Words In Corpus Research Articles

English Grammar Auto-Correction Robot based on Grammatical Error Generation Model

The traditional English grammar error correction system has problems such as poor error recognition precision and error correction success rate to be improved. Therefore, in this study, data augmentation techniques were used to transform and process error correcting English texts, and a rule-based and shallow neural network-based English text grammar correction model was constructed. The experimental results showed that the grammar error generation (GEG), rule-based (RB), classification-based (CB), and recurrent neural network (RNN) models achieved accuracy rates of 93.92%, 82.17%, 79.41%, and 88.09% in correcting grammar errors on 1702641 test sentences in the One Billion word corpus, respectively. The experimental results showed that the English grammar error correction model designed in this study had a strong error correction ability, but the computational efficiency was low. The research results significantly improved the accuracy and generalization ability of English grammar correction, optimized learning costs, and brought positive impacts to educational applications, providing strong support for the development of intelligent English grammar correction.

SENSE RELATION IN VOCABULARY ACQUISITION

Foreign language teachers must select vocabulary carefully to ensure that high quality items are included and provide varied opportunities for practice. Teachers should pay the most prudent attention in organization of lexis for the classroom as to offer as wide a range of practice ideas in order to facilitateeffective learning. Acquiring a large, coloured and nuanced vocabulary knowledge does not imply simply studying a list of words alongside with dictionary definitions. Language diversity implies great possibilities of lexical choice as means of enhancing the effectiveness and accuracy of speech. Modern foreign languages teaching methods should aim at building a broad, deep, rich and flexible vocabulary corpus of words. Grouping of words is based upon similarities and contrasts. Every language has in its lexicon a variety of words, kindred in meaning, but distinct in morphemic composition and usage, ensuring the expression of the most delicate and somehow hidden shades of human perception, thought, feeling and imagination. The use of synonyms and antonyms is a quick and effective way of explaining unknown words and teachers can resort to them as means of describing words’ meanings.

Serendipity and surprise: A diachronic analysis of <em>International Baccalaureate</em> in the global press 1977-2019

This study examines perceptions of the International Baccalaureate (IB) over five decades to better understand its growing influence on education systems around the world. As part of the global education industry, the IB plays an important role in shaping national public education discourse. Building on previous synchronic analyses that used a corpus-assisted discourse studies (CADS) approach, the present study conducts a diachronic exploration of the same 28 million word corpus of global press articles to see how perceptions of the IB may have changed over its 55-year history. Trends over time revealed five main shifts in IB discourse: (i) from education to brand; (ii) from concrete curriculum-related lexis to recurring value-laden terms; (iii) from comparison to competition with other national curricula; (iv) from academic qualification to institutional regulation; and (v) from students to IB coordinator. Findings also showed a stable group of collocates that occurred consistently across all time periods, pointing to an “invariant core” (Morley & Partington, 2009) meaning of the IB that provides fresh insight into how the dominant view of the IB as a singular standardized brand may be entrenched and taken for granted.

A machine-based corpus optimization method for extracting domain-oriented technical words: an example of COVID-19 corpus data

Although natural language processing (NLP) refers to a process involving the development of algorithms or computational models that empower machines to understand, interpret, and generate human language, machines are still unable to fully grasp the meanings behind words. Specifically, they cannot assist humans in categorizing words with general or technical purposes without predefined standards or baselines. Empirically, prior researches have relied on inefficient manual tasks to exclude these words when extracting technical words (i.e., terminology or terms used within a specific field or domain of expertise) for obtaining domain information from the target corpus. Therefore, to enhance the efficiency of extracting domain-oriented technical words in corpus analysis, this paper proposes a machine-based corpus optimization method that compiles an advanced general-purpose word list (AGWL) to serve as the exclusion baseline for the machine to extract domain-oriented technical words. To validate the proposed method, this paper utilizes 52 COVID-19 research articles as the target corpus and an empirical example. After compared to traditional methods, the proposed method offers significant contributions: (1) it can automatically eliminate the most common function words in corpus data; (2) through a machine-driven process, it removes general-purpose words with high frequency and dispersion rates –57% of word types belonging to general-purpose words, constituting 90% of the total words in the target corpus. This results in 43% of word types representing domain-oriented technical words that makes up 10% of the total words in the target corpus are able to be extracted. This allows future researchers to focus exclusively on the remaining 43% of word types in the optimized word list (OWL), enhancing the efficiency of corpus analysis for extracting domain knowledge. (3) The proposed method establishes a set of standard operation procedure (SOP) that can be duplicated and generally applied to optimize any corpus data.

