Semantic Errors Research Articles

OTT BL(Boys’ Love) 콘텐츠의 대중화 전략 및 수용 특징 연구

Focusing on the recent trend of producing and distributing a large number of BL (Boys' Love) contents on OTT(over-the-top) platforms, this study examined the background and popularization success strategy through a text and acceptance characteristics analysis of WATCHA Original’s series “Semantic Error”. “Semantic Error” is evaluated as a work that has been a catalyst for bringing BL to the public level, which was previously enjoyed by a small number of female fans with male homosexual tastes on specific platforms. A meticulous popularization strategy was hidden behind the spectacular report cards, including securing the long-term No. 1 viewer ranking creating topics of conversation, and achieving the commercial success of related IPs. As a result, “Semantic Error” spread to more audiences, leading to increased interest among IPs such as novels, webtoons, and dramas, as well as strategic level control aimed at expanding BL audiences and enjoying variations based on Super IP. “Semantic Error” is also meaningful in that it presented an evolved BL type by breaking away from the cliché of stereotypical BL such as 'seme' and 'uke'. The story development, which focuses on love, friendship, and growth, was recognized by viewers as an ordinary drama, not BL, and served as a pointt of attraction and competitiveness as a “content” which was confirmed through text and audience response analysis.

An Automated English Essay Scoring Engine Based on Neutrosophic Ontology for Electronic Education Systems

Most educators agree that essays are the best way to evaluate students’ understanding, guide their studies, and track their growth as learners. Manually grading student essays is a tedious but necessary part of the learning process. Automated Essay Scoring (AES) provides a feasible approach to completing this process. Interest in this area of study has exploded in recent years owing to the difficulty of simultaneously improving the syntactic and semantic scores of an article. Ontology enables us to consider the semantic constraints of the actual world. However, there are several uncertainties and ambiguities that cannot be accounted for by standard ontologies. Numerous AES strategies based on fuzzy ontologies have been proposed in recent years to reduce the possibility of imprecise knowledge presentation. However, no known efforts have been made to utilize ontologies with a higher level of fuzzification in order to enhance the effectiveness of identifying semantic mistakes. This paper presents the first attempt to address this problem by developing a model for efficient grading of English essays using latent semantic analysis (LSA) and neutrosophic ontology. In this regard, the presented work integrates commonly used syntactic and semantic features to score the essay. The integration methodology is implemented through feature-level fusion. This integrated vector is used to check the coherence and cohesion of the essay. Furthermore, the role of neutrosophic ontology is investigated by adding neutrosophic membership functions to the crisp ontology to detect semantic errors and give feedback. Neutrosophic logic allows the explicit inclusion of degrees of truthfulness, falsity, and indeterminacy. According to the comparison with state-of-the-art AES methods, the results show that the proposed model significantly improves the accuracy of scoring the essay semantically and syntactically and is able to provide feedback.

Twin Pseudo-training for semi-supervised semantic segmentation

Recent advancements in semi-supervised semantic segmentation have demonstrated the effectiveness of utilizing pseudo labels supervision to mitigate the limitations of pixel-wise annotations. However, pseudo-labels generated using self-training techniques typically contain a significant amount of noise, which can impede the training process of the supervised model. In this study, we identify low- and high-level semantic errors as the two key factors that hinder the accuracy of pseudo labels. To fully exploit the potential of pseudo labels, we introduce a novel semi-supervised framework named Twin Pseudo-training (TPseudo), which employs a consistency and disagreement collaboration strategy. Specifically, we correct pseudo labels with a False-positive Filter (FPF) to reduce high-level semantic noise and refine low-level semantic biases using a Semantic Error Detector (SED). Lastly, we design a Self-Adaptive Weight (SAW) loss function based on a disagreement between two predictions to exploit each pixel of pseudo labels. Experimental results on the standard benchmarks PASCAL VOC2012 and Cityscapes demonstrate the efficacy of the proposed method.

