Dictionary Of Words Research Articles

Peningkatan Kemampuan Membedakan Bahasa Baku dan Tidak Baku Berdasarkan EYD

The standard words are words that follow the recognized language rules in Indonesian. Spelling, conventional grammar, and common dictionary words are just some of the linguistic norms that can be found in standard words. The purpose of this research is to ensure how students use common and uncommon words. This research is qualitative and descriptive. Students majoring in Indonesian Language Education at the State Islamic University of North Sumatra participated in the research. Based on the research findings, most students majoring in Indonesian Language Education at the State Islamic University of North Sumatra are able to understand standard and non-standard words, while very few cannot.

WORD-FORMATION OF NEW VOCABULARY IN THAI DICTIONARY

The increase or decrease in the vocabulary of a language is a natural aspect of language evolution. Word formation refers to the process through which a language incorporates new words and meanings into social usage. Thai is a language known for its diverse methods of forming new words. This article aims to illustrate how new words are formed in Thai. The research data were extracted from the New Words Dictionary, published by the Royal Institute. This study adopts a documentary and qualitative linguistic approach. Rahti and Gusdian’s (2018) word-formation classification framework, based on the Oxford dictionary, serves as a guide for analyzing new words in Thai. The findings reveal 10 distinct methods of forming new words in the Thai language, with compounding being the most prevalent. While this research aligns with Rahti and Gusdian’s framework in 8 processes, it diverges in 2 processes. Specifically, this study did not identify affixation and blending processes but identified sound imitation and sound assimilation processes, which may be unique to Thai word formation. Subsequent research should explore English loanwords in the new words dictionary, categorizing them to uncover their relationships with new meanings in Thai society.

The problem of reflecting the variability of a word in a dialect dictionary (on the material of the “Pskov Regional Dictionary with Historical Data”)

The article is devoted to the problem of reflecting the variability of the Russian dialect word in dialect dictionaries published in Russia from the 19th to the 21st centuries. The study aims to consider the method of presenting different types of variants of a dialect word in various dialect dictionaries, with the main attention being paid to the analysis of works of dialect lexicography created since the middle of the 20th century until now. The submission of variants in the “Pskov Regional Dictionary”, which has been worked on at Leningrad State University — St. Petersburg State University (and Pskov Pedagogical Institute — Pskov State University) since the 1950s, is analyzed in most detail. Research objectives: to study, based on the relevant sections of the introductions and prefaces to dialect dictionaries of the Russian language, what exactly the authors of dictionaries consider to be variants of a dialect word, what types of variants they distinguish and how they are presented in their dictionaries. The novelty of the work lies in the consideration of the problem under study on the basis of a wide range of dialect dictionaries published in different regions of Russia and differently interpreting the theory of variance and describing lexical variation. The material of the study is both the theoretical introductory sections of Russian dialect dictionaries — namely, those parts that are devoted to the variability of the dialect lexeme — and the dialect dictionaries themselves, which differently present certain types of word variants in the dictionary and in a separate dictionary entry.

Processing of emotional connotations in Chinese monomorphic and compound words reflected by the early posterior negativity.

Writing stands as one of humanity's most profound inventions, facilitating the efficient sharing and transmission of vast amounts of information. Similar to images and facial expressions, visual (written) words possess the ability to evoke emotional connotations. Understanding how the brain perceives these emotional nuances encoded in highly symbolic visual words is a key focus of the emerging field of "affective neurolinguistics." At the core of this inquiry lies the examination of the early posterior negativity (EPN), an event-related potentials (ERPs) component peaking around 300 ms after stimulus onset in the occipitotemporal scalp region. EPN has consistently emerged in response to emotional stimuli, encompassing pictures, faces, and visual words. However, prior research has notably lacked observation of EPN in response to Chinese emotional words, raising questions about potential differences in emotional processing between Chinese and other languages. Given the logographic nature of the Chinese writing system and the prevalence of compound words in the Chinese lexicon, this study aims to explore whether the emotional processing of Chinese monomorphic and compound words elicits an EPN response. Two experiments were conducted: Experiment 1 utilized one-character words (monomorphic words), while Experiment 2 employed two-character words (compound words). Participants were assigned a go/no-go task, instructed to respond to unknown words (word recognition task) or blue stimuli (color decision task). Data analysis using a data-driven mass univariate approach revealed significant ERP differences between emotional and neutral words. Notably, the time course, scalp topography, and cortical generators of the difference ERP presented a characteristic EPN response in both experiments. These findings strongly support the notion that the processing of emotional connotations in both Chinese monomorphic and compound words is reflected by the EPN, paving the way for future research using EPN as an emotion-related ERP component for investigating emotional processing of Chinese words.

Enhancing hate speech detection in Indonesian using abusive words lexicon

Hate speech is a major challenge in Indonesia, a diverse country with multiple languages and a dynamic online landscape. This research explores the phenomenon of hate speech and its detection, particularly in language contexts with limited resources. We introduce a new abusive words lexicon, created by collecting words from various sources, adapted for Indonesian, Javanese and Sundanese. Our study investigates the practical implementation of this lexicon. We conducted extensive experiments using different datasets and machine learning models, aiming to improve hate speech detection. The results consistently show a positive impact of the lexicon, which significantly improves detection, especially in languages with fewer resources. But this research paves the way for further exploration. The lexicon can be expanded, broadening its scope. Additionally, we suggest investigating more sophisticated models, such as transformerbased models, to more effectively detect hate speech. In a world where hate speech is a growing problem, our research provides valuable insights and tools to combat it effectively in Indonesia and other countries.

Semi-Supervised Medical Report Generation via Graph-Guided Hybrid Feature Consistency

Medical report generation generates the corresponding report according to the given radiology image, which has been attracting increasing research interest. However, existing methods mainly adopt supervised training which rely on large amount of medical reports that are actually unavailable owing to the labor-intensive labeling process and privacy protection protocol. In the meanwhile, the intrinsic relationships between local pathological changes in the image are often ignored, which actually are important hints to high quality report generation. To this end, we propose a Relation-Aware Mean Teacher (RAMT) framework, which follows a standard mean teacher paradigm for semi-supervised report generation. The key to the encoder of the backbone network is the Graph-guided Hybrid Feature Encoding (GHFE) module, which exploits a prior disease knowledge graph to encode the intrinsic relations between pathological changes into the graph embedding and learns a word dictionary to retrieve the semantic embedding for each potential pathological change. GHFE combines the graph embedding, semantic embedding and visual features to form hybrid features, which are sent to a Transformer-based decoder for report generation. Extensive experiments on the MIMIC-CXR and IU X-Ray datasets demonstrate the effectiveness of our proposed approach.

Potential cyberbullying detection in social media platforms based on a multi-task learning framework

The proliferation of online violence has given rise to a spate of malignant incidents, necessitating a renewed focus on the identification of cyberbullying comments. Text classification lies at the heart of efforts to tackle this pernicious problem. The identification of cyberbullying comments presents unique challenges that call for innovative solutions. In contrast to traditional text classification tasks, cyberbullying comments are often accompanied by subtle and arbitrary expressions that can confound even the most sophisticated classification networks, resulting in low recognition accuracy and effectiveness. To address this challenge, a novel approach is proposed that leverages the BERT pre-training model for word embedding to retain the hidden semantic information in the text. Building on this foundation, the BiSRU++ model which combines attentional mechanisms is used to further extract contextual features of comments. A multi-task learning framework is employed for joint training of sentiment analysis and cyberbullying detection to improve the model's classification accuracy and generalization ability. The proposed model is no longer entirely reliant on a sensitive word dictionary, and experimental results demonstrate its ability to better understand semantic information compared to traditional models, facilitating the identification of potential online cyberbullying comments.

The Case of the Cookie Jar: Differences in Typical Language Use in Dementia.

Findings from language sample analyses can provide efficient and effective indicators of cognitive impairment in older adults. This study used newly automated core lexicon analyses of Cookie Theft picture descriptions to assess differences in typical use across three groups. Participants included adults without diagnosed cognitive impairments (Control), adults diagnosed with Alzheimer's disease (ProbableAD), and adults diagnosed with mild cognitive impairment (MCI). Cookie Theft picture descriptions were transcribed and analyzed using CLAN. Results showed that the ProbableAD group used significantly fewer core lexicon words overall than the MCI and Control groups. For core lexicon content words (nouns, verbs), however, both the MCI and ProbableAD groups produced significantly fewer words than the Control group. The groups did not differ in their use of core lexicon function words. The ProbableAD group was also slower to produce most of the core lexicon words than the MCI and Control groups. The MCI group was slower than the Control group for only two of the core lexicon content words. All groups mentioned a core lexicon word in the top left quadrant of the picture early in the description. The ProbableAD group was then significantly slower than the other groups to mention a core lexicon word in the other quadrants. This standard and simple-to-administer task reveals group differences in overall core lexicon scores and the amount of time until the speaker produces the key items. Clinicians and researchers can use these tools for both early assessment and measurement of change over time.

Fusion of SoftLexicon and RoBERTa for Purpose-Driven Electronic Medical Record Named Entity Recognition

Recently, researchers have extensively explored various methods for electronic medical record named entity recognition, including character-based, word-based, and hybrid methods. Nonetheless, these methods frequently disregard the semantic context of entities within electronic medical records, leading to the creation of subpar-quality clinical knowledge bases and obstructing the discovery of clinical knowledge. In response to these challenges, we propose a novel purpose-driven SoftLexicon-RoBERTa-BiLSTM-CRF (SLRBC) model for electronic medical records named entity recognition. SLRBC leverages the fusion of SoftLexicon and RoBERTa to incorporate the word lexicon information from electronic medical records into the character representations, enhancing the model’s semantic embedding representations. This purpose-driven approach helps achieve a more comprehensive representation and avoid common segmentation errors, consequently boosting the accuracy of entity recognition. Furthermore, we employ the classical BiLSTM-CRF framework to capture contextual information of entities more effectively. In order to assess the performance of SLRBC, a series of experiments on the public datasets of CCKS2018 and CCKS2019 were conducted. The experimental results demonstrate that SLRBC can efficiently extract entities from Chinese electronic medical records. The model attains F1 scores of 94.97% and 85.40% on CCKS2018 and CCKS2019, respectively, exhibiting outstanding performance in the extraction and utilization efficiency of clinical information.

Personal CAPTCHA-based authentication protocol

In recent years, the number of personal accounts assigned to a single user has considerably increased. According recent study, the average employee had above 190 individual login credentials. The most important problems users face with such a large number of passwords are the strength of passwords and the user's ability to remember them. Researchers have shown that, in general, users are aware of what a secure password should look like, but may forgot these security measures in favour of more convenient passwords, depending largely on the type of account (le Bras, 2015). Repeating the same password across multiple platforms or creating it using dictionary words has also proven to be a common practice among many people. In this article, a new password reminder scheme will be presented. The goal was for the user to create a CAPTCHA-like image with a hidden meaning that only the user would be able to reveal. The image must have some connection to the person's experience and memory or unique knowledge. With such an image, presented each time the user logs in, he is asked to associate a password consisting of two or more words and a number. If the image is appropriately selected and associated with a strong association with the person's visual memory, the chances of recalling the long password he created in this way should not be a problem.

Organization of Words in the Mental Lexicon: A Psycholinguistic Study

This study investigated organization of words in the mental lexicon of grade 4 students. For this purpose, one hundred students of grade 4 were selected from the sample schools. This study employed a non-probability convenience sampling technique. The word association test was used to collect the data, which involved selecting 40 words from grade 4 English textbooks taught at three different private sector schools under three respective education systems. This study employed quantitative method to collect the responses of sample students in word association test. The data was analyzed by using Fitzpatrick’s (2007) model of classification of association response. The results showed that position-based association, which accounted for 56.95% of the total collected data dominated the distribution of the data; 17.95% of connections were meaning-based; whereas collocation associations were prevalent in position-based association test. The findings of the word association test showed that the participants’ mental lexicon was impacted by their comprehension and use of the target term. The research concluded that in the participants’ mental lexicon, position-based association was the strongest lexical network. The findings implied that teaching vocabulary and its meanings was insufficient for expanding English language learners’ mental lexicon. This study was helpful for English language teachers to teach English vocabulary based on a proper understanding of the organization of the mental lexicon of English language learners.

A Comparison Analysis of Implicit Chinese Cultural Word Definitions in Export-Oriented Chinese Dictionary Apps

Implicit cultural words in Chinese, such as color terms and numerals embedded with distinct cultural connotations, encompass both conceptual and cultural meanings. The acquisition of these Chinese implicit cultural words is vital yet challenging for learners. Presently, exportoriented Chinese dictionary applications serve as indispensable tools for learning Chinese vocabulary, playing a significant role in mastering implicit cultural words. This study conducts a comparative analysis of implicit cultural word definitions in four widely-used export-oriented Chinese dictionary applications (Pleco, LINE Dict, TrainChinese, and Panda), drawing on the cultural definitions found in the Modern Chinese Dictionary (7th Edition) application. The paper also advocates for the incorporation of standardized criteria, comprehensive definition content, and systematic regulations for implicit cultural words in export-oriented Chinese dictionaries.

A novel meta-embedding technique for drug reviews sentiment analysis

<p>Traditional word embedding models have been used in the feature extraction process of deep learning models for sentiment analysis. However, these models ignore the sentiment properties of words while maintaining the contextual relationships and have inadequate representation for domainspecific words. This paper proposes a method to develop a meta embedding model by exploiting domain sentiment polarity and adverse drug reaction (ADR) features to render word embedding models more suitable for medical sentiment analysis. The proposed lexicon is developed from the medical blogs corpus. The polarity scores of the existing lexicons are adjusted to assign new polarity score to each word. The neural network model utilizes sentiment lexicons and ADR in learning refined word embedding. The refined embedding obtained from the proposed approach is concatenated with original word vectors, lexicon vectors, and ADR feature to form a meta-embedding model which maintains both contextual and sentimental properties. The final meta-embedding acts as a feature extractor to assess the effectiveness of the model in drug reviews sentiment analysis. The experiments are conducted on global vectors (GloVE) and skip-gram word2vector (Word2Vec) models. The empirical results demonstrate the proposed meta-embedding model outperforms traditional word embedding in different performance measures.</p>

Teaching Writing in English in Public Secondary Schools

This paper presents the findings of a descriptive multiple case study carried out with six teachers of English in five public secondary schools in a city in northwestern Mexico aimed to determine how writing was taught. A mixed methods approach was followed with an embedded design in which qualitative data had increased weight and quantitative data was supportive. Qualitative data were collected through an interview guide approach, non-participant observation, and qualitative document analysis, while quantitative supporting data were collected using structured observation and quantitative document analysis. In the qualitative analysis, four themes emerged: 1) writing activities, 2) resources, 3) assessment, and 4) frequency of work on writing. Results suggest that writing activities in which students are required to write one word, a phrase or a sentence predominate. Also, findings show that paper-based resources for writing tasks are frequently used. Qualitative results suggest that written exams, word dictation and class projects were assessment practices assigned by teachers; besides, findings suggest that the writing activities are form-driven rather than meaningdriven. The relevance of this research is that findings might provide the basis for designing quantitative studies in this context that allow for generalizations.

Медійна мова у змінних координатах (скринінг поглядів в українській та польській науці)

The proposed research shows the process of the language dynamic changes in mass communication. The author analyzes the concepts of (media) communication – (media) space – (media) discourse in their evolution, explains the reasons for scientific shifts in the interpretation of these categories, emphasizes new approaches for the analyzed media linguistic phenomena. In order to objectify the scientific picture, the author has chosen authoritative works from Polish and Ukrainian media linguistics, by means of which distinguished is the panorama of the mentioned problem. A separate locus of the research is the monitoring of language innovations over the past five years (Shevchenko L., Syzonov D., Novi slova ta frazeologizmy v ukrainskyh masmedia [New words and phraseologisms / idioms in the Ukrainian mass media: dictionary], Kyiv 2017‑2022) based on material from the Ukrainian mass media. The collected e‑card index of media innovations and the annually published dictionary of new words and phrases helped to analyze the factors that have influenced the linguistic renewal of the Ukrainian language.

Analisis Sentimen Brand Ambassador Artis Korea Selatan pada Produk Indonesia dengan Lexicon

The use of South Korean artists as brand ambassadors for Indonesian products is one way that e-commerce and local companies do to attract consumers. However, it reaped positive and negative responses from the public. The social media used to issue opinions regarding this matter is Twitter. This study conducts sentiment analysis from tweets on Twitter regarding this topic. The method used is Lexicon sentiment. The sentiment Lexicon works by first making a dictionary of opinion words and matching them with the sentiment words contained in the Lexicon dictionary. The Lexicon dictionary used is InSet Lexicon. Sentiment classification is divided into 2 namely, positive and negative. Data collection uses the Twitter API with the keyword "BA Artis Korea" as many as 366 data tweets. The research phase consists of data collection, text preprocessing, Lexicon sentiment, visualization, and testing. The results of the classification using the Lexicon method obtained 67.1% positive and 32.9% negative. It was found that the use of South Korean artists to become brand ambassadors for Indonesian products received positive sentiment. Accuracy, precision, and recall values ​​obtained by looking at the confusion matrix table are 78% accuracy, 68% precision, and 61% recall.

Measuring trust: a text analysis approach to compare, contrast, and select trust questionnaires.

Trust has emerged as a prevalent construct to describe relationships between people and between people and technology in myriad domains. Across disciplines, researchers have relied on many different questionnaires to measure trust. The degree to which these questionnaires differ has not been systematically explored. In this paper, we use a word-embedding text analysis technique to identify the differences and common themes across the most used trust questionnaires and provide guidelines for questionnaire selection. A review was conducted to identify the existing trust questionnaires. In total, we included 46 trust questionnaires from three main domains (i.e., Automation, Humans, and E-commerce) with a total of 626 items measuring different trust layers (i.e., Dispositional, Learned, and Situational). Next, we encoded the words within each questionnaire using GloVe word embeddings and computed the embedding for each questionnaire item, and for each questionnaire. We reduced the dimensionality of the resulting dataset using UMAP to visualize these embeddings in scatterplots and implemented the visualization in a web app for interactive exploration of the questionnaires (https://areen.shinyapps.io/Trust_explorer/). At the word level, the semantic space serves to produce a lexicon of trust-related words. At the item and questionnaire level, the analysis provided recommendation on questionnaire selection based on the dispersion of questionnaires' items and at the domain and layer composition of each questionnaire. Along with the web app, the results help explore the semantic space of trust questionnaires and guide the questionnaire selection process. The results provide a novel means to compare and select trust questionnaires and to glean insights about trust from spoken dialog or written comments.

A lexicographical approach to neologisms created through blending

Blending has long been regarded as an unproductive word-formation process. “Portmanteau”, a word formed through blending, can be traced back to Lewis Carroll’s Through The Looking-glass, published in 1871, but the earliest words created this way were found to be in use in the late Middle English period and many of them did not survive. The number of blends failed to experience any apparent increase in the several centuries that ensued. Since the beginning of the 20th century, blending has been playing an ever-increasing role in forming neologisms. Simonini (1966) found that blend words comprised approximately 3% of new English words. Algeo’s (1991) research into neologisms revealed that about 5% of the words were blends. Among the 1,186 new words analyzed by Cook and Stevenson (2010), around 43% were blends. The growing number of new blends in the English vocabulary means the inclusion of more such words in English dictionaries. In the recent updates of the Oxford English Dictionary (OED), neologisms such as shockvertising, sharenting, Xennial, and staycation are formed through blending. This paper intends to make a thorough research into new blends recorded not only in monolingual English dictionaries such as the OED, Collins English Dictionary, and Merriam-Webster Dictionary but also in bilingual dictionaries such as An English-Chinese Dictionary of Neologisms in Present-day English and A Dictionary of Blends in Contemporary English. The blends found therein will be classified, and the deficiencies in the coverage of blends in dictionaries like the OED will be discussed in detail.

COVID-related new words in French dictionaries

Research at the intersection of neology and lexicography is attracting more and more attention worldwide. Although differences in this respect may be substantial, dictionaries are not always particularly responsive, as far as the inclusion of new words or meanings is concerned. However, major events such as the COVID-19 pandemic have accelerated the process in lexicography for several major European languages. In a fast-changing world, reactivity appears to be a must. However, this new-found enthusiasm for new words does raise some methodological questions, such as the scope of a general dictionary and the criteria for word additions. This article analyzes French lexicographic neologisms related to COVID-19 in Le Petit Larousse Illustré and Le Petit Robert by focusing on lexical incorporation in macrostructures, word-formation processes, and definitions. It also draws comparisons with Wiktionnaire entries, in order to show how responsive and accurate French dictionaries are in recording and describing COVID-related new words.

Applying Optimisation Theory in English Language Teaching in Practice

Abstract This paper firstly researches the optimization of the teaching process and teaching language according to the English teaching objectives and teaching contents and summarizes the optimization strategy of university English teachers’ language so as to improve the efficiency of English teaching. Secondly, on the basis of applying the optimization theory, the formal description of English utterances and word splitting is carried out, followed by obtaining the features of English utterances through the selection function and further constructing the English utterance alignment model by using the particle swarm optimization algorithm. Then, we study the application of English utterance alignment in English teaching, which is mainly embodied in the construction of the English teaching corpus, word frequency list, and core word lexicon, followed by the analysis of the practical application in English teaching. The results show that in terms of algorithm performance, the correct rate of utterance alignment based on the particle swarm optimization algorithm is 0.82, and the correct rate of utterance alignment is also improved to a great extent. On speaking teaching, self-efficacy showed a significant correlation with cognitive strategies, metacognitive strategies, affective strategies, communicative strategies, compensatory strategies, and memory strategies, and their corresponding coefficient values were 0.652, 0.716, 0.741, 0.732, 0.718, and 0.748, which indicated that the better self-efficacy was, the higher the scores of speaking memory strategies were.