business models.

Evaluation of word embedding models used for diachronic semantic change analysis

In the last decade, the quantitative analysis of diachronic changes in language and lexical semantic changes have become the subject of active research. A significant role was played by the development of new effective techniques of word embedding. This direction has been effectively demonstrated in a number of studies. Some of them have focused on the analysis of the optimal type of word2vec models, hyperparameters for training, and evaluation techniques. In this research, we used Corpus of Historical American English (COHA). The paper demonstrates the results of multiple training runs and the comparison of word2vec models with different variations of hyperparameters used for lexical semantic change detection. In addition to traditional word similarities and analogical reasoning tests, we used testing on an extended set of synonyms. We have evaluated word2vec models on the set of more than 100,000 English synsets that were randomly selected from the WordNet database. We have shown that changing the word2vec model parameters (such as a dimension of word embedding, a size of context window, a type of model, a word discard rate etc.) can significantly impact on the resulting word embedding vector space and the detected lexical semantic changes. Additionally, the results strongly depended on properties of the corpus, such as word frequency distribution.

Identifying the Patterns of Author-Generated Tags to Library and Information Science Papers in The Academic Social Networks: Focusing on Academia.edu

This research aims to identify some patterns of author (as user) generated tags to the papers of library and information science field in Academia.edu. The research method is typically based on text analysis and word frequency distribution. The population contains over 6000 papers tagged in Academia.edu, and their abstracts were extracted from 159 English journals of the library and information science (LIS) field in the Scopus database. The growth of different types of tags in terms of the number of their words (one-word, two-word, three-word, and four-word and more), as well as the total number of tags over time, appeared as a logistic curve. It was also found that two-word tags had the most matching (54.92%) and four-word tags or more the least matching (1.76%) with different sections of papers (title, abstract, and authors’ keywords). The total tags matched 7.5% with the title, 76.61% with the abstract, and 15.89% with the authors’ keywords. Regarding the reuse of tags, it was revealed that on the one hand, 38.8% of the tags had been reused; on the other hand, 16% of the tags were reused in the first year, and more than 50% of the tags were reused in the first three years. Finally, it can be said that the users’ consensus on specific terms can identify the new patterns of users’ tagging at least partially compatible with professional indexing concepts, and by focusing on the most widely used tags and their sustainable distribution, the weighting of indexing terms and even classification schemes may be achieved.

Evidence of Zipfian distributions in three sign languages

One striking commonality between languages is their Zipfian distributions: A power-law distribution of word frequency. This distribution is found across languages, speech genres, and within different parts of speech. The recurrence of such distributions is thought to reflect cognitive and/or communicative pressures and to facilitate language learning. However, research on Zipfian distributions has mostly been limited to spoken languages. In this study, we ask whether Zipfian distributions are also found across signed languages, as expected if they reflect a universal property of human language. We find that sign frequencies and ranks in three sign language corpora (BSL, DGS and NGT) show a Zipfian relationship, similar to that found in spoken languages. These findings highlight the commonalities between spoken and signed languages, add to our understanding of the use of signs, and show the prevalence of Zipfian distributions across language modalities, supporting the idea that they facilitate language learning and communication.

Word length and frequency effects on text reading are highly similar in 12 alphabetic languages

Reading research robustly finds that shorter and more frequent words are recognized faster and skipped more often than longer and less frequent words. An empirical question that has not been tested yet is whether languages within the same writing system would produce similarly strong length and frequency effects or whether typological differences between written languages would cause those effects to vary systematically in their magnitude. We analyzed text reading eye-movement data in 12 alphabetic languages from the Multilingual Eye-Movement Corpus (MECO). The languages varied substantially in their word length and frequency distributions as a function of their orthographic depth and morpho-syntactic type. Yet, the effects of word length and frequency on fixation durations and skipping rate were highly similar in size between the languages. This finding suggests a high degree of cross-linguistic universality in the readers’ behavioral response to linguistic complexity (indexed by word length) and the amount of experience with the word (indexed by word frequency). These findings run counter to influential theories of single word recognition, which predict orthographic depth of a language to modulate the size of these benchmark effects. They also facilitate development of cross-linguistically generalizable computational models of eye-movement control in reading.