Grammatical Error Correction: A Survey of the State of the Art

Abstract Grammatical Error Correction (GEC) is the task of automatically detecting and correcting errors in text. The task not only includes the correction of grammatical errors, such as missing prepositions and mismatched subject–verb agreement, but also orthographic and semantic errors, such as misspellings and word choice errors, respectively. The field has seen significant progress in the last decade, motivated in part by a series of five shared tasks, which drove the development of rule-based methods, statistical classifiers, statistical machine translation, and finally neural machine translation systems, which represent the current dominant state of the art. In this survey paper, we condense the field into a single article and first outline some of the linguistic challenges of the task, introduce the most popular datasets that are available to researchers (for both English and other languages), and summarize the various methods and techniques that have been developed with a particular focus on artificial error generation. We next describe the many different approaches to evaluation as well as concerns surrounding metric reliability, especially in relation to subjective human judgments, before concluding with an overview of recent progress and suggestions for future work and remaining challenges.We hope that this survey will serve as a comprehensive resource for researchers who are new to the field or who want to be kept apprised of recent developments.

Listening to Yourself and Watching Your Tongue: Distinct Abilities and Brain Regions for Monitoring Semantic and Phonological Speech Errors.

Despite the many mistakes we make while speaking, people can effectively communicate because we monitor our speech errors. However, the cognitive abilities and brain structures that support speech error monitoring are unclear. There may be different abilities and brain regions that support monitoring phonological speech errors versus monitoring semantic speech errors. We investigated speech, language, and cognitive control abilities that relate to detecting phonological and semantic speech errors in 41 individuals with aphasia who underwent detailed cognitive testing. Then, we used support vector regression lesion symptom mapping to identify brain regions supporting detection of phonological versus semantic errors in a group of 76 individuals with aphasia. The results revealed that motor speech deficits as well as lesions to the ventral motor cortex were related to reduced detection of phonological errors relative to semantic errors. Detection of semantic errors selectively related to auditory word comprehension deficits. Across all error types, poor cognitive control related to reduced detection. We conclude that monitoring of phonological and semantic errors relies on distinct cognitive abilities and brain regions. Furthermore, we identified cognitive control as a shared cognitive basis for monitoring all types of speech errors. These findings refine and expand our understanding of the neurocognitive basis of speech error monitoring.

ANALISIS KESALAHAN BERBAHASA DALAM TATARAN SEMANTIK PADA POSTINGAN AKUN INSTAGRAM @demauinsurakarta

This research is motivated by the use of the Indonesian language where there are still many errors found, especially at the semantic level. The purpose of this study is to find out language errors at the semantic level contained in the @demauinsurakarta account. This study uses a qualitative descriptive method approach. The data source in this study is the account caption @demauinsurakarta. This study uses data analysis methods in the form of references from the Kamus Besar Bahasa Indonesia (KBBI) and Pedoman Umum Ejaan Bahasa Indonesia (PUEBI). The results of this study found semantic level errors in the field of hypercorrection found 7 errors, pleonasm found 8 errors, inappropriate word choice or diction found 3 errors and word selection which resulted in ambiguity meaning found 3 errors.

Supremacy of suprasegmentals in Arabic phonology: Evidence from malapropisms

Speech errors are an important source of information to understand language processing and production. Earlier research focused on different types of errors including semantic and phonological errors while malapropisms, which refer to slips of the tongue involving whole word substitutions that share phonological similarities but are not related semantically, have not received adequate attention in the Arabic language. Drawing on malapropisms in Jordanian Arabic, we bring evidence on the supremacy of suprasegmental phonological aspects in Arabic phonology. This is unexpected as stress in Arabic is non-phonemic and fully predictable, besides Arabic rhythm is much less stress-timed than that of Germanic languages. Data was collected from spontaneous speech over a period of three years. Results showed that malapropisms share the primary stress position, the number of syllables and the word rhythmic pattern with the target words. To a lesser degree, the target and the error share the same rime and initial segments. Findings suggest that suprasegmental features are very crucial in Arabic phonology, like in Indo-European languages. Evidence suggests that formal similarity that is based on the syllabic and metrical structure of words plays a significant role in language processing and the organization of the mental lexicon in Arabic, which suggests that this is a language universal. Furthermore, our findings do not agree with earlier claims that Arabic has a flat syllabic structure. Rather, evidence suggests that Arabic, like English, has a hierarchical syllable structure, which seems to represent another language universal. More research on other Arabic dialects is recommended to corroborate these findings. Disclosure statement No potential conflict of interest was reported by the authors. * Corresponding author: Mohammed Nour Abu Guba, 0000-0002-5007-6439 mabu-gub@sharjah.ac.ae