Introducing the Single Player Offline Game Corpus (spoc): a corpus of seven registers from digital role-playing games

This paper describes the compilation and design of the Single Player Offline Game Corpus (spoc), which is being made freely available for research and educational purposes. The spoc was compiled by extracting the localisation files from the digital directories of four popular commercial digital role-playing games: Divinity: Original Sin II, Fallout 4, the Elder Scrolls V: Skyrim, and the Witcher 3: Wild Hunt. The 3.7 million word corpus contains more than 30,000 texts and is unique compared with other game corpora in that it has the following three characteristics: ( 1) the texts are categorised into seven registers using Biber and Conrad’s (2019) register framework, ( 2) texts are systematically parsed into the smallest meaningful units of observation, and ( 3) all texts were compiled from the data files of the games themselves. Nearly all language use in the four games is accounted for and parsed into register categories based on their underlying situational characteristics – in particular, the communicative purposes and the associated contexts in which the texts appear in the games.

Analysis of Metaverse and Cryptocurrency Crimes ın Forensic Accounting

The purpose of this study is to collect data on crimes that encompass the technological and software aspects referred to as the "new generation," and to analyze case studies and legal incidents related to crimes committed within the scope of the metaverse and cryptocurrency.
 The starting point of the study is the analysis of court decisions related to metaverse and cryptocurrency crimes. Within the scope of the study, thousands of court decisions have been scanned using text mining techniques. The keywords "metaverse," "bitcoin," "digital currency," "cryptocurrency," and "digital money" have been included in the search algorithm for the word corpus. Although no court decision containing the word "metaverse" was found, 37 decisions were identified in which the other terms from the word corpus were mentioned. The oldest decision dates back to July 12, 2019, while the most recent decision is dated March 23, 2023. In addition to the decisions of first-instance courts, the decisions of appellate and higher judicial authorities have also been examined within the framework of text mining studies.

Nesterov-accelerated Adaptive Moment Estimation NADAM-LSTM based text summarization1

Automatic text summarization is the task of creating concise and fluent summaries without human intervention while preserving the meaning of the original text document. To increase the readability of the languages, a summary should be generated. In this paper, a novel Nesterov-accelerated Adaptive Moment Estimation Optimization based on Long Short-Term Memory [NADAM-LSTM] has been proposed to summarize the text. The proposed NADAM-LSTM model involves three stages namely pre-processing, summary generation, and parameter tuning. Initially, the Giga word Corpus dataset is pre-processed using Tokenization, Word Removal, Stemming, Lemmatization, and Normalization for removing irrelevant data. In the summary generation phase, the text is converted to the word-to-vector method. Further, the text is fed to LSTM to summarize the text. The parameter of the LSTM is then tuned using NADAM Optimization. The performance analysis of the proposed NADAM-LSTM is calculated based on parameters like accuracy, specificity, Recall, Precision, and F1 score. The suggested NADAM-LSTM achieves an accuracy range of 99.5%. The result illustrates that the proposed NADAM-LSTM enhances the overall accuracy better than 12%, 2.5%, and 1.5% in BERT, CNN-LSTM, and RNN respectively.

A mobile Deep Sparse Wavelet autoencoder for Arabic acoustic unit modeling and recognition

In this manuscript, we introduce a novel methodology for modeling acoustic units within a mobile architecture, employing a synergistic combination of various motivating techniques, including deep learning, sparse coding, and wavelet networks. The core concept involves constructing a Deep Sparse Wavelet Network (DSWN) through the integration of stacked wavelet autoencoders. The DSWN is designed to classify a specific class and discern it from other classes within a dataset of acoustic units. Mel-frequency cepstral coefficients (MFCC) and perceptual linear predictive (PLP) features are utilized for encoding speech units. This approach is tailored to leverage the computational capabilities of mobile devices by establishing deep networks with minimal connections, thereby immediately reducing computational overhead. The experimental findings demonstrate the efficacy of our system when applied to a segmented corpus of Arabic words. Notwithstanding promising results, we will explore the limitations of our methodology. One limitation concerns the use of a specific dataset of Arabic words. The generalizability of the sparse deep wavelet network (DSWN) to various contexts requires further investigation “We will evaluate the impact of speech variations, such as accents, on the performance of our model, for a nuanced understanding.

