Sentence Level Research Articles

Investigating the Impact of Semantic Operations on PersianSpeaking Aphasics: Further Evidence on the Localization View

Introduction: Broca and Wernicke’s patients perform satisfactorily regarding the processing of canonical syntactic structures, as maintained by previous studies; however, there has been a gap in the literature because no particular research has yet investigated the performance of these patients in the Persian circumstances once they were required to analyze sentences which would demand extra-semantic processing. This study clarifies the role of two critical semantic operations demanding extra-semantic processing at the sentential level: Aspectual coercion. It complements to provide some evidence on the localist view of the brain. Our rationale for selecting these operations was their pure semantic nature, not relying on morphosyntactic properties.
 Materials and Methods: Having recruited two age- and education-matched Broca, two Wernicke, and four healthy controls, we conducted a semantic judgment task in which the participants were asked to express their correct semantic judgment in the two coercion and two normal conditions.
 Results: Our results showed an approximately above-chance performance of the Broca group for all conditions; however, in the Wernicke group, the same result was not observed due to their poor performance in coercion conditions, though in ordinary sentences, they performed much better.
 Conclusion: Our findings, along with similar off-line and imaging studies, corroborate the view of localism, based on which Wernicke’s area is mainly responsible for the primary semantic operations while Broca’s area predominantly takes over syntactic parsing.

Penerapan Sistem Informasi Untuk Identifikasi Literasi Pada Siswa Disabilitas Taman Kanak-Kanak dan Siswa Sekolah Dasar

Students with disabilities are students who experience physical, intellectual, mental and/or sensory limitations for a long period of time, who in interacting with the environment can experience obstacles and difficulties in participating fully and effectively with the people around them. Students with disabilities need special ways and methods to determine children's development, especially in their reading literacy. Children's reading comprehension levels need to be measured and tested to make it easier to group study groups. The grouping of children's reading comprehension is grouped based on the child's level of understanding, namely basic level, letter level, word level, sentence level, paragraph level and description level. Grouping can be done by testing and measuring children's level of understanding based on these levels. This can be done quickly and accurately if supported by an information system that answers these needs. The aim of this research is to create an information system for mapping the reading literacy of students with disabilities against student difficulties. The system also makes it easier for teachers or agencies to identify children's reading abilities so that they can be grouped based on level and difficulty

Developing a Framework to Infer Opioid Use Disorder Severity From Clinical Notes to Inform Natural Language Processing Methods: Characterization Study.

Information regarding opioid use disorder (OUD) status and severity is important for patient care. Clinical notes provide valuable information for detecting and characterizing problematic opioid use, necessitating development of natural language processing (NLP) tools, which in turn requires reliably labeled OUD-relevant text and understanding of documentation patterns. To inform automated NLP methods, we aimed to develop and evaluate an annotation schema for characterizing OUD and its severity, and to document patterns of OUD-relevant information within clinical notes of heterogeneous patient cohorts. We developed an annotation schema to characterize OUD severity based on criteria from the Diagnostic and Statistical Manual of Mental Disorders, 5th edition. In total, 2 annotators reviewed clinical notes from key encounters of 100 adult patients with varied evidence of OUD, including patients with and those without chronic pain, with and without medication treatment for OUD, and a control group. We completed annotations at the sentence level. We calculated severity scores based on annotation of note text with 18 classes aligned with criteria for OUD severity and determined positive predictive values for OUD severity. The annotation schema contained 27 classes. We annotated 1436 sentences from 82 patients; notes of 18 patients (11 of whom were controls) contained no relevant information. Interannotator agreement was above 70% for 11 of 15 batches of reviewed notes. Severity scores for control group patients were all 0. Among noncontrol patients, the mean severity score was 5.1 (SD 3.2), indicating moderate OUD, and the positive predictive value for detecting moderate or severe OUD was 0.71. Progress notes and notes from emergency department and outpatient settings contained the most and greatest diversity of information. Substance misuse and psychiatric classes were most prevalent and highly correlated across note types with high co-occurrence across patients. Implementation of the annotation schema demonstrated strong potential for inferring OUD severity based on key information in a small set of clinical notes and highlighting where such information is documented. These advancements will facilitate NLP tool development to improve OUD prevention, diagnosis, and treatment.

