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Journal articles on the topic 'Semantic Error'

Author: Grafiati

Published: 3 June 2025

Last updated: 31 July 2025

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1

Tawfik Ali, Mai Morsy. "ANALISI DEGLI ERRORI LESSICALI PIÙ FREQUENTI NELL’ITALIANO DEGLI STUDENTI EGIZIANI." Italiano LinguaDue 16, no. 1 (2024): 359–73. http://dx.doi.org/10.54103/2037-3597/23846.

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In questo lavoro ci proponiamo di analizzare alcune tipologie di errori lessicali commessi da studenti egiziani di italiano LS evidenziando le strategie da essi adottate per colmare le lacune lessicali. Questo studio si basa sull’analisi di composizioni scritte di 60 studenti egiziani iscritti al secondo anno del corso di laurea in italiano presso la Facoltà di Al Alsun. L’analisi ha rivelato che gli errori lessicali sono il tipo di errore più grave e frequente comprovando che se un messaggio presenta molti errori lessicali ha una scarsa efficacia comunicativa, poiché tali errori sono noti ele

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Pramesti, Rahmadini Windy, Aidil Syah Putra, Ida Farhatul Iffah, and Sarrohmahniyati Sarrohmahniyati. "Semantic Error Analysis in English Oral Presentation." Dinamika Bahasa dan Budaya 19, no. 2 (2024): 105–13. https://doi.org/10.35315/bb.v19i2.9880.

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This study aims to analyze the oral semantic errors made by students during their English oral presentations and to classify these errors according to the categories defined by Al-Shormani and Al-Sohbani's (2012), adapted from James' (1998) error classification theory. This study uses qualitative descriptive analysis method. The study’s population consisted of approximately 50 6th-semester English Education students at Universitas Muhammadiyah Tangerang, with a sample size of 14 students from class 6A1, who were scheduled to perform their oral presentation. The data collection techniques used

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Nowbakht, Mohammad, and Thierry Olive. "The Role of Error Type and Working Memory in Written Corrective Feedback Effectiveness on First-Language Self Error-Correction." Written Communication 38, no. 2 (2021): 278–310. http://dx.doi.org/10.1177/0741088320986554.

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This study examined the role of error-type and working memory (WM) in the effectiveness of direct-metalinguistic and indirect written corrective feedback (WCF) on self error-correction in first-language writing. Fifty-one French first-year psychology students volunteered to participate in the experiment. They carried out a first-language error-correction task after receiving WCF on typographical, orthographic, grammatical, and semantic errors. Results indicated that error-type affected the efficacy of WCF. In both groups, typographical error-correction was performed better than the others; ort

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Wrede, Olga, Dasa Munkova, Tomas Banik, and Michal Munk. "Zur Erforschung von Korrelationen zwischen verschiedenen Fehlertypen bei der maschinellen Übersetzung aus dem Deutschen ins Slowakische." Lebende Sprachen 67, no. 2 (2022): 432–56. http://dx.doi.org/10.1515/les-2022-1032.

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Abstract We focus on examining the impact of machine translation (MT) error rate on adequacy and fluency in machine-translated journalistic texts. German is the source language, with significant polysynthetic features in the formation of composites, and the target language is Slovak, with predominantly inflected features. We analyse twelve error categories, which are incorporated into the categorical framework for the analysis of MT errors and correspond to the four-member core MQM-DQF error typology. The results show that the most significant errors are in the categories of lexical semantics,

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McGregor, Karla K., Robyn M. Newman, Renée M. Reilly, and Nina C. Capone. "Semantic Representation and Naming in Children With Specific Language Impairment." Journal of Speech, Language, and Hearing Research 45, no. 5 (2002): 998–1014. http://dx.doi.org/10.1044/1092-4388(2002/081).

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When 16 children with SLI (mean age=6;2) and 16 normally developing age-mates named age-appropriate objects, the SLI cohort made more naming errors. For both cohorts, semantic misnaming and indeterminate responses were the predominant error types. The contribution of limited semantic representation to these naming errors was explored. Each participant drew and defined each item from his or her semantic and indeterminate error pools and each item from his or her correctly named pool. When compared, the drawings and definitions of items from the error pools were poorer, suggesting limited semant

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ARNAUD, PIERRE J. L. "Target–error resemblance in French word substitution speech errors and the mental lexicon." Applied Psycholinguistics 20, no. 2 (1999): 269–87. http://dx.doi.org/10.1017/s0142716499002052.