Quantifying the Dissimilarity of Texts

Quantifying the dissimilarity of two texts is an important aspect of a number of natural language processing tasks, including semantic information retrieval, topic classification, and document clustering. In this paper, we compared the properties and performance of different dissimilarity measures D using three different representations of texts—vocabularies, word frequency distributions, and vector embeddings—and three simple tasks—clustering texts by author, subject, and time period. Using the Project Gutenberg database, we found that the generalised Jensen–Shannon divergence applied to word frequencies performed strongly across all tasks, that D’s based on vector embedding representations led to stronger performance for smaller texts, and that the optimal choice of approach was ultimately task-dependent. We also investigated, both analytically and numerically, the behaviour of the different D’s when the two texts varied in length by a factor h. We demonstrated that the (natural) estimator of the Jaccard distance between vocabularies was inconsistent and computed explicitly the h-dependency of the bias of the estimator of the generalised Jensen–Shannon divergence applied to word frequencies. We also found numerically that the Jensen–Shannon divergence and embedding-based approaches were robust to changes in h, while the Jaccard distance was not.

American cultural regions mapped through the lexical analysis of social media

Cultural areas represent a useful concept that cross-fertilizes diverse fields in social sciences. Knowledge of how humans organize and relate their ideas and behavior within a society can help us to understand our actions and attitudes toward different issues. However, the selection of common traits that shape a cultural area is somewhat arbitrary. What is needed is a method that can leverage the massive amounts of data coming online, especially through social media, to identify cultural regions without ad-hoc assumptions, biases, or prejudices. This work takes a crucial step in this direction by introducing a method to infer cultural regions based on the automatic analysis of large datasets from microblogging posts. The approach presented here is based on the principle that cultural affiliation can be inferred from the topics that people discuss among themselves. Specifically, regional variations in written discourse are measured in American social media. From the frequency distributions of content words in geotagged tweets, the regional hotspots of words’ usage are found, and from there, principal components of regional variation are derived. Through a hierarchical clustering of the data in this lower-dimensional space, this method yields clear cultural areas and the topics of discussion that define them. It uncovers a manifest North–South separation, which is primarily influenced by the African American culture, and further contiguous (East–West) and non-contiguous divisions that provide a comprehensive picture of modern American cultural areas.

Comparison of open education research between China and other countries from knowledge graph perspective

Based on the Chinese Social Science Citation Index (CSSCI) and Social Science Citation Index (SSCI), this paper uses visualization software CiteSpace to draw the knowledge graph, and makes a bibliometric analysis of open education research between China and other countries from 1998 year to 2020 year. Keywords co-occurrence, word frequency distribution and keyword clustering are used to analyze hot topics and research trends. The research finds that research hotspots mainly focus on the current situation, practical achievements, sustainable development of open education resources, the design of open curriculum and analysis of learner characteristics. The paper points out that the current open education research lacks of in-depth research on resource evaluation, and it should be strengthened to sustainable development of open education resources in the future.

Clustering Highly Divergent Homologous Proteins: An Alignment‐Free Method

The comparative analysis of amino acid sequences is an important tool in molecular biology that often requires multiple sequence alignments. In comparisons between less closely related genomes, however, it becomes more difficult to accurately align protein-coding sequences, or even to identify homologous regions in different genomes. In this article, we describe an alignment-free method for the classification of homologous protein-coding regions from different genomes. This methodology was originally developed for comparing genomes within virus families, but may be adapted for other organisms. We quantify sequence homology from the overlap (intersection distance) of the k-mer (word) frequency distributions for different protein sequences. Next, we extract groups of homologous sequences from the resulting distance matrix using a combination of dimensionality reduction and hierarchical clustering methods. Finally, we demonstrate how to generate visualizations of the composition of clusters with respect to protein annotations, and by coloring protein-coding regions of genomes by cluster assignments. These provide a useful means to quickly assess the reliability of the clustering results based on the distribution of homologous genes among genomes. © 2023 Wiley Periodicals LLC. Basic Protocol 1: Data collection and processing Basic Protocol 2: Calculating k-mer distances Basic Protocol 3: Extracting clusters of homology Support Protocol: Genome plot based on clustering results.