Dissecting The Analects: an NLP-based exploration of semantic similarities and differences across English translations

The Analects, a classic Chinese masterpiece compiled during China’s Warring States Period, encapsulates the teachings and actions of Confucius and his disciples. The profound ideas it presents retain considerable relevance and continue to exert substantial influence in modern society. The availability of over 110 English translations reflects the significant demand among English-speaking readers. Grasping the unique characteristics of each translation is pivotal for guiding future translators and assisting readers in making informed selections. This research builds a corpus from translated texts of The Analects and quantifies semantic similarity at the sentence level, employing natural language processing algorithms such as Word2Vec, GloVe, and BERT. The findings highlight semantic variations among the five translations, subsequently categorizing them into “Abnormal,” “High-similarity,” and “Low-similarity” sentence pairs. This facilitates a quantitative discourse on the similarities and disparities present among the translations. Through detailed analysis, this study determined that factors such as core conceptual words, and personal names in the translated text significantly impact semantic representation. This research aims to enrich readers’ holistic understanding of The Analects by providing valuable insights. Additionally, this research offers pragmatic recommendations and strategies to future translators embarking on this seminal work.

A Corpus-Based Study on the Translation Style of Four English Versions of Fu Sheng Liu Ji

ABSTRACT Based on a parallel corpus of Chinese-English texts, this article utilizes corpus tools to examine the translation styles of four versions of Fu Sheng Liu Ji from three levels and nine variables. It is found that different translators have different perspectives and focuses when dealing with the same source text, thus presenting different translation styles. At vocabulary level, Sanders’ translation has the highest lexical density, Black’s translation has the richest lexical variation, and Pratt’s translation has the lowest lexical richness. At the sentence level, Lin’s translation fluctuated more obviously in the use of alternating long and short sentences, while the changes in Sanders’ translation are less obvious, and Pratt’s translation has the lowest average sentence length, which means this version tends to use more short sentences to translate the original text. At the level of discourse, Black’s translation has a more compact plot and a tighter contextual connection, making the translation more readable and narrative. The distinct styles of the four translations are mainly due to the difference in the translators’ aims and their historical backgrounds.

Decoding Bilingual EEG Signals With Complex Semantics Using Adaptive Graph Attention Convolutional Network.

Decoding neural signals of silent reading with Brain-Computer Interface (BCI) techniques presents a fast and intuitive communication method for severely aphasia patients. Electroencephalogram (EEG) acquisition is convenient and easily wearable with high temporal resolution. However, existing EEG-based decoding units primarily concentrate on individual words due to their low signal-to-noise ratio, rendering them insufficient for facilitating daily communication. Decoding at the word level is less efficient than decoding at the phrase or sentence level. Furthermore, with the popularity of multilingualism, decoding EEG signals with complex semantics under multiple languages is highly urgent and necessary. To the best of our knowledge, there is currently no research on decoding EEG signals during silent reading of complex semantics, let alone decoding silent reading EEG signals with complex semantics for bilingualism. Moreover, the feasibility of decoding such signals remains to be investigated. In this work, we collect silent reading EEG signals of 9 English Phrases (EP), 7 English Sentences (ES), 10 Chinese Phrases (CP), and 7 Chinese Sentences (CS) from the subject within 26 days. We propose a novel Adaptive Graph Attention Convolution Network (AGACN) for classification. Experimental results demonstrate that our proposed method outperforms state-of-the-art methods, achieving the highest classification accuracy of 54.70%, 62.26%, 44.55%, and 57.14% for silent reading EEG signals of EP, ES, CP, and CS, respectively. Moreover, our results prove the feasibility of complex semantics EEG signal decoding. This work will aid aphasic patients in achieving regular communication while providing novel ideas for neural signal decoding research.

Enhancing Narrative Commonsense Reasoning With Multilevel Causal Knowledge.

