Standard Word Research Articles

Quizizz as Indonesian Learning Media During The COVID-19 Pandemic

During the COVID-19 epidemic, this research aimed to improve students' fundamental Indonesian knowledge using Quizizz as a game-based learning tool. Description qualitative research technique. The study sample consisted of 33 students from the first semester of the Indonesian Language and Literature Education Study Program. The research instrument is multiple-choice questions about standard and non-standard words and Indonesian writing, which is included in the quizizz website. The data analysis technique was carried out in four stages; 1) data collection, data reduction; data presentation; and concluding. Based on the analysis results, it was found that the quizizz learning media in learning Indonesian can increase student interest in learning and is very effective in increasing knowledge of writing or spelling Indonesian and standard Indonesian grammar. Quizizz also prepares lecturers and students for global education expectations. Online interactive games can help enhance language education and boost digital literacy among the youth, both of which are essential for the fourth industrial revolution.

Analysis of Language Errors in the “Larise” Magazine Article at SMP Negeri 8 Surakarta

This study aims to analyze language errors and their interpretations at the level of syntax, morphology, and Indonesian Spelling System as well as to increase knowledge and insight about how to write properly and correctly according to the language rules that have been regulated in the KBBI and PUEBI. This research uses descriptive qualitative research method. The data in this study are in the form of words (not numbers) sourced from Larise magazine in an article entitled "Philosophy of Kidungan Jawa "Ana Kidung Rumeksa ing Wengi" published on Sunday, October 11, 2020. The data collection technique in this study is a note-taking technique, namely: by reading Larise magazine as a data source. The analysis used in this study is an interactive analysis which includes the steps of a) data collection, b) error identification, c) error explanation, d) error classification, and e) error evaluation. In this study, an analysis of errors in writing rules was carried out at the level of syntax, morphology, and accuracy in the use of Indonesian Spelling (EBI). Errors at the syntactic level are in the form of errors in the use of effective sentences, errors at the morphological level are affixation errors, and errors related to Indonesian spelling include errors in using punctuation marks, using capital letters, using standard words, using prepositions, and using particles

DeIDNER Model: A Neural Network Named Entity Recognition Model for Use in the De-identification of Clinical Notes.

Clinical named entity recognition (NER) is an essential building block for many downstream natural language processing (NLP) applications such as information extraction and de-identification. Recently, deep learning (DL) methods that utilize word embeddings have become popular in clinical NLP tasks. However, there has been little work on evaluating and combining the word embeddings trained from different domains. The goal of this study is to improve the performance of NER in clinical discharge summaries by developing a DL model that combines different embeddings and investigate the combination of standard and contextual embeddings from the general and clinical domains. We developed: 1) A human-annotated high-quality internal corpus with discharge summaries and 2) A NER model with an input embedding layer that combines different embeddings: standard word embeddings, context-based word embeddings, a character-level word embedding using a convolutional neural network (CNN), and an external knowledge sources along with word features as one-hot vectors. Embedding was followed by bidirectional long short-term memory (Bi-LSTM) and conditional random field (CRF) layers. The proposed model reaches or overcomes state-of-the-art performance on two publicly available data sets and an F1 score of 94.31% on an internal corpus. After incorporating mixed-domain clinically pre-trained contextual embeddings, the F1 score further improved to 95.36% on the internal corpus. This study demonstrated an efficient way of combining different embeddings that will improve the recognition performance aiding the downstream de-identification of clinical notes.

ZHÂHIRAH AL-MAZJI AL-LUGHAWÎ FÎ MUJTAMA’ DZAWÎ AL-USHÛL AL-‘ARABIYAH FÎ INDONESIA: KUNHAHÂ WA MABNÂHÂ WA DALÂLATUHÂ

This article aims to discuss the phenomenon of code blending in society of Arab descent in Indonesia. By using ethnographic research methods, samples of data collected through direct personal interviews was analyzed. The results of the data analysis show that the phenomenon of code blending is rife in their conversations, something that is clearly visible in the mixing of Arabic with Indonesian. So that, they replace nouns with verbs, plural words with singular words, standard words with non-standard, and use the inflection Arabic for Indonesian and vice versa, so that the form and structure of the two languages are changed. This study also shows that this phenomenon is caused by a lack of mastery of Arabic and Indonesian by speakers, continuous communication in this way between families, because parents pass on Arabic and Indonesian vocabulary to their children and do not pass on their forms and structure of the sentence, or there is always the occurrence language contact in various situations such as schools and workplaces.