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The word substitution errors from a corpus of 2,400 French slips of the tongue were grouped into several categories: contaminational, semantic, formal, and mixed cases; substitutions of syntagmatic codependents also occurred. Semantic and formal substitutions involved a resemblance between target and error. In addition, all substitutions exhibited a strong degree of word class and gender identity. The various types of resemblance were analyzed with reference to three-layer models of lexicalization. They did not make a lemma layer necessary, but stronger evidence came from another error categor

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Singh, Mukul, José Cambronero, Sumit Gulwani, et al. "DataVinci: Learning Syntactic and Semantic String Repairs." Proceedings of the ACM on Management of Data 3, no. 1 (2025): 1–26. https://doi.org/10.1145/3709677.

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String data is common in real-world datasets: 67.6% of values in a sample of 1.8 million real Excel spreadsheets from the web were represented as text. Automatically cleaning such string data can have a significant impact on users. Previous approaches are limited to error detection, require that the user provides annotations, examples, or constraints to fix the errors, and focus independently on syntactic errors or semantic errors in strings, but ignore that strings often contain both syntactic and semantic substrings. We introduce DataVinci, a fully unsupervised string data error detection an

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He, Zhe, Duo Wei, Gai Elhanan, Yan Chen, and Huanying Gu. "Validating UMLS Semantic Type Assignments Using SNOMED CT Semantic Tags." Methods of Information in Medicine 57, no. 01/02 (2018): 43–53. http://dx.doi.org/10.3414/me17-01-0120.

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Summary Background: The UMLS assigns semantic types to all its integrated concepts. The semantic types are widely used in various natural language processing tasks in the biomedical domain, such as named entity recognition, semantic disambiguation, and semantic annotation. Due to the size of the UMLS, erroneous semantic type assignments are hard to detect. It is imperative to devise automated techniques to identify errors and inconsistencies in semantic type assignments. Objectives: Designing a methodology to perform programmatic checks to detect semantic type assignment errors for UMLS concep

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Wang, Xue, Fang Yang, Hongyuan Liu, and Qingxuan Shi. "Error correction of semantic mathematical expressions based on bayesian algorithm." Mathematical Biosciences and Engineering 19, no. 6 (2022): 5428–45. http://dx.doi.org/10.3934/mbe.2022255.

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<abstract> <p>The semantic information of mathematical expressions plays an important role in information retrieval and similarity calculation. However, a large number of presentational expressions in the presentation MathML format contained in electronic scientific documents do not reflect semantic information. It is a shortcut to extract semantic information using the rule mapping method to convert presentational expressions in presentation MathML format into semantic expressions in the content MathML format. However, the conversion result is prone to semantic errors because the

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Morell, Larry, and Branson Murrill. "Semantic metrics through error flow analysis." Journal of Systems and Software 20, no. 3 (1993): 253–65. http://dx.doi.org/10.1016/0164-1212(93)90069-a.

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McGregor, Karla K. "The Nature of Word-Finding Errors of Preschoolers With and Without Word-Finding Deficits." Journal of Speech, Language, and Hearing Research 40, no. 6 (1997): 1232–44. http://dx.doi.org/10.1044/jslhr.4006.1232.

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Twelve preschoolers with word-finding deficits (WF) and their age-matched normally developing (ND) peers participated in three tasks requiring word finding: the noun-naming and verb-naming subtests of the Test of Word Finding (TWF-N, TWF-V) and story retelling. The general error profiles of the two subject groups were similar. Semantic errors were always more common than phonological errors and were typically more common than unrelated errors (e.g., "I don’t know" responses). The difference in proportions of semantic and phonological substitutions constitutes developmental evidence for lemma a

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Kellison, Ariel E., and Justin Hsu. "Numerical Fuzz: A Type System for Rounding Error Analysis." Proceedings of the ACM on Programming Languages 8, PLDI (2024): 1954–78. http://dx.doi.org/10.1145/3656456.

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Algorithms operating on real numbers are implemented as floating-point computations in practice, but floating-point operations introduce roundoff errors that can degrade the accuracy of the result. We propose Λ num , a functional programming language with a type system that can express quantitative bounds on roundoff error. Our type system combines a sensitivity analysis, enforced through a linear typing discipline, with a novel graded monad to track the accumulation of roundoff errors. We prove that our type system is sound by relating the denotational semantics of our language to the exact a

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McMillen, Stephanie, Zenzi M. Griffin, Elizabeth D. Peña, Lisa M. Bedore, and Gary M. Oppenheim. "“Did I Say Cherry ?” Error Patterns on a Blocked Cyclic Naming Task for Bilingual Children With and Without Developmental Language Disorder." Journal of Speech, Language, and Hearing Research 63, no. 4 (2020): 1148–64. http://dx.doi.org/10.1044/2019_jslhr-19-00041.