Narratives is an account of the unfolding of events, along with explanations of how and why these processes and events came to be. To understand narratives, causality has been proven to be especially useful. Causality manifests itself primarily at both the event and sentence levels, offering essential insights into understanding narratives. However, previous works utilize either sentence-level or event-level causalities. In this article, we devise a two-stage approach to fully exploit both levels of causal relationships. In the first stage, by devising posttraining tasks, we inject sentence-level causalities into pretrained language models (PLMs). The causal-enhanced PLMs, which carry sentence-level causalities, can be transferred to downstream tasks. In the second stage, we utilize event causalities to further refine narrative commonsense reasoning. But, the event sparsity problem brings about the difficulty to learn event representations and capture useful causal semantics. To alleviate this problem, we break down events into multiple word components, enabling the retrieval of word-word relations between these components. And it is possible to alleviate the event sparsity problem since word-word relations capture the interplays between event components. Based on the event-level causalities and the word-level relations, we construct the hierarchical knowledge graph (KG) as knowledge ground. A KG-based reasoning process is then employed for narrative commonsense reasoning. Experimental results affirm the effectiveness of our framework.

BELT: Bootstrapped EEG-to-language Training by Natural Language Supervision.

Decoding natural language from noninvasive brain signals has been an exciting topic with the potential to expand the applications of brain-computer interface (BCI) systems. However, current methods face limitations in decoding sentences from electroencephalography (EEG) signals. Improving decoding performance requires the development of a more effective encoder for the EEG modality. Nonetheless, learning generalizable EEG representations remains a challenge due to the relatively small scale of existing EEG datasets. In this paper, we propose enhancing the EEG encoder to improve subsequent decoding performance. Specifically, we introduce the discrete Conformer encoder (D-Conformer) to transform EEG signals into discrete representations and bootstrap the learning process by imposing EEG-language alignment from the early training stage. The D-Conformer captures both local and global patterns from EEG signals and discretizes the EEG representation, making the representation more resilient to variations, while early-stage EEG-language alignment mitigates the limitations of small EEG datasets and facilitates the learning of the semantic representations from EEG signals. These enhancements result in improved EEG representations and decoding performance. We conducted extensive experiments and ablation studies to thoroughly evaluate the proposed method. Utilizing the D-Conformer encoder and bootstrapping training strategy, our approach demonstrates superior decoding performance across various tasks, including word-level, sentence-level, and sentiment-level decoding from EEG signals. Specifically, in word-level classification, we show that our encoding method produces more distinctive representations and higher classification performance compared to the EEG encoders from existing methods. At the sentence level, our model outperformed the baseline by 5.45%, achieving a BLEU-1 score of 42.31%. Furthermore, in sentiment classification, our model exceeded the baseline by 14%, achieving a sentiment classification accuracy of 69.3%.

Research on the Method and Dataset for Aspect Sentiment Triplet Extraction from Documents

With the increasing frequency of documents appearing in scenarios including online comments, car reviews and movie evaluations, the text length in sentiment analysis tasks has become longer. Since documents consist of multiple sentences, aspect terms and opinion terms in aspect sentiment triplets may not be in the same sentence, which requires multi-hop reasoning across sentences for extraction, which exceeds the scope of intra-sentence aspect sentiment triplet extraction. Apparently, the existing models at the sentence level are not suitable for extracting sentiment triples directly from documents. Therefore, a well-performing document sentiment analysis model is needed to address the degraded performance of triplet extraction caused by cross-sentence sentiment elements in long texts. To promote the development of aspect sentiment triplet extraction from documents, a dataset based on movie review texts is proposed and the existing methods are evaluated based on this dataset. Through error analysis, this study explores the difficulties and possible future research directions of document-level aspect sentiment triplet extraction.

Application of Corpus Analysis Software and Error Analysis Theory on Non-English Majors’ Chinese-English Translation Features

This research focuses on non-English major students’ Chinese-English (C-E) translation features. It aims at annotating and summarizing the features and error types in students’ C-E translation by applying to corpus analysis software Readability Analyzer. This study first collects students’ C-E translation texts from a computer-based exam, then forms a self-constructed corpus and a reference corpus. The translation materials in the target corpus and the reference corpus are first analyzed and compared by Readability Analyzer to figure out the features of students C-E translation texts from the perspectives of sentence length, word length, percentage of difficult words, Flesch Reading Ease, Dale-Chall Score, Fry Readability Grade Level. After that, different types of errors in students C-E translation are labeled and analyzed based on statistical analysis and Error Analysis (EA) theory. According to Readability Analyzer, students C-E translation texts have the following features. From passage statistics, it shows that translation texts that get lower scores tend to use shorter sentences and easier words. In addition, from readability scores, Flesch scores, Dale-Chall Scores shows and Fry Readability Grade Level are taken into consideration. Flesch scores shows that compared with the reference corpus, students’ translation which get lower scores are harder to understand since there are more language errors than higher-score translation. Dale-Chall Scores indicate that translation materials which score 8-9 are very close in the difficulty of word-using with the reference corpus. Fry Readability Grade Level shows that texts of lower scores are simpler and more accurate than texts of higher scores. According to Error Analysis (EA) Theory, students’ errors are further classified and discussed on lexical level, sentence level and textual level. The conclusions of this research are expected to make contributions to the improvement of C-E translation teaching and help college students, especially non-English majors, to enhance their C-E translation competence.