DAMPAK PANDEMI COVID-19 TERHADAP KESALAHAN BERBAHASA PADA TEKS ILMIAH MAHASISWA IAIN BONE

The study aims to describe the error of Indonesian done the student at covid-19 pandemic. This research is a descriptive type. The source of the data in this study are the student of IAIN Bone. The instruments used in this research include observation sheets, reading, and note-taking techniques. Analytical techniques used in analyzing data-error language i.e. qualitative data analysis techniques. The results of this research show that there were errors in using Indonesian, which include standard words, phonological, morphological, syntactic, and percentage of the language error diagram. The advice of this research, the lecturer or the teacher can use the results of the analysis on language errors made by students as reflection of learning.

Creating sustainability reports that matter: an investigation of factors behind the narratives

PurposeThis paper aims to provide a novel approach to examine sustainability report narratives by considering key features of these narratives including, forward-looking content, risk content, tone and sustainability-specific content.Design/methodology/approachUsing a sample of UK firms' sustainability reports from 2014 to 2018, the authors capture the report content by compiling a collection of words using a computational linguistic technique that attempts to identify specific attributes of sustainability reports.FindingsThe findings show the main factors that determine the content of sustainability reports are: (1) external governance-related factors, including the voluntary adoption of sustainability reporting assurance, the choice of assurance provider, stakeholder engagement and ownership concentration; (2) internal governance factors, including board quality and the existence of a sustainability committee; and (3) reporting behaviour including the publication of standardised Global Reporting Initiative (GRI) sustainability reports and financial reporting quality.Research limitations/implicationsThe authors limit our sample to companies operated in the UK. Future research can explore the results in other institutional contexts such as North America or Asia–Pacific where the governance of sustainability reporting and other factors determining the content of sustainability reports could be different. Also, it would be interesting to interview managers and other stakeholders to obtain their opinions with regard to sustainability reporting and assurance practices and to understand their opinions regarding the GRI guidelines and its appropriateness. This study combines different research streams to advance our understanding of sustainability disclosures and factors that determine sustainability narratives.Practical implicationsCorporate managers need to strengthen their internal and external governance mechanisms to enhance the comprehensiveness and credibility of sustainability reports and are encouraged to engage stakeholders in the sustainability reporting process. Policymakers can mandate the assurance of sustainability reports and establish reporting formats and standard words to control the tone of sustainability reports. Finally, researchers, professionals as well as policymakers need to monitor sustainable development goals and targets to increase awareness, knowledge and practices that can be operationalised to ensure a global society that can afford sustainable living.Originality/valueTo the best of our knowledge, no study has yet examined sustainability report narratives by considering key features of these reports, including forward-looking content, risk content, tone and sustainability-specific content.

The Demands of Simple and Complex Arithmetic Word Problems on Language and Cognitive Resources.

Solving arithmetic word problems requires constructing a situation model based on the problem text and translating that into a mathematical model. As such, word problem solving makes demands on students’ language comprehension and their domain-general cognitive resources. These demands may decrease when students get more experienced and use strategies that do not require fully understanding the situation presented in the problem. The current study aims to address this hypothesis. Students (N=444) from third to sixth grade solved a paper-and-pencil task with 48 mathematics problems, comprising symbolic arithmetic problems and standard word problems, as well as more complex word problems that involve two arithmetic steps or include irrelevant numerical information. Their performance was analyzed with multilevel logistic regression analyses. Results showed that within each grade, performance on the different problem types did not differ, suggesting that already in third-grade students seem helped nor hindered by presenting arithmetic problems in a story, even if that story contains irrelevant numerical information. Non-verbal reasoning was more important in standard word problems than in arithmetic problems in symbolic format in one-step arithmetic, and reading comprehension was more important in solving two-step arithmetic word problems than in one-step arithmetic word problems.