Error Pattern Discovery in Spellchecking Using Multi-Class Confusion Matrix Analysis for the Croatian Language

This paper introduces a novel approach to the creation and application of confusion matrices for error pattern discovery in spellchecking for the Croatian language. The experimental dataset has been derived from a corpus of mistyped words and user corrections collected since 2008 using the Croatian spellchecker available at ispravi.me. The important role of confusion matrices in enhancing the precision of spellcheckers, particularly within the diverse linguistic context of the Croatian language, is investigated. Common causes of spelling errors, emphasizing the challenges posed by diacritic usage, have been identified and analyzed. This research contributes to the advancement of spellchecking technologies and provides a more comprehensive understanding of linguistic details, particularly in languages with diacritic-rich orthographies, like Croatian. The presented user-data-driven approach demonstrates the potential for custom spellchecking solutions, especially considering the ever-changing dynamics of language use in digital communication.

“IN DER KRISE STECKEN” – ON THE WORD “CRISIS” AND ITS VERBAL COLLOCATIONS IN GERMAN AND ROMANIAN FROM THE PERSPECTIVE OF TEACHING GERMAN AS A FOREIGN LANGUAGE

The aim of this study is to analyze the verb collocations of the word “crisis” both in German and Romanian, through the lens of DaF teaching. The 24 German and 22 Romanian verb collocations of the analysed word were determined both from the dictionaries and with the help of specific instruments for determining the statistically significant combination partners of search words in corpora. The verbal combinations are analysed in terms of action-oriented DaF teaching by relating them to the communicative needs of a foreign language learner. From a phraseological point of view, more than half of the analysed word combinations function as collocations.

Research on the Digital Innovation and Inheritance Path of Southwest Lu Drum and Blow Music under the Background of Informatization

Abstract In this paper, we build a web crawler through the Scrapy framework to obtain the digitized data of Luxinan Drum and Blow music according to the demand, and construct the feature word vector and corpus of Luxinan Drum and Blow music by using the word2vec model. Using the three operations of pre-emphasis, frame-splitting and windowing, the digitized music of Lunan drums and dulcimers is digitized, and the Fourier transform of Lunan drums and dulcimers is used to obtain the cepstral coefficients of the Mel frequency, the fundamental frequency and the resonance peaks. The research object is selected, the questionnaire about the research of digital inheritance of the south Lu drum and blow music under the background of informationization is designed, and according to the initial data obtained for the research and analysis, the statistical analysis method is used to carry out the empirical analysis of the digital inheritance of the south Lu drum and blow music under the background of informationization. The results show that from the perspective of the inheritance value dimension of the Southwest Lu drum and blow music corpus, its mean and standard deviation are 2.11 and 0.958, respectively, indicating that many young people are increasingly concerned about the digital inheritance of the Southwest Lu drum and blow music. This study focuses on the analysis of the dilemma of inheritance and innovation in the new period and puts forward some feasible, innovative development paths to help this excellent national intangible cultural heritage to be inherited and developed.

America's racial framework of superiority and Americanness embedded in natural language.

America's racial framework can be summarized using two distinct dimensions: superiority/inferiority and Americanness/foreignness. We investigated America's racial framework in a corpus of spoken and written language using word embeddings. Word embeddings place words on a low-dimensional space where words with similar meanings are proximate, allowing researchers to test whether the positions of group and attribute words in a semantic space reflect stereotypes. We trained a word embedding model on the Corpus of Contemporary American English-a corpus of 1 billion words that span 30 years and 8 text categories-and compared the positions of racial/ethnic groups with respect to superiority and Americanness. We found that America's racial framework is embedded in American English. We also captured an additional nuance: Asian people were stereotyped as more American than Hispanic people. These results are empirical evidence that America's racial framework is embedded in American English.

APPROACHES TO AUTOMATIC WORD SENSE DISAMBIGUATION BASED ON UNEVEN DISTRIBUTION OF WORD SENSES IN CORPUS

Word Sense Disambiguation (WSD) is a key task of automatic semantic analysis that affects other upstream tasks. Nevertheless, the selection of appropriate sense of ambiguous word in context is a complicated task even for human native speakers. It is even more relevant for automatic disambiguation models. That is why we need any observations and heuristics able to make the WSD task simpler or performance higher. Researchers have noticed that the distribution of ambiguous word senses follow certain laws. In the paper we discuss three hypotheses about word senses distribution in corpus: 1) Most Frequent Sense, MFS; 2) One Sense per Discourse and 3) One Sense per Collocation. The following results were obtained on the material of a corpus of Russian texts. Most Frequent Sense based algorithm demonstrates relatively high precision on both training and test set (85.7% and 71.1% respectively). The One Sense per Discourse hypothesis has been confirmed in 93% of texts. The One Sense per Collocation hypothesis has been confirmed in 84.46% word pairs from texts. The exceptions are related to difficulties and errors by manual word sense labeling. Heuristics based on uneven distribution of ambiguous words in corpus allow to make WSD task simpler and can be used by collecting training data sets for WSD models.

Pyrlato: A novel methodology to collect real-world acoustic data

In this paper, we present Pyrlato, an innovative tool developed in Python for collecting acoustic data from YouTube. The development of this tool was motivated by the need to conveniently collect real-world spoken data. By executing this Python code, researchers can obtain a spoken corpus of specific words, syllables, constituents, and more. We illustrate the main steps of the execution to demonstrate how it works and how to use it. Additionally, we provide a complete example for reference, demonstrating how to customize Pyrlato according to specific requirements. Finally, we discuss the future developments we intend to cover for Pyrlato.

Derivational peculiarities of the high style vocabulary in the lyrics of I.A. Brodsky and A.A. Tarkovsky

The article is devoted to the word-formation features of high style vocabulary on the material of I.A. Brodsky’s and A.A. Tarkovsky poetry. The author shows the continuity of the poetic language of Brodsky and Tarkovsky with the heritage of Pushkin, and links lexical resources used by these poets with the corpus of Slavic words in the Russian literary language. The productivity of book word-formation type of nominative and objectifical names with abstract feature with suffixes -ость/- есть, -ство/-ество, lexical apophathisms, formed by prefix-suffixal method with prefixes без- and не-; the productivity of book-poetic verbal nouns with suffixes --ениj-/-ниj-, verbal derivatives of nouns with the suffix -тель; poets’ active use of participial forms. The conclusion is made about the commonality in the representation of stylistic synthesis by I.A. Brodsky and A.A. Tarkovsky. Along with the widely represented book vocabulary, their poetry reflects common and expressive vocabulary of colloquial style. Style synthesis testifies to the syncretism of the author’s perception of reality: subjective experience is combined with collective, value beginning.

There as a discourse-pragmatic marker in Irish English

The upper ranks of corpus word frequency lists are primarily populated by functional items, as well as by other small words characterised by their polysemy that reward focussed attention and research. Exploring these items has resulted in rich and fruitful insights from a corpus pragmatic perspective. This paper focuses on the nature and value of there as a discourse-pragmatic marker in Irish English (IrE). We focus on intimate discourse (i.e., the language used between family and friends), and use corpora of IrE to trace and investigate the varietal nuances of there in this context. Our analysis acknowledges the expected existential and locative functions and illustrates that there has specific discourse–pragmatic properties in the intimate sphere in IrE. We argue that there has acquired additional pragmatic dimensions and is used in the language of family and friends as, for example, an involvement marker or mitigator. In the discussion of the development of there as a discourse-pragmatic marker, we consult historical data that matches our focal context, and discuss the processes by which there has come to fulfil a range of discourse-pragmatic functions in IrE.

Military slang: Origin, structure and semantics

The humankind has been facing different wars and military conflicts during its existence and development. In the current globalized world, the role of the military is very essential and obvious. Furthermore, military slang in English is of utmost significance due to the leading role of the military from various English-speaking countries. The aim of the following research is to identify and analyze military slangy words and phrases in English; to create the military slang phrases and words corpus; to make their semantic and structural characteristics analyses; and to explain their practical usage context. The methods applied in this research include linguistic analysis, which was utilized in the selection of military slang words and phrases applied in modern English; the word-building analysis, which helped in determining semantics and structural features of military words and phrases utilized in the English language; and linguo-cultural and socio-cultural analyses that allowed giving explanations of the UK and US military traditions, cultures, and vocabulary specific features. The specific characteristics of military slang are dynamism, exaggerated expression, broad usage by the military, and general familiarity. The military slang vocabulary units have the same formation ways typical of general English vocabulary, including meanings transfer (metaphor), abbreviation and acronyms, blending, affixation, conversion, shortening, and compounding.

Classification of Severe Maternal Morbidity from Electronic Health Records Written in Spanish Using Natural Language Processing

One stepping stone for reducing the maternal mortality is to identify severe maternal morbidity (SMM) using Electronic Health Records (EHRs). We aim to develop a pipeline to represent and classify the unstructured text of maternal progress notes in eight classes according to the silver labels defined by the ICD-10 codes associated with SMM. We preprocessed the text, removing protected health information (PHI) and reducing stop words. We built different pipelines to classify the SMM by the combination of six word-embeddings schemes, three different approaches for the representation of the documents (average, clustering, and principal component analysis), and five well-known machine learning classifiers. Additionally, we implemented an algorithm for typos and misspelling adjustment based on the Levenshtein distance to the Spanish Billion Word Corpus dictionary. We analyzed 43,529 documents constructed by an average of 4.15 progress notes from 22,937 patients. The pipeline with the best performance was the one that included Word2Vec, typos and spelling adjustment, document representation by PCA, and an SVM classifier. We found that it is possible to identify conditions such as miscarriage complication or hypertensive disorders from clinical notes written in Spanish, with a true positive rate higher than 0.85. This is the first approach to classify SMM from the unstructured text contained in the maternal EHRs, which can contribute to the solution of one of the most important public health problems in the world. Future works must test other representation and classification approaches to detect the risk of SMM.

Statistical Information Affects Spoken Word Recognition of Tone Languages in Stutterers: Evidence From an Auditory-Perceptual Gating Study.

Previous studies have shown that individuals who stutter exhibit abnormal speech perception in addition to disfluent production as compared with their nonstuttering peers. This study investigated whether adult Chinese-speaking stutterers are still able to use knowledge of statistical regularities embedded in their native language to recognize spoken words and, if so, how much acoustic information is needed to trigger this information. Seventeen stutterers and 20 typical, nonstuttering controls participated in a gating experiment. All participants listened to monosyllabic words that consisted of syllables and lexical tones and were segmented into eight successive gates. These words differed in syllable token frequency and syllable-tone co-occurrence probability in line with a Chinese spoken word corpus. The correct syllable-only, correct tone-only, correct syllable-tone word, and correct syllable-incorrect tone responses were analyzed between the two groups using mixed-effects models. Stutterers were less accurate overall than controls, with fewer correct syllables, tones, and their combination as words. However, stutterers showed consistent and reliable perceptual patterns triggered by statistical information of speech, as reflected by more accurate responses to high-frequency syllables, high-probability tones, and tone errors all in manners similar to those of nonstuttering controls. Stutterers' atypical speech perception is not due to a lack of statistical learning. Stutterers were able to perceive spoken words with phonological tones based on statistical regularities embedded in their native speech. This finding echoes previous production studies of stuttering and lends some support for a link between perception and production. Implications of pathological, diagnostic, and therapeutic conditions of stuttering are discussed.

Об оформлении комбинаций ноэм посредством сложных слов: лингвоперсонологический аспект

This article is an application of the ideas and propositions developed by G.I. Bogin (head of the Tver hermeneutic school and founder of the theory of linguistic personality) to historical linguistic material. The key concept in the study – the noema – is defined as a minimal unit of meaning, which is rarely found in its pure form, but rather in combinations with other minimal units of meaning. In the Life of Epiphanius of Solovki, which is presented in the Pustozersk Collection compiled by I.N. Zavoloko, 261 uses of compound words were detected by means of continuous sampling. Using comparison, analogy, phenomenological reduction and contextual analysis, the author obtained two sequences by the first roots. One of the sequences forms the movement from a quantitatively larger corpus of compound words to a quantitatively smaller one (Бог-, Благ-, Рук-, Зло-/Пол-, Пуст-), while the second, on the contrary, from the smaller to the larger one (Добр-/Три-, Пред-/Преж-, Все-, Мног-/Жив-, Пуст-). As a result of the steps taken, the author determined that words with the first element Пуст-, firstly, serve as the central component in the entire lexical corpus, and, secondly, are directly related to the linguistic personality of Epiphanius of Solovki. Further, the phenomenologically reduced second roots were grouped under three headings. After this step, the resulting semantic sets were extrapolated to contexts where compound words with the first element Пуст- are used. It was established that it is these compounds that are one of the semantic axes of the narrative about the torments experienced by Epiphanius during the exile.