Analysis of Jamaica Kincaid’s Girl from the Perspective of Feminist Stylistics

Girl is contemporary Antiguan-American writer Jamaica Kincaid’s autobiographical writing, which describes a girl dictated by the instructions of her demanding mother about how to behave properly like a lady. Through the narration of daily life between mother and daughter, the influence of colonial history and colonial culture on the family life of African Caribbean women is reflected in the work. With the analysis at three different levels of words, sentence and discourse from the perspective of feminist stylistics, the weak feminine role and the inferior social status of women in Antigua are explored. A traditional and typical Caribbean mother and daughter relationship of the controlling mother with the obedient daughter is revealed.

A Study of Business English Translation Skills Based on Parallel Corpus

Abstract In this paper, the support vector machine is used as the overall classification of the paragraph alignment method. The paragraph length relationship is taken as an essential feature of the business English paragraph alignment model, the CRFs discriminative model is used as the word alignment task model, and the maximum likelihood estimation algorithm is used for model parameter training. Aiming at the real-time alignment needs at the sentence or paragraph level in Business English, the word alignment algorithm based on semantic similarity calculation is proposed, and the evaluation indexes of English-Chinese EC, Chinese-English CE unidirectional alignment and bi-directional fusion alignment, and semantic extension alignment are compared. Adding traditional business English translation techniques, comparing the effects of conventional translation techniques on business English sentence and paragraph translation versus parallel corpus-based translation techniques. The dynamic chunking accuracy of two-way fusion alignment and semantic expansion alignment is 94.57% and 94.32%, respectively, and the accuracy of semantic expansion alignment is 6.85% and 8.17% higher than that of EC one-way alignment. Meanwhile, the number of errors in business English translation works based on parallel corpora is reduced, which can help translators overcome the interference of Chinese chapter structures and improve the quality of their translation works.

OVERCOMING PSYCHOLOGICAL BARRIERS TO WRITE: A COGNITIVE COACHING APPROACH TO PLANNING WRITING

The current research examines a common issue faced by high school students in Morocco, specifically in the area of sentence construction. The study uses action research embracing a cognitive coaching approach to identify and address the problem. The researcher hypothesizes that students' writing difficulties extend beyond the sentence level to the paragraph level. To address this issue, the researcher implements several activities which aim to raise students' awareness of sentence structure and help them construct meaningful and grammatically correct sentences. The results indicate that the activities were successful in improving students' writing skills, particularly in sentence construction. Moreover, the cognitive coaching approach which is applied to overcome the psychological barriers that hinder writing skills has yielded to positive results. Overall, the research suggests that these activities along with the approach used can be effective in improving students' writing skills and should be incorporated into language instruction.

Analyzing political party positions through multi-language twitter text embeddings.

Traditional monolingual word embedding models transform words into high-dimensional vectors which represent semantics relations between words as relationships between vectors in the high-dimensional space. They serve as productive tools to interpret multifarious aspects of the social world in social science research. Building on the previous research which interprets multifaceted meanings of words by projecting them onto word-level dimensions defined by differences between antonyms, we extend the architecture of establishing word-level cultural dimensions to the sentence level and adopt a Language-agnostic BERT model (LaBSE) to detect position similarities in a multi-language environment. We assess the efficacy of our sentence-level methodology using Twitter data from US politicians, comparing it to the traditional word-level embedding model. We also adopt Latent Dirichlet Allocation (LDA) to investigate detailed topics in these tweets and interpret politicians' positions from different angles. In addition, we adopt Twitter data from Spanish politicians and visualize their positions in a multi-language space to analyze position similarities across countries. The results show that our sentence-level methodology outperform traditional word-level model. We also demonstrate that our methodology is effective dealing with fine-sorted themes from the result that political positions towards different topics vary even within the same politicians. Through verification using American and Spanish political datasets, we find that the positioning of American and Spanish politicians on our defined liberal-conservative axis aligns with social common sense, political news, and previous research. Our architecture improves the standard word-level methodology and can be considered as a useful architecture for sentence-level applications in the future.