Zipfian Distributions in Child-Directed Speech.

Across languages, word frequency and rank follow a power law relation, forming a distribution known as the Zipfian distribution. There is growing experimental evidence that this well-studied phenomenon may be beneficial for language learning. However, most investigations of word distributions in natural language have focused on adult-to-adult speech: Zipf's law has not been thoroughly evaluated in child-directed speech (CDS) across languages. If Zipfian distributions facilitate learning, they should also be found in CDS. At the same time, several unique properties of CDS may result in a less skewed distribution. Here, we examine the frequency distribution of words in CDS in three studies. We first show that CDS is Zipfian across 15 languages from seven language families. We then show that CDS is Zipfian from early on (six-months) and across development for five languages with sufficient longitudinal data. Finally, we show that the distribution holds across different parts of speech: Nouns, verbs, adjectives and prepositions follow a Zipfian distribution. Together, the results show that the input children hear is skewed in a particular way from early on, providing necessary (but not sufficient) support for the postulated learning advantage of such skew. They highlight the need to study skewed learning environments experimentally.

Interpretable Bias Mitigation for Textual Data: Reducing Genderization in Patient Notes While Maintaining Classification Performance

Medical systems in general, and patient treatment decisions and outcomes in particular, can be affected by bias based on gender and other demographic elements. As language models are increasingly applied to medicine, there is a growing interest in building algorithmic fairness into processes impacting patient care. Much of the work addressing this question has focused on biases encoded in language models—statistical estimates of the relationships between concepts derived from distant reading of corpora. Building on this work, we investigate how differences in gender-specific word frequency distributions and language models interact with regards to bias. We identify and remove gendered language from two clinical-note datasets and describe a new debiasing procedure using BERT-based gender classifiers. We show minimal degradation in health condition classification tasks for low- to medium-levels of dataset bias removal via data augmentation. Finally, we compare the bias semantically encoded in the language models with the bias empirically observed in health records. This work outlines an interpretable approach for using data augmentation to identify and reduce biases in natural language processing pipelines.

Word frequency effects found in free recall are rather due to Bayesian surprise.

The inconsistent relation between word frequency and free recall performance (sometimes a positive one, sometimes a negative one, and sometimes no relation) and the non-monotonic relation found between the two cannot all be explained by current theories. We propose a theoretical framework that can explain all extant results. Based on an ecological psychology analysis of the free recall situation in terms of environmental and informational resources available to the participants, we propose that because participants’ cognitive system has been shaped by their native language, free recall performance is best understood as the end result of relational properties that preexist the experimental situation and of the way the words from the experimental list interact with those. In addition to this, we borrow from predictive coding theory the idea that the brain constantly predicts “what is coming next” so that it is mainly prediction errors that will propagate information forward. Our ecological psychology analysis indicates there will be “prediction errors” because the word frequency distribution in an experimental word list is inevitably different from the particular Zipf’s law distribution of the words in the language that shaped participants’ brains. We further propose the particular distributional discrepancies inherent to a given word list will trigger, as a function of the words that are included in the list, their order, and of the words that are absent from the list, a surprisal signal in the brain, something that is isomorphic to the concept of Bayesian surprise. The precise moment when Bayesian surprise is triggered will determine to what word of the list that Bayesian surprise will be associated with, and the word the Bayesian surprise will be associated with will benefit from it and become more memorable as a direct function of the magnitude of the surprisal. Two experiments are presented that show a proxy of Bayesian surprise explains the free recall performance and that no effect of word frequency is found above and beyond the effect of that proxy variable. We then discuss how our view can account for all data extant in the literature on the effect of word frequency on free recall.