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Purpose Using a blocked cyclic picture-naming task, we compared accuracy and error patterns across languages for Spanish–English bilingual children with and without developmental language disorder (DLD). Method Pictured stimuli were manipulated for semantic similarity across two (Same and Mixed) category contexts. Children's productions were scored off-line for accuracy, error frequency, and error type. Results Typically developing children were more accurate and produced fewer errors than their peers with DLD; however, this was moderated by task language and semantic context. Children were ge

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Li, Gang, Yawen Zeng, Huilan Huang, Shaojian Song, Bin Liu, and Xiang Liao. "A Multi-Feature Fusion Slam System Attaching Semantic Invariant to Points and Lines." Sensors 21, no. 4 (2021): 1196. http://dx.doi.org/10.3390/s21041196.

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The traditional simultaneous localization and mapping (SLAM) system uses static points of the environment as features for real-time localization and mapping. When there are few available point features, the system is difficult to implement. A feasible solution is to introduce line features. In complex scenarios containing rich line segments, the description of line segments is not strongly differentiated, which can lead to incorrect association of line segment data, thus introducing errors into the system and aggravating the cumulative error of the system. To address this problem, a point-line

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Sumiati, Ati, Andhika Laksana, and Romel Noverino. "Translation Error Taxonomies in Indonesian Tourism Guidebooks." Lililacs Journal : English Literature, Language, and Cultural Studies Journal 1, no. 1 (2021): 21–32. http://dx.doi.org/10.21009/lililacs.011.03.

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There are many tourism guidebooks spread all around Indonesia yet they are not of good quality. This study embarks on the idea that the quality of tourism guidebook is still far from perfect. This study intends to explore the type of error taxonomies found in the tourism guidebooks by employing qualitative method. Six tourism guidebooks were collected and used as data and the theory being used is proposed by Dastjerdi and Abdolmaleki (2012). They modify the classification of errors in translation based on Keshavarz and ATA and proposed the name of error taxonomy. This error taxonomy has four m

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Tündik, Máté Ákos, Valér Kaszás, and György Szaszák. "On the Effects of Automatic Transcription and Segmentation Errors in Hungarian Spoken Language Processing." Periodica Polytechnica Electrical Engineering and Computer Science 63, no. 4 (2019): 254–62. http://dx.doi.org/10.3311/ppee.14052.

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Emerging Artificial Intelligence (AI) technology has brought machines to reach an equal or even superior level compared to human capabilities in several fields; nevertheless, among many other fields, making a computer able to understand human language still remains a challenge. When dealing with speech understanding, Automatic Speech Recognition (ASR) is used to generate transcripts, which are processed with text-based tools targeting Spoken Language Understanding (SLU). Depending on the ASR quality (which further depends on speech quality, the complexity of the topic, environment etc.), trans

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YUDES, CAROLINA, PEDRO MACIZO, LUIS MORALES, and M. TERESA BAJO. "Comprehension and error monitoring in simultaneous interpreters." Applied Psycholinguistics 34, no. 5 (2012): 1039–57. http://dx.doi.org/10.1017/s0142716412000112.

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ABSTRACTIn the current study we explored lexical, syntactic, and semantic processes during text comprehension in English monolinguals and Spanish/English (first language/second language) bilinguals with different experience in interpreting (nontrained bilinguals, interpreting students and professional interpreters). The participants performed an error-detection task in which they read English texts and tried to identify lexical, syntactic, and semantic errors embedded in texts. After reading, global comprehension of the texts was assessed by means of a sentence verification task and open/ended

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Willers, Indra F., Mónica L. Feldman, and Ricardo F. Allegri. "Subclinical naming errors in mild cognitive impairment: A semantic deficit?" Dementia & Neuropsychologia 2, no. 3 (2008): 217–22. http://dx.doi.org/10.1590/s1980-57642009dn20300010.

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Abstract Mild cognitive impairment (MCI) is the transitional stage between normal aging and Alzheimer's disease (AD). Impairments in semantic memory have been demonstrated to be a critical factor in early AD. The Boston Naming Test (BNT) is a straightforward method of examining semantic or visuo-perceptual processing and therefore represents a potential diagnostic tool. The objective of this study was to examine naming ability and identify error types in patients with amnestic mild cognitive impairment (aMCI). Methods: Twenty aMCI patients, twenty AD patients and twenty-one normal controls, ma

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Leontjeva, Oksana. "Transfer of Cultural Codes in Intercultural Communication." Półrocznik Językoznawczy Tertium 7, no. 2 (2023): 33–57. http://dx.doi.org/10.7592/tertium.2022.7.2.226.

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One of the factors affecting the quality of intercultural communication is the correct transmission of the information and semantic component of the codes that implement the model of culture. Coming into contact with the codes of a foreign culture through the language, the communicant involuntarily makes mistakes when the codes are transformed into the system of codes of their own culture. As a result of this, there is a misunderstanding, and often a misunderstanding of cultural meanings, which leads to a certain conflict - hidden or explicit - in intercultural communication. We single out sev

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Botirova, Gulsanam. "MORPHOLOGICAL STRUCTURE OF ENGLISH WORDS. MORPHEMES. FREE AND BOUND FORMS, MORPHOLOGICAL CLASSIFICATION OF WORDS, WORD-FAMILIES." EURASIAN JOURNAL OF ACADEMIC RESEARCH 1, no. 1 (2021): 818–22. https://doi.org/10.5281/zenodo.4737735.

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<em>The present study aims to determine whether semantic relatedness plays a role in the production of speech errors involving derivational morphemes. A word order competition technique was used to induce morpheme and syllable exchange errors. Semantic relatedness was manipulated by contrasting error rates for prefixed words derived from free stems to those derived from bound roots.</em>

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Utami, Ni Putu Laksmi Dewi, and Ni Made Verayanti Utami. "Unveiling Semantic Errors Found in Lexical Translations of Tasya Farasya’s Tiktok Account." Lingua Cultura 17, no. 2 (2023): 219–25. http://dx.doi.org/10.21512/lc.v17i2.10435.

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The research observed the semantic errors in lexis that occurred in the translation by TikTok machine translation. It became the main issue in translation studies because the accuracy of translation produced by machine translation was still questionable and debatable. The research aimed to identify the types of semantic error in lexis made by TikTok auto machine translation found in Tasya Farasya TikTok’s account and suggested a more appropriate translation. The research applied a descriptive qualitative method to analyze the error in translation produced by TikTok machine translation. The the

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Cheon, Haein, Haerim Yu, and Soyeong Pae. "Text Reading Fluency Error Analysis of Korean 1st and 2nd Graders with Developmental Dyslexia considering Semantic Knowledge." Communication Sciences & Disorders 27, no. 3 (2022): 483–94. http://dx.doi.org/10.12963/csd.22908.

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Objectives: The purpose of this study was to investigate the characteristics of text reading fluency error analysis of Korean 1st and 2nd graders with developmental dyslexia considering semantic knowledge. Methods: Thirty developmental dyslexic children were divided into two groups of 15 children, one group with poor vocabulary and the other not. Both groups had similar word decoding abilities as determined by Korean Language based Reading Assessment (Pae, Kim, Yoon, & Jang, 2015). The children had normal intelligence and difficulties in reading. The task was the text taken from Korean Lan

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Dillig, Isil, Thomas Dillig, and Alex Aiken. "Static error detection using semantic inconsistency inference." ACM SIGPLAN Notices 42, no. 6 (2007): 435–45. http://dx.doi.org/10.1145/1273442.1250784.

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Shamim, Maria, Humaira Irfan, and Muhammad Mubeen Shah. "An analysis of lexical and syntactic errors in the Urdu translation of the Punjab Textbook Board's English literature." International Journal of Language Studies 18, no. 4 (2024): 131–48. https://doi.org/10.5281/zenodo.13956424.

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English, a second language in Pakistan, is challenging for many intermediate learners who rely on guidebooks for Urdu translations of English literature. This study examines lexical and syntactic errors in these translations, focusing on the Dogar Publisher’s guidebook for Intermediate Part 1. Using Baker’s (1992) translation strategies, the analysis revealed significant errors at both lexical and syntactic levels. Lexically, discrepancies were observed, with some English words remaining untranslated or being inaccurately translated. Syntactically, errors were identified in sentenc

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Cao, Xueyuan. "A Study of Modeling Algorithm Improvement for Semantic Analysis of Public English Texts." Journal of Combinatorial Mathematics and Combinatorial Computing 127a (April 15, 2025): 2569–89. https://doi.org/10.61091/jcmcc127a-145.

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Semantics in public English texts are more challenging to understand accurately because they are influenced by specific contextual contexts. Traditional English text semantic understanding methods do not design their semantic understanding methods based on the conceptual semantic features of the text, and they have the problem of poor accuracy in understanding the deep semantics of English texts. For this reason, the article takes the public English text semantic algorithm as the research perspective, firstly conducts relevant theoretical research on English text semantic feature representatio

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Weißbecker-Klaus, Xenija, Peter Ullsperger, Gabriele Freude, and Sergei A. Schapkin. "Impaired Error Processing and Semantic Processing During Multitasking." Journal of Psychophysiology 31, no. 4 (2017): 167–78. http://dx.doi.org/10.1027/0269-8803/a000178.

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Abstract. Neuronal mechanisms of error processing under multitasking and their impact on the processing of a concurrent task were examined. Twenty-one younger and twenty older healthy adults performed a visual-motor flanker task or an auditory-vocal semantic task or both tasks simultaneously. During task performance the electroencephalogram (EEG) was continuously recorded. The event-related potential (ERP) was derived from the EEG, and ERP components associated with error processing (Ne and Pe) and semantic processing (N400) were analyzed. Older participants responded more slowly than younger

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Ananda, Ellara Yusea, and Henny Putri Saking Wijaya. "Lexical Errors Produced by High And Low Proficiency Learners of Writing 2 Classes." K@ta Kita 9, no. 2 (2021): 180–86. http://dx.doi.org/10.9744/katakita.9.2.180-186.

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This study was conducted to find out the types of lexical errors that high and low proficiency learners produced in their writing, and find the differences and similarities from the production of the two groups. To answer the research questions, the writers used the theory by James (2013) about lexical error classification. This study was a qualitative study. The sources of the data of this study were the lexical errors from 32 writing drafts. The writers divided the students into two groups: eight students from high proficiency level and eight students from low proficiency level. The findings

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Mandal, Ayan S., Mackenzie E. Fama, Laura M. Skipper-Kallal, Andrew T. DeMarco, Elizabeth H. Lacey, and Peter E. Turkeltaub. "Brain Structures and Cognitive Abilities Important for the Self-Monitoring of Speech Errors." Neurobiology of Language 1, no. 3 (2020): 319–38. http://dx.doi.org/10.1162/nol_a_00015.

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The brain structures and cognitive abilities necessary for successful monitoring of one’s own speech errors remain unknown. We aimed to inform self-monitoring models by examining the neural and behavioral correlates of phonological and semantic error detection in individuals with post-stroke aphasia. First, we determined whether detection related to other abilities proposed to contribute to monitoring according to various theories, including naming ability, fluency, word-level auditory comprehension, sentence-level auditory comprehension, and executive function. Regression analyses revealed th

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Albuhairy, Mohammad Mahyoob, and Jeehaan Algaraady. "DeepSeek vs. ChatGPT: Comparative Efficacy in Reasoning for Adults’ Second Language Acquisition Analysis." مجلة العلوم التربوية و الدراسات الإنسانية, no. 44 (February 2, 2025): 864–83. https://doi.org/10.55074/hesj.vi44.1313.

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The advent of new generation language models has revolutionized the field of natural language processing (NLP) due to their exceptional understanding and human language generation capabilities. ChatGPT emerged as an essential model with remarkable strengths for various applications. DeepSeek has recently emerged as the latest advancement in NLP, showing great potential in pure text-generation jobs, semantic analysis, and context-dependent language modeling capabilities. The study investigates and compares the performance of DeepSeek and ChatGPT in assessing adult L2 (second language) acquisiti

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Zeng, Guanguan. "Intelligent Test Algorithm for English Writing Using English Semantic and Neural Networks." Mobile Information Systems 2022 (June 30, 2022): 1–11. http://dx.doi.org/10.1155/2022/4484201.

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English writing is considered by English learners to be the portion of English learning with the greatest application, the most thorough understanding, and the most challenging instruction. It automatically detects and corrects (DAC) grammatical faults in English writing, which is critical in the English learning and teaching processes. The goal of this research is to investigate the sequence annotation model and the Seq2Seq NN model based on cyclic NN, and to use these two models to detect grammatical faults in English (EGE). This paper provides an EGE DAC approach based on sequence annotatio

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Wen, Jing, Jiayuan He, Ye Wang, Haibo Tan, and Pengcheng Li. "Semantic Web-Based Identification of Key Causative Factors and Chains for Team Error in NPPs' ACR." International Journal on Semantic Web and Information Systems 20, no. 1 (2024): 1–27. http://dx.doi.org/10.4018/ijswis.357697.

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Team errors are identified as a major contributor to incidents in safety-critical systems like nuclear power plants (NPPs). Safety in NPPs is more dependent on team performance than individual, and reducing team errors requires identifying key causative factors and chains. Current studies primarily use qualitative analysis whereas lacking quantitative methods. This study addressed this by constructing a complex network model of team error based on semantic features and coding of the Semantic Web and determining criteria for selecting key causative factors by three node failure strategies. The

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Hussen Maulud, Dastan, Subhi R. M. Zeebaree, Karwan Jacksi, Mohammed Mohammed Sadeeq, and Karzan Hussein Sharif. "State of Art for Semantic Analysis of Natural Language Processing." Qubahan Academic Journal 1, no. 2 (2021): 21–28. http://dx.doi.org/10.48161/qaj.v1n2a40.

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Semantic analysis is an essential feature of the NLP approach. It indicates, in the appropriate format, the context of a sentence or paragraph. Semantics is about language significance study. The vocabulary used conveys the importance of the subject because of the interrelationship between linguistic classes. In this article, semantic interpretation is carried out in the area of Natural Language Processing. The findings suggest that the best-achieved accuracy of checked papers and those who relied on the Sentiment Analysis approach and the prediction error is minimal.

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Ohlerth, Ann-Katrin, Roelien Bastiaanse, Chiara Negwer, et al. "Bihemispheric Navigated Transcranial Magnetic Stimulation Mapping for Action Naming Compared to Object Naming in Sentence Context." Brain Sciences 11, no. 9 (2021): 1190. http://dx.doi.org/10.3390/brainsci11091190.

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Preoperative language mapping with navigated transcranial magnetic stimulation (nTMS) is currently based on the disruption of performance during object naming. The resulting cortical language maps, however, lack accuracy when compared to intraoperative mapping. The question arises whether nTMS results can be improved, when another language task is considered, involving verb retrieval in sentence context. Twenty healthy German speakers were tested with object naming and a novel action naming task during nTMS language mapping. Error rates and categories in both hemispheres were compared. Action

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Akai, Kyoka, Takahiro Sekiguchi, and Eriko Kawasaki. "Moses illusion and retrieval error of semantic memory." Proceedings of the Annual Convention of the Japanese Psychological Association 81 (September 20, 2017): 1D—066–1D—066. http://dx.doi.org/10.4992/pacjpa.81.0_1d-066.

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Gutierrez, Fernando, Dejing Dou, Nisansa de Silva, and Stephen Fickas. "Online Reasoning for Semantic Error Detection in Text." Journal on Data Semantics 6, no. 3 (2017): 139–53. http://dx.doi.org/10.1007/s13740-017-0079-6.

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Gong, Lixue, Yiqun Zhang, Yunke Zhang, Yin Yang, and Weiwei Xu. "Erroneous pixel prediction for semantic image segmentation." Computational Visual Media 8, no. 1 (2021): 165–75. http://dx.doi.org/10.1007/s41095-021-0235-7.

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AbstractWe consider semantic image segmentation. Our method is inspired by Bayesian deep learning which improves image segmentation accuracy by modeling the uncertainty of the network output. In contrast to uncertainty, our method directly learns to predict the erroneous pixels of a segmentation network, which is modeled as a binary classification problem. It can speed up training comparing to the Monte Carlo integration often used in Bayesian deep learning. It also allows us to train a branch to correct the labels of erroneous pixels. Our method consists of three stages: (i) predict pixel-wis

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San, Maocuo, Zhijie Cai, Rangzhuoma Cai, and Jizhaxi Dao. "Analysis on types of spelling errors in true Tibetan characters." MATEC Web of Conferences 336 (2021): 06019. http://dx.doi.org/10.1051/matecconf/202133606019.

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Spelling error checking is a challenging research topic with a wide range of applications such as text editing, word processing, spell checking, teaching, etc. As an alphabetic language, spelling errors in Tibetan could be categorized into three types, namely, non-true type, true type, and punctuation misuse. In order to study true Tibetan syllable spelling error in much more depth, the article analyses the types of Tr u e Tibetan syllable spelling errors based on Tibetan word formation rules, grammar and semantic features laying a foundation for Tibetan spelling error checking research.

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Ramli. "Kesalahan Makna Leksikal pada Terjemahan Teks Bahasa Indonesia ke dalam Bahasa Inggris." DIALEKTIKA: Jurnal Pendidikan Bahasa, Sastra, dan Matematika 1, no. 1 (2017): 43–55. https://doi.org/10.5281/zenodo.835623.

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The objective of this research was to investigate the error of lexical semantic found in the translation of Indonesian text into English text done by the English Education Program students Lakidende University, Southeast Sulawesi in 2013. It was a qualitative research with a content analysis method. The data were collected using written translation test. Based on the data analysis and interpretation, the semantic error of lexis consisted of: 1) Confusion of sense relation (General term for specific one, Specific term for general one, Co-hyponym, Near-synonym) and 2) Collocation. The sources of

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ASHTARI, SOMAYEH, MALIHE DANESH, and Hossein Shirgahi. "A NOVEL USER PROFILE-BASED FUZZY APPROACH FOR EVALUATING TRUST IN SEMANTIC WEB." IIUM Engineering Journal 20, no. 1 (2019): 158–76. http://dx.doi.org/10.31436/iiumej.v20i1.1060.

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ABSTRACT: As a developed World Wide Web architecture, the Semantic Web collects traditional web contents with a formal and understandable semantic using a machine. The main purpose of the Semantic Web is to increase automation, web information processing, and improve interactions and collaboration among information systems. The subject of trust is one of the main challenges in the semantic web. Since different tools and individuals exist in the semantic web, a certain measure of trust in an entity cannot be used and a central system is responsible for data collection and estimating the reliabi

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Rohmah, Idayatul, Fathur Rokhman, and Deby Luriawati. "KOMPETENSI SEMANTIK LEKSIKAL PADA PENYANDANG DOWN SYNDROME DI SLB NEGERI SEMARANG." Jurnal Sastra Indonesia 8, no. 1 (2019): 12–22. http://dx.doi.org/10.15294/jsi.v8i1.29853.

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Penelitian semantik leksikal pada penyandang down syndrome perlu dilakukan untuk mengungkap sejauh mana tentang kompetensi terhadap realitas yang ada di sekelilingnya. Dengan demikian, dapat dipaparkan pula tentang faktor yang mempengaruhi gejala penyimpangan tersebut. Tujuan penelitian ini adalah: (1) Menjelaskan kompetensi semantik leksikal pada penyandang down syndrome di SLB Negeri Semarang dilihat dari ketepatan dan penyimpangan dalam menguasai medan makna; dan (2) Mengklasifikasikan faktor yang mempengaruhi gejala penyimpangan semantik leksikal pada penyandang down syndrome di SLB Negeri

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Chen, Y., Z. He, M. Halper, L. Chen, and H. Gu. "Quality Assurance of UMLS Semantic Type Assignments Using SNOMED CT Hierarchies." Methods of Information in Medicine 55, no. 02 (2016): 158–65. http://dx.doi.org/10.3414/me14-01-0104.

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SummaryBackground: The Unified Medical Language System (UMLS) is one of the largest biomedical terminological systems, with over 2.5 million concepts in its Metathesaurus repository. The UMLS’s Semantic Network (SN) with its collection of 133 high-level semantic types serves as an abstraction layer on top of the Metathesaurus. In particular, the SN elaborates an aspect of the Metathesaurus’s concepts via the assignment of one or more types to each concept. Due to the scope and complexity of the Metathesaurus, errors are all but inevitable in this semantic-type assignment process.Objectives: To

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Mussiraliyeva, Shynar, Milana Bolatbek, Kuralay Azanbay, and Zhastay Yeltay. "Development of an error correction algorithm for Kazakh language." Journal of Mathematics, Mechanics and Computer Science 123, no. 3 (2024): 81–97. http://dx.doi.org/10.26577/jmmcs2024-v123-i3-8.

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This article discusses a method for correcting spelling errors in the Kazakh language using the advantages of morphological analysis and a model based on noisy channels. To achieve this goal, modern problems of automatic processing of Kazakh textual information were analyzed, existing linguistic resources and processing systems of the Kazakh language were systematized, the basic requirements for the development of a system for analyzing Kazakh textual information based on machine learning were determined, and models and algorithms for extracting facts from unstructured and poorly structured te

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Minkina, Irene, JoAnn P. Silkes, Lauren Bislick, et al. "The Influence of Phonomotor Treatment on Word Retrieval: Insights From Naming Errors." Journal of Speech, Language, and Hearing Research 62, no. 11 (2019): 4080–104. http://dx.doi.org/10.1044/2019_jslhr-l-19-0014.

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Purpose An increasing number of anomia treatment studies have coupled traditional word retrieval accuracy outcome measures with more fine-grained analysis of word retrieval errors to allow for more comprehensive measurement of treatment-induced changes in word retrieval. The aim of this study was to examine changes in picture naming errors after phonomotor treatment. Method Twenty-eight individuals with aphasia received 60 hr of phonomotor treatment, an intensive, phoneme-based therapy for anomia. Confrontation naming was assessed pretreatment, immediately posttreatment, and 3 months posttreat

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Anggraeni, Dwinesa. "SEMANTICS ERRORS COMMITED BY INDONESIAN CELEBRITIES: CLASSIFICATIONS AND MEANINGS." Scope : Journal of English Language Teaching 4, no. 2 (2020): 75. http://dx.doi.org/10.30998/scope.v4i2.6356.

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<div><p><strong>Abstract</strong></p><p><strong> </strong></p></div><p class="NoList1">Examining and studying semantics error is a challenging area, due to the position of English as the foreign language in Indonesia. The skill of Indonesian in using English might be as not as good as Singaporean, Malaysian, or Filipinos. Indonesian try to use some English words in their daily conversation or certain occasion. However, they tend to ignore the meanings, as the result the words used are semantically incorrect. Insufficient knowled

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Plaut, David C., and Tim Shallice. "Perseverative and Semantic Influences on Visual Object Naming Errors in Optic Aphasia: A Connectionist Account." Journal of Cognitive Neuroscience 5, no. 1 (1993): 89–117. http://dx.doi.org/10.1162/jocn.1993.5.1.89.

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Although perseveration—the inappropriate repetition of previous responses—is quite common among patients with neurological damage, relatively few detailed computational accounts of its various forms have been put forth. A particularly well-documented variety involves the pattern of errors made by “optic aphasic” patients, who have a selective deficit in naming visually presented objects. Based on our previous work in modeling impaired reading via meaning in deep dyslexia, we develop a connectionist simulation of visual object naming. The major extension in the present work is the incorporation

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Yu-Ge Liu, Yu-Ge Liu, Bin-Feng Tang Yu-Ge Liu, and Ying Huang Bin-Feng Tang. "A Modular Data Analysis for Ocean Surface Wind Prediction using Artificial Intelligence-based Semantic Verification." 電腦學刊 35, no. 6 (2024): 001–14. https://doi.org/10.53106/199115992024123506001.

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<p>Predicting ocean surface winds aids in forecasting precise weather conditions and tides for residential and commercial purposes. A Multi-Modular Semantic Data Analysis (M2SDA) method is proposed to address the missing data errors across different accumulation regions to improve the forecast precision. Large volumes of data from multiple oceanic regions are gathered by sensors deployed across the ocean bed and buoy sense. Artificial intelligence-based analysis was used to identify missing data errors. Considering that the time factor is confined, the forecast endurance based on sensing

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Peng, Jingquan, Yanqing Liu, and Haochen Jiang. "Semantic and edge-based visual odometry by joint minimizing semantic and edge distance error." Image and Vision Computing 113 (September 2021): 104240. http://dx.doi.org/10.1016/j.imavis.2021.104240.

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Malik, Nikita, Sanjay Kumar Malik, and Vanita Jain. "Semantic Web of Things for pollution measurement and validation interoperability using AI Techniques." Journal of Information and Optimization Sciences 45, no. 3 (2024): 765–84. http://dx.doi.org/10.47974/jios-1517.

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In response to the growing IoT device diversity, efforts are underway to better integrate data, applications, and services. The Semantic Web, known for its simplicity in integration, has the potential to improve data interpretation and interoperability. In this research, a pollution management model is used, combining the Semantic Web of Things (SWoT) and Artificial Intelligence (AI), to create smarter cities, providing real-time environmental information. The dataset has been sourced from Aarhus City, Denmark, and the study outlines Semantic Web Technologies (SWTs) in IoT frameworks, includin

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Wu, Xingjie. "A Computational Neural Network Model for College English Grammar Correction." Computational Intelligence and Neuroscience 2022 (September 5, 2022): 1–9. http://dx.doi.org/10.1155/2022/9592200.

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For the error correction of English grammar, if there are errors in the semantic units (words and sentences), it will inevitably affect the subsequent text analysis and semantic understanding, and ultimately reduce the overall performance of the practical application system. Therefore, intelligent error detection and correction of the word and grammatical errors in English texts is one of the key and difficult points of natural language processing. This exploration innovatively combines a computational neural model with college grammar error correction to improve the accuracy of college gramma

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Maesaroh, Siti, Tatang Tatang, and Nalahuddin Saleh. "Phonological Errors In Arabic Pronunciation Among Islamic Speech Contest Participants." INJECT (Interdisciplinary Journal of Communication) 10, no. 1 (2025): 661–80. https://doi.org/10.18326/inject.v10i1.4512.

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This study explores phonological errors in the pronunciation of Arabic scriptural references by participants in the Islamic Fest 2024 speech contest, analyzing underlying psycholinguistic factors. Employing a descriptive qualitative approach with phonetic and psycholinguistic perspectives, data were collected from two purposively selected participants' speech videos. Analysis involved an observe-and-note technique complemented by Praat software to examine acoustic features. Findings reveal two primary categories of phonological errors: linguistic errors, stemming from inadequate mastery of let

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