Functional connectivity explains how neuronavigated TMS of posterior temporal subregions differentially affect language processing

Background“Wernicke's area” is most often used to describe the posterior superior temporal gyrus (STG) and refers to a region traditionally thought to support language comprehension. However, the posterior STG additionally plays a critical role in language production. The purpose of the current study was to determine to what extent regions within the posterior STG are selectively recruited during language production. Methods23 healthy right-handed participants completed an auditory fMRI localizer task, resting-state fMRI and underwent neuronavigated TMS language mapping. We applied repetitive TMS bursts during a picture naming paradigm to probe speech disruptions of different categories (anomia, speech arrest, semantic paraphasia and phonological paraphasia). We combined an in-house built high precision stimulation software suite with E-field modeling to map the naming errors to cortical regions and revealed a dissociation of language functions within the temporal gyrus. Resting state fMRI was used to explain how E-field peaks of different categories differentially affected language production. ResultsPeaks for phonological and semantic errors were found in the STG while those for anomia and speech arrest were located in the MTG. Seed-based connectivity analysis revealed a local connectivity pattern for phonological and semantic errors, while anomia and speech arrest seeds resulted in a larger network between IFG and posterior MTG. ConclusionsOur study provides important insights into the functional neuroanatomy of language production and might help to increase the current understanding of specific language production difficulties on a causal level.

THE PROBLEMS OF VIDEO GAMES TRANSLATION INTO THE UKRAINIAN LANGUAGE

The study outlines a number of problems faced by the translator when translating video games from the English language into the Ukrainian language. The demand for entertainment software is growing, and at the same time, it is encouraging video game creators to translate more of their products. However, in order to achieve a high level of popularization, the nature of multimedia interactive entertainment software products requires special translation, i.e. localization. Localization of video games is a complex process that is not sufficiently researched to date. The article attempts to determine the nature of the difficulties faced by the translator, such as the choice of translation strategies, the translation of cultural realities, the translation of the comic, and others. Attention is focused on the fact that semantic errors are often made during localization, which in their turn are divided into gross distortion of the original content, inaccuracy and ambiguity, and linguistic errors, among which lexical, grammatical, stylistic, spelling and punctuation errors can be singled out. We conclude that in order to achieve the adequacy of the translation, it is necessary to apply skillfully a number of translation transformations, namely transliteration, transcription, loaning (when translating proper names, names of objects, phenomena, events, localities), as well as search for effective approaches to the adaptation of various cultural aspects and historical features, during which transcreation (creative translation) turns out to be apt.

Three Principles of Intellectualization of Automated Systems

Three Principles of Intellectualization of Automated Systems

Methods of teaching graphic-symbolic analysis in the literary and cultural-historical context of the 20th century (in terms of the novel ‟The Master and Margarita” by Mikhail Bulgakov)

The paper discusses the methodology for teaching graphic-symbolic analysis in the literary and cultural-historical context of the 20th century in terms of the novel ‟The Master and Margarita” by Mikhail Bulgakov. This method of analysis is an effective method of teaching oral monologue speech to foreign students. The purpose of this study is to develop the oral coherent speech of foreign students with the help of a graphical and symbolic analysis of the text of the novel ‟The Master and Margarita”. The set goal involves solving the following tasks: to help students from China understand the artistic features of the works by Mikhail Bulgakov, to acquire the skills of literary text analysis and, in general, to study Russian literature more deeply. The material of the presented study can be used in the preparation of training courses designed to teach the Russian language and literature to Chinese students, as well as in the development of special courses on the specifics of literature. The research methods were chosen in accordance with the object of study, its subject, purpose and objectives, our methodology is based on the use of practice-oriented provisions of semiotics, artistic and graphic-symbolic analysis of the text and the introduction of a new concept of ‟sign word” into the system of dictionary work. This study determines the effectiveness of using the method of teaching graphic-symbolic analysis in the educational process at different stages of teaching literature and the Russian language to foreign students. The novelty of the study lies in the analysis of materials relating to the peculiarities of the perception of Russian fiction by native speakers of the Chinese language. The theoretical significance of the work lies in the study of the specifics of Russian works and language in the aspect of Russian as a foreign language. The practical significance lies in the prevention of lexical and grammatical errors when using the Russian language and semantic errors in the analysis of Russian literature.

Semantic interference affects speech production by increasing disfluencies, not errors

Several studies have shown that different types of disfluency occur depending on the language production stage at which people experience difficulties. The current study combined a network task and a picture–word interference task to analyse whether lexical-semantic difficulty triggers errors and disfluencies in connected-speech production. The participants produced more disfluencies in the presence of a semantically related distractor word than an unrelated distractor word, while few semantic errors were made. These results support the hypothesis that difficulties at distinct stages of language production lead to distinct patterns of disfluency, with lexical-semantic difficulties leading to self-corrections and silent pauses. The results also have implications for the role of the monitoring system in connected-speech production.

Effectively fusing clinical knowledge and AI knowledge for reliable lung nodule diagnosis

Clinicians typically use semantic features to judge the malignant status of nodules, while artificial intelligence systems (AI) tend to extract unknown features to diagnose nodules. The former relies on clinical knowledge, while the latter explores AI knowledge. Although many studies indicate that fusing clinical and AI knowledge can help computer-aided diagnosis (CAD) systems improve diagnostic accuracy and gain clinician approval, how to effectively fuse them is still an open question. This paper proposes a simple and effective pipeline (abbreviated as CKAK), which fuses clinical and AI knowledge at both feature and decision levels for accurate lung nodule malignancy classification and semantic attributes characterization. The feature-level fusion can retain rich information in high-dimensional features and improve the model’s accuracy; the decision-level fusion can provide some interpretability for the model’s decision-making process, which is expected in clinical applications. Specifically, the proposed CKAK consists of two sequential stages: (i) the initial prediction stage (IPS); and (ii) the prediction refine stage (PRS). The IPS predicts eight radiologist-interpreted semantic attributes and an initial malignancy diagnosis in parallel. Then, these results are fed to the subsequent PRS to refine the diagnosis further by fully fusing them at feature and decision levels. Besides, to enhance the ability of feature learning, we propose a novel scale-aware feature extraction block (SAFE). It integrates multi-scale contextual features with a lightweight Transformer rather than adding or concatenating them roughly. Extensive experiments at the LIDC-IDRI data set show that the proposed CKAK can achieve superior benign-malignant classification accuracy with minor radiologist-interpreted semantic scores error, meeting the need for a reliable CAD system.

The Impact of Negative Transfer of Mother Tongue on Learning Chinese in Senior Primary Schools in Thailand and Teaching Strategies

Mother tongue negative transfer refers to the errors in grammar, pronunciation, semantics and other aspects that learners make due to the influence of their mother tongue when learning a new language, which has a certain impact on the learning of Chinese by Thai primary school students. This article analyzes the problem of mother tongue negative transfer in the learning of Chinese by Thai primary school students and proposes corresponding teaching strategies to help students better learn Chinese.

Artificial Intelligence Can Generate Fraudulent but Authentic-Looking Scientific Medical Articles: Pandora's Box Has Been Opened.

Artificial intelligence (AI) has advanced substantially in recent years, transforming many industries and improving the way people live and work. In scientific research, AI can enhance the quality and efficiency of data analysis and publication. However, AI has also opened up the possibility of generating high-quality fraudulent papers that are difficult to detect, raising important questions about the integrity of scientific research and the trustworthiness of published papers. The aim of this study was to investigate the capabilities of current AI language models in generating high-quality fraudulent medical articles. We hypothesized that modern AI models can create highly convincing fraudulent papers that can easily deceive readers and even experienced researchers. This proof-of-concept study used ChatGPT (Chat Generative Pre-trained Transformer) powered by the GPT-3 (Generative Pre-trained Transformer 3) language model to generate a fraudulent scientific article related to neurosurgery. GPT-3 is a large language model developed by OpenAI that uses deep learning algorithms to generate human-like text in response to prompts given by users. The model was trained on a massive corpus of text from the internet and is capable of generating high-quality text in a variety of languages and on various topics. The authors posed questions and prompts to the model and refined them iteratively as the model generated the responses. The goal was to create a completely fabricated article including the abstract, introduction, material and methods, discussion, references, charts, etc. Once the article was generated, it was reviewed for accuracy and coherence by experts in the fields of neurosurgery, psychiatry, and statistics and compared to existing similar articles. The study found that the AI language model can create a highly convincing fraudulent article that resembled a genuine scientific paper in terms of word usage, sentence structure, and overall composition. The AI-generated article included standard sections such as introduction, material and methods, results, and discussion, as well a data sheet. It consisted of 1992 words and 17 citations, and the whole process of article creation took approximately 1 hour without any special training of the human user. However, there were some concerns and specific mistakes identified in the generated article, specifically in the references. The study demonstrates the potential of current AI language models to generate completely fabricated scientific articles. Although the papers look sophisticated and seemingly flawless, expert readers may identify semantic inaccuracies and errors upon closer inspection. We highlight the need for increased vigilance and better detection methods to combat the potential misuse of AI in scientific research. At the same time, it is important to recognize the potential benefits of using AI language models in genuine scientific writing and research, such as manuscript preparation and language editing.

Comparing two picture naming tasks in primary progressive aphasia: Insights from behavioural and neural results

Picture naming tests are widely used to evaluate language impairments in neurodegenerative diseases, especially in Primary Progressive Aphasia (PPA). The available tests differ for many factors affecting the performance, e.g. format of stimuli and their psycholinguistic properties. We aim to identify the most appropriate naming test to be used on PPA according to the clinical and research demands. We investigated the behavioural characteristics, i.e. proportion of correct responses and error type, and their neural correlates in two Italian naming tests, CaGi naming (CaGi) and naming subtest of the Screening for Aphasia in NeuroDegeneration battery (SAND), administered to 52 PPA patients who underwent an FDG-PET scan. We analysed the effectiveness of the tests in distinguishing PPA versus controls and among PPA variants, considering the psycholinguistic variables affecting performance. We explored the brain metabolic correlates of behavioural performance in the tests. SAND, differently from CaGi, has time limits for the response and its items are less frequent and acquired later. SAND and CaGi differed in terms of number of correct responses and error profile, suggesting a higher difficulty to name SAND items compared to CaGi. Semantic errors predominated in CaGi, while anomic and semantic errors were equally frequent in SAND. Both tests distinguished PPA from controls, but SAND outperformed CaGi in discriminating among PPA variants. FDG-PET imaging revealed a shared metabolic involvement of temporal areas associated with lexico-semantic processing, encompassing anterior fusiform, temporal pole, and extending to posterior fusiform in sv-PPA. Concluding, a picture naming test with response time limit and items which are less frequent and acquired later in life, as SAND, may be effective at highlighting subtle distinctions between PPA variants, improving the diagnosis. Conversely, a naming test without time limit for the response, as CaGi, may be useful for a better characterization of the nature of the naming impairment at the behavioural level, eliciting more naming errors than anomia, possibly helping in the development of rehabilitation protocols.

Linguistic anomalies observed in the Sentence Completion Test in patients with schizophrenia

ABSTRACT Background Schizophrenia is a chronic, debilitating disorder characterised by distorted thinking, perceptions, behaviours, and even language impairments. We investigated the linguistic anomalies in Korean schizophrenia patients compared to non-psychotic psychiatric controls to determine whether the linguistic anomalies in English speakers with schizophrenia were replicated in Korean speakers. Methods Thirty-four schizophrenia patients and 70 non-psychotic psychiatric controls were included in this study. The SCT was utilised as the text data for analysis. For linguistic analysis, we evaluated texts regarding semantics and syntax. We separately counted the number of semantic or syntactic errors in the written texts of study participants and compared them between patients and controls. Results Schizophrenia patients showed significantly more semantic errors (p < .001) and syntactic errors (p < .001) per 1,000 characters than non-psychotic psychiatric controls. Specifically, inappropriate word or syntactic component selection is noticeable in schizophrenia patients. These differences were still significant after adjusting for general intelligence measured by the K-WAIS-IV. Conclusion Schizophrenia patients showed both semantic and syntactic errors in written language. Moreover, these errors seemed to be partly independent of general intelligence. Notably, patients showed a noticeable number of syntactic errors. Further investigation into the language of patients with schizophrenia and schizophrenia-spectrum disorders is required.

Semantic-guided 3D building reconstruction from triangle meshes

Planar primitives tend to be incorrectly detected or incomplete in complex scenes where adhesions exist between different objects, resulting in topology errors in the reconstructed models. We propose a semantic-guided building reconstruction method known as semantic-guided reconstruction (SGR), which is capable of achieving the independence and integrity of building models in two key stages. In the first stage, the space partition is represented by a 2.5D convex cell complex and is capable of restoring planar primitives that are easily lost and can further infer the potential structural adaptivity. The second stage incorporates semantic information into a graph-cut formulation that can assist in the independent reconstruction of buildings while eliminating interference from the surrounding environment. Our experimental results confirmed that the SGR method can authentically reconstruct weakly observed surfaces. Furthermore, qualitative and quantitative evaluations show that SGR is suitable for reconstructing surfaces from insufficient data with semantic and geometric ambiguity or semantic errors and can obtain watertight models considering fidelity, integrity and time complexity.

Characterization of reading errors in languages with different orthographic regularity: an Italian–English comparison

The study examined whether a classification of errors based on Hendriks and Kolk’s (1997) proposal would effectively characterize the reading profile of children learning two orthographies varying for regularity, such as Italian and English. The study considered both an age-match and a grade-match comparison. Offline analysis of error production was carried out for two lists of stimuli: List 1 including regular words varying for frequency and matched non-words and List 2 including low-frequency words varying for regularity. In List 1, Italian-reading children made more multiple attempts characterized by a slow and progressive approach to the target (sounding-out behavior) than English-reading children, while the latter made relatively more word substitutions and non-word lexicalizations. As for List 2, Italian-reading children made relatively more multiple attempts and progressive approaches to the target compared to the English-reading children (with more sounding-out behaviors and syllabications), while the opposite occurred for phonological-visual errors, word substitutions, morphological, and semantic errors. Both groups showed a high proportion of phonological-visual and regularization errors (stress assignment in the case of Italian-reading children). Overall, the use of an error coding system specifically tuned to the characteristics of the orthographies investigated allowed a more comprehensive identification of reading difficulties which allowed the different strategies used by children of different languages to emerge more clearly (more reliance on sub-lexical routines in Italian readers and on lexical routines in English readers). These results call for more attention to error patterns in the identification of reading difficulties in children of different languages including those learning a transparent orthography where error analyses have largely been ignored.

Accuracy of Naming Error Profiles Elicited From Adaptive Short Forms of the Philadelphia Naming Test.

The purpose of this study was to evaluate whether a short-form computerized adaptive testing (CAT) version of the Philadelphia Naming Test (PNT) provides error profiles and model-based estimates of semantic and phonological processing that agree with the full test. Twenty-four persons with aphasia took the PNT-CAT and the full version of the PNT (hereinafter referred to as the "full PNT") at least 2 weeks apart. The PNT-CAT proceeded in two stages: (a) the PNT-CAT30, in which 30 items were selected to match the evolving ability estimate with the goal of producing a 50% error rate, and (b) the PNT-CAT60, in which an additional 30 items were selected to produce a 75% error rate. Agreement was evaluated in terms of the root-mean-square deviation of the response-type proportions and, for individual response types, in terms of agreement coefficients and bias. We also evaluated agreement and bias for estimates of semantic and phonological processing derived from the semantic-phonological interactive two-step model (SP model) of word production. The results suggested that agreement was poorest for semantic, formal, mixed, and unrelated errors, all of which were underestimated by the short forms. Better agreement was observed for correct and nonword responses. SP model weights estimated by the short forms demonstrated no substantial bias but generally inadequate agreement with the full PNT, which itself showed acceptable test-retest reliability for SP model weights and all response types except for formal errors. Results suggest that the PNT-CAT30 and the PNT-CAT60 are generally inadequate for generating naming error profiles or model-derived estimates of semantic and phonological processing ability. Post hoc analyses suggested that increasing the number of stimuli available in the CAT item bank may improve the utility of adaptive short forms for generating error profiles, but the underlying theory also suggests that there are limitations to this approach based on a unidimensional measurement model. https://doi.org/10.23641/asha.22320814.