DDCO model based false news detection research

With the rapid development of the information age, while the popularity of social media brings great convenience, it also brings some negative effects, such as the spread of false news. At present, the identification of fake news is still based on the personal screening ability, therefore, the intelligent and information-based automatic detection algorithm has become one of the hot issues of current research. Based on the characteristics of DCAN and DEFEND models, this paper proposes an novel model DDCO, which uses multi-layer collaborative attention mechanism to extract the most relevant information from the three dimensions of sentence level, word level and sentence-comment level respectively. Finally, the model designed in this paper is tested on Weibo and Twitter data sets, and the results show that the DDCO has a higher accuracy than the existing models, which provides an important reference for false news detection.

مشاكل القواعد في ترجمة مجموعة قصة قصيرة "أرنى الله" لتوفيق الحكيم في موضوع الحب

The problem in this research is the grammatical problem found in the translation of the short story collection of Arinillah by Taufik Hakim. This study aims to determine (1) the number of grammatical problems, (2) the types of grammatical problems, and to describe (3) the relationship between the translated text and the theory of grammatical problems found in Arinillah's short stories with the theme of love. Based on the data sources studied, the researcher found grammatical problems in the level of phrases and sentences found in four short story titles with the theme of love. The results of this study are tarkib washfi, tarkib idhafi, atahf mathuf, taukid, simple sentences, complete sentences, and complex sentences in a collection of short stories titled Taufik Hakim Arinillah. The short stories contain Taufik Hakim's philosophical thoughts which are good for the development of today's mindset

초급 중국인 학습자의 ‘안’, ‘못’ 부정표현 습득 양상 연구

Negation is one of the most fundamental and crucial aspects in Korean language learning. However, Chinese learners often make many errors when learning Korean negation. Due to Chinese and Korean are two totally different languages, Korean is an agglutinative language while Chinese is an isolating language, Chinese learners face difficulties in learning Korean negation. Representative negations in Korean include ‘안’ and ‘못’, while in Chinese, they are represented by ‘不’ and ‘没’. However, the negations in both languages do not correspond completely. Although there are similarities, there are also many differences. Therefore, Chinese learners often make errors in using Korean negations by the influence of their native language.
 This study aims to examine the acquisition and error patterns of negations when Chinese beginners use the negations ‘안’ and ‘못’. Firstly, learners were given tasks at the sentence and discourse levels. Through the analysis of the results, the types of errors made by the learners were examined. According to the results, there are three types of errors: substitution errors between ‘안’ and ‘못’, errors in the use of ‘안’ as a single negation form, and errors related to past tense. The causes of the errors were analyzed, and suggestions for resolving the errors were provided. I hope that this study will contribute to future education on Korean negations.

ПОНЯТТЯ НАДМІРНОСТІ В МОВІ ТА В СКЛАДНОМУ БАГАТОКОМПОНЕНТНОМУ РЕЧЕННІ

The article examines lexical, grammatical and stylistic manifestations of excess in modern Ukrainian artistic discourse. A defi nition of the phenomenon of redundancy, in-cluding their lexical, grammatical and stylistic types, is proposed. The specifi cs of their application at the level of a complex multi-component sentence are outlined. Excessive el-ements are used to emphasize the statement and have a pragmatic eff ect on the addressee.The theoretical apparatus discussed in the article allows us to diff erentiate pleonasm as an excess of means that serves to convey the lexical or grammatical content of an utterance, manifests itself in repetition, synonymous duplication of lexemes or grammatical forms, as well as in too verbose conveying of content. The concept of tautology, which is tangential to the defi nition of pleonasm, is interpreted as a stylistic fi gure aimed at strengthening a phenomenon or sign. The basis of this technique is the repeated designation of what was said earlier in a diff erent form. Emphasis is placed on the fact that the use of repetitions in the text or partial duplication of content improves the style and gives the statement clarity and specifi cation. On the basis of the descriptive method used in the work, it was established that repeated words demonstrate the gradation of feelings