ArGoT: A Glossary of Terms extracted from the arXiv

We introduce ArGoT, a data set of mathematical terms extracted from the articles hosted on the arXiv website. A term is any mathematical concept defined in an article. Using labels in the article's source code and examples from other popular math websites, we mine all the terms in the arXiv data and compile a comprehensive vocabulary of mathematical terms. Each term can be then organized in a dependency graph by using the term's definitions and the arXiv's metadata. Using both hyperbolic and standard word embeddings, we demonstrate how this structure is reflected in the text's vector representation and how they capture relations of entailment in mathematical concepts. This data set is part of an ongoing effort to align natural mathematical text with existing Interactive Theorem Prover Libraries (ITPs) of formally verified statements.

The Southwestern Mandarin /n/-/l/ Merger: Effects on Production in Standard Mandarin and English

Southwestern Mandarin is one of the most important modern Chinese dialects, with over 270 million speakers. One of its most noticeable phonological features is an inconsistent distinction between the pronunciation of (n) and (l), a feature shared with Cantonese. However, while /n/-/l/ in Cantonese has been studied extensively, especially in its effect upon English pronunciation, the /l/-/n/ distinction has not been widely studied for Southwestern Mandarin speakers. Many speakers of Southwestern Mandarin learn Standard Mandarin as a second language when they begin formal schooling, and English as a third language later. Their lack of /l/-/n/ distinction is largely a marker of regional accent. In English, however, the lack of a distinction risks loss of intelligibility because of the high functional load of /l/-/n/. This study is a phonetic investigation of initial and medial (n) and (l) production in English and Standard Mandarin by speakers of Southwestern Mandarin. Our goal is to identify how Southwestern Mandarin speakers produce (n) and (l) in their additional languages, thus providing evidence for variations within Southwestern Mandarin and identifying likely difficulties for L2 learning. Twenty-five Southwestern Mandarin speakers recorded English words with word initial (n) and (l), medial <ll> or <nn> spellings (e.g., swallow, winner), and word-medial (nl) combinations (e.g., only) and (ln) combinations (e.g., walnut). They also read Standard Mandarin monosyllabic words with initial (l) and (n), and Standard Mandarin disyllabic words with (l) or (n). Of the 25 subjects, 18 showed difficulties producing (n) and (l) consistently where required, while seven (all part of the same regional variety) showed no such difficulty. The results indicate that SWM speakers had more difficulty with initial nasal sounds in Standard Mandarin, which was similar to their performance in producing Standard Mandarin monosyllabic words. For English, production of (l) was significantly less accurate than (n), and (l) production in English was significantly worse than in Standard Mandarin. When both sounds occurred next to each other, there was a tendency toward producing only one sound, suggesting that the speakers assimilated production toward one phonological target. The results suggest that L1 influence may differ for the L2 and L3.

Pergeseran Bahasa Baku: Ragam Bahasa Elitis dalam Akun Instagram Humor Recehku

The purpose of this study is to describe elitist language on the Humor Recehku Instagram account, namely (1) non-standard word forms; (2) abbreviated forms that cause non-standard; and (3) word forms from foreign languages. The method used in this research is the descriptive method. The data collection technique was done by observing and taking notes. Data analysis used Miles and Huberman qualitative analysis, namely data reduction, data presentation and drawing conclusions and levers. This research leads to the content analysis “content analysis”. The data in this study are in the form of words, phrases, and sentences that experience language shifts in an elitist form. The results of this study indicate that there is a language shift in the case of using elitist language in the Humor Recehku Instagram account. First, non-standard word forms include non-standard words without a play (emang, gini, telfon, sampe, ngerasa dan sebagainya); non-standard words with a pun (Oktrouble dan gw); and non-standard words weakening phonemes (males, denger, bener-bener, ye, cakep, anjeng, kecapean, dan temen). Second, abbreviations that cause non-standard abbreviated standard words (sm, org, krn, dan trs); and abbreviated non-standard words (not and yesterday2). Third, foreign language words are divided into (typing, excuse, mood and so on); phrase (self-reminder, close friend IG); and sentences (is the best kind of sleep, oh well honey, you keep your small circle, and friend with many but trust any).

Two-fold complex network approach to discover the impact of word-order in Urdu language

<div>This work examines standard Urdu text to confirm impact of word order in the language structure. The complex network approach is used to obtain universal properties of two different word co-occurrence networks. Macro and micro scale two-fold examinations of networks are performed for structure discovery. While preserving the vocabulary size, two networks are generated from same text with and without standard word order. In addition, text networks are benchmarked with a random network to extract global features. Achieved outcomes indicate certain word order in Urdu structure for most of the sentences. The normal and shuffled text networks demonstrated similar large-scale characteristics. The results show that average path length and network diameter is reduced after shuffling. On the other hand, clustering coefficient is increased in shuffled text as compared to normal text. Our results validated that few short sentences in range of three words are fully free order. The observations revealed that long sentences are ambiguous without standard order. Both networks are topologically similar but shuffling caused massive discrepancy in network composition and sentence structure. Inside graph view, grammatical association-based words connectivity exists in normal text network. With this universal approach, impact of word order in Urdu language is confirmed. Meanwhile, this breakthrough directs to uncover language composition by extracting small sentences as motifs.</div>

Customer sentiment analysis with more sensibility

Customers’ evaluations on products can be derived by analyzing online reviews using machine learning. Positive or negative responses can be sensed by words they write in reviews, and topics they compliment or complain about can be grasped by clustering reviews. Combination of those results is regarded as the customers’ sentiment analysis. When reviews are given as free-form text without scores, general-purpose dictionaries are used to recognize sentiment words. However, customers do not only use standard words to express their emotions, but they also use non-grammatical language such as internet jargon. Unfortunately, existing methods cannot capture those sentiment words. Moreover, combination of sentiment words with customer topics simply represents frequencies and does not indicate detailed evaluation patterns. In this study, we propose a customer sentiment analysis method consisting of sentiment propagation and customer review analysis. It works more sensibly by expanding sentiment words from dictionary to those varying words as mentioned above. To implement this, semi-supervised learning is employed to a word graph that is constructed by a word embedding algorithm. Using this more sensible word graph, customer review analysis is conducted. Reviews are grouped into major complaint topics. Meanwhile, an index for customer dissatisfaction is designed by composition of ‘controversy’ and ‘complaint’. The former stands for ‘coverage of dissatisfaction’ while the latter indicates ‘degree of dissatisfaction’. The proposed method was applied to 3,11,550 reviews across five automobiles from ten internet communities. Case study illustrates which parts of automobiles lead to customer dissatisfaction, and therefore where investment and examination are required.

Feedback as a space for academic social practice in doctoral writing groups

ABSTRACT Objective While academic writing is considered a core competency in academia, academic writing anxiety is ubiquitous in doctoral student cohorts. Doctoral writing groups provide a space for participants to learn from each other’s writing through the peer feedback process. In this conceptual review, we explore the dialogic nature of the peer feedback process in doctoral writing groups. Methods The findings in this study are based on thematic analyses of published peer-reviewed research using standard word processing, annotation, and referencing software. Results We focus on how the space of security and trust that is created in doctoral writing groups encourages participants to explore writing as a social practice. Through the relationships built in this type of interactive community, doctoral writing group participants discover and explore the discourses of scholarly communication. Furthermore, doctoral writing groups foster students’ ability to navigate the often-times implicit narratives of academia, thereby building student agency. Conclusion We argue, therefore, that feedback in doctoral writing groups should be viewed as a space of academic social practice. Implications This study underscores the importance of embedding opportunities for peer-based learning into doctoral programmes not only in the fields of educational and developmental psychology, but also in other disciplines more broadly.

A Word Pair Dataset for Semantic Similarity and Relatedness in Korean Medical Vocabulary: Reference Development and Validation.

BackgroundThe fact that medical terms require special expertise and are becoming increasingly complex makes it difficult to employ natural language processing techniques in medical informatics. Several human-validated reference standards for medical terms have been developed to evaluate word embedding models using the semantic similarity and relatedness of medical word pairs. However, there are very few reference standards in non-English languages. In addition, because the existing reference standards were developed a long time ago, there is a need to develop an updated standard to represent recent findings in medical sciences.ObjectiveWe propose a new Korean word pair reference set to verify embedding models.MethodsFrom January 2010 to December 2020, 518 medical textbooks, 72,844 health information news, and 15,698 medical research articles were collected, and the top 10,000 medical terms were selected to develop medical word pairs. Attending physicians (n=16) participated in the verification of the developed set with 607 word pairs.ResultsThe proportion of word pairs answered by all participants was 90.8% (551/607) for the similarity task and 86.5% (525/605) for the relatedness task. The similarity and relatedness of the word pair showed a high correlation (ρ=0.70, P<.001). The intraclass correlation coefficients to assess the interrater agreements of the word pair sets were 0.47 on the similarity task and 0.53 on the relatedness task. The final reference standard was 604 word pairs for the similarity task and 599 word pairs for relatedness, excluding word pairs with answers corresponding to outliers and word pairs that were answered by less than 50% of all the respondents. When FastText models were applied to the final reference standard word pair sets, the embedding models learning medical documents had a higher correlation between the calculated cosine similarity scores compared to human-judged similarity and relatedness scores (namu, ρ=0.12 vs with medical text for the similarity task, ρ=0.47; namu, ρ=0.02 vs with medical text for the relatedness task, ρ=0.30).ConclusionsKorean medical word pair reference standard sets for semantic similarity and relatedness were developed based on medical documents from the past 10 years. It is expected that our word pair reference sets will be actively utilized in the development of medical and multilingual natural language processing technology in the future.

Design and implementation of an African native language-based programming language

&lt;p&gt;Most of the existing high level programming languages havehitherto borrowed their lexical items from human languages including European and Asian languages. However, there is paucity of research information on programming languages developed with the lexicons of an African indigenous language. This research explored the design and implementation of an African indigenous language-based programming language using Yoruba as case study. Yoruba is the first language of over 30 million people in the south-west of Nigeria, Africa; and is spoken by over one hundred million people world-wide. It is hoped, as established by research studies, that making computer programming possible in one’s mother tongue will enhance computer-based problem-solving processes by indigenous learners and teachers. The alphabets and reserved words of the programming language were respectively formed from the basic Yoruba alphabets and standard Yoruba words. The lexical items and syntactic structures of the programming language were designed with appropriate regular expressions and context-free grammars, using Backus-Naur Form (BNF) notations. A prototype implementation of the programming language was carried out as a source-to-source, 5-pass compiler. QBasic within QB64 IDE was the implementation language. The results from implementation showed functional correctness and effectiveness of the developed programming language. Thus lexical items of a programming language need not be borrowed exclusively from European and Asian languages, they can and should be borrowed from most African native languages. Furthermore, the developed native language programming language can be used to introduce computer programming to indigenous pupils of primary and junior secondary schools.&lt;/p&gt;

THE FUNC THE FUNCTIONS OF ST TIONS OF STANDARD INTRODUCTORY WORDS IN Y WORDS IN MEDIATEXTS

Background. The article analyzes the stylistic features of standard introductory words used in newspaper texts in Uzbek and English. It has also been suggested that there is a standard similarity in the introductory words that connect sentences in Uzbek and English newspaper texts, and these words are often significant as they come at the beginning of a sentence. All analyzes were compared using examples of Uzbek and English newspapers. Methods. Introductory words in modern newspaper text is a powerful means of representing the author of a newspaper text, explication of dialogicity, as well as reproduction of the features of colloquial speech to give effect ease, spontaneity in expressing thoughts.

Database Metadata Standardization Processing Model Using Web Dictionary

Data quality control is an important issue these days. Improve data quality by providing consistent metadata. This study presents algorithms that facilitate standard word dictionary management for consistent metadata management. Algorithms are presented to automate synonyms management of database metadata through web dictionary crawling. It also improves the accuracy of the data by resolving homonym distinction issues that may arise during the web dictionary crawling process. Up to now, database metadata synonyms management has been managed manually by users. So the reliability of synonym data was poor, and a lot of time was spent. The algorithms proposed in this study bring synonym data through web dictionary crawling, thus increasing the reliability of metadata data quality compared to conventional passive management. It can also reduce the time spent on registering and managing synonym data. Further research on the new data standardization partial automation model will need to be continued, with a detailed understanding of some of the automatable tasks in future data standardization activities.

Question Design Affects Students' Sense-Making on Mathematics Word Problems.

Mathematics word problems provide students with an opportunity to apply what they are learning in their mathematics classes to the world around them. However, students often neglect their knowledge of the world and provide nonsensical responses (e.g., they may answer that a school needs 12.5 buses for a field trip). This study examined if the question design of word problems affects students' mindset in ways that affect subsequent sense-making. The hypothesis was that rewriting standard word problems to introduce inherent uncertainty about the result would be beneficial to student performance and sense-making because it requires students to reason explicitly about the context described in the problem. Middle school students (N=229) were randomly assigned to one of three conditions. In the standard textbook condition, students solved a set of six word problems taken from current textbooks. In the modified yes/no condition, students solved the same six problems rewritten so the solution helped answer a "yes" or "no" question. In the disfluency control condition, students solved the standard problems each rewritten in a variety of fonts to make them look unusual. After solving the six problems in their assigned condition, all students solved the same three "problematic" problems designed to assess sense-making. Contrary to predictions, results showed that students in the modified yes/no condition solved the fewest problems correctly in their assigned condition problem set. However, consistent with predictions, they subsequently demonstrated more sense-making on the three problematic problems. Results suggest that standard textbook word problems may be able to be rewritten in ways that mitigate a "senseless" mindset.

NAZIEF-ADRIANI STEMMER DENGAN IMBUHAN TAK BAKU PADA NORMALISASI BAHASA PERCAKAPAN DI MEDIA SOSIAL

Penggunaan bahasa tak baku semakin marak dalam komunikasi di media sosial. Penggunaan bahasa tak baku tidak terbatas pada kalimat, klausa, atau frasa saja namun juga pada penggunaan kata. Pada penelitian ini, akan dilakukan normalisasi kata yang tak baku/ nonstandard word (NSW) tersebut ke kata baku/ standard word (SW) Bahasa Indonesia. Metode stemmer Nazief-Adriani (Nazief-Adriani stemmer (NAS)) dikembangkan menjadi nonstandard stemmer (NSS) dengan meningkatkan kemampuannya untuk mendeteksi imbuhan tak baku. Tujuan penelitian ini adalah membandingkan penggunaan NAS dan NSS dalam normalisasi NSW. Algoritma kemiripan Needleman-Wunsch digunakan untuk membobot hasil pencocokan. Hasil pengujian dengan Mean Reciprocal Rank (MRR) pada sebanyak 3.438 NSW didapatkan penggunaan NSS dengan jumlah kueri = 9 (Q=9) memiliki tertinggi sebesar 79.26% dengan rata-rata sebesar 50.48%. Sedangkan pengujian MRR menggunakan NAS dengan Q=9 mendapatkan hasil tertinggi sebesar 72.87% dan rata-rata sebesar 47.23%. Dari dua pengujian MRR yang dilakukan, ada 3 huruf yang memiliki hasil stemming tertinggi, baik dalam pengujian menggunakan NAS maupun menggunakan NSS yaitu huruf awal r, f dan j. Peningkatan nilai MRR paling signifikan terjadi pada huruf awal ‘d’, ‘n’ dan ‘t’ yang merupakan huruf awal dari sebagian imbuhan tak standar.

Method of Profanity Detection Using Word Embedding and LSTM

With the rising number of Internet users, there has been a rapid increase in cyberbullying. Among the types of cyberbullying, verbal abuse is emerging as the most serious problem, for preventing which profanity is being identified and blocked. However, users employ words cleverly to avoid blocking. With the existing profanity discrimination methods, deliberate typos and profanity using special characters can be discriminated with high accuracy. However, as they cannot grasp the meaning of the words and the flow of sentences, standard words such as “Sibaljeom (starting point, a Korean word that sounds similar to a swear word)” and “Saekkibalgalag (little toe, a Korean word that sounds similar to another swear word)” are less accurately discriminated. Therefore, in order to solve this problem, this study proposes a method of discriminating profanity using a deep learning model that can grasp the meaning and context of words after separating Hangul into the onset, nucleus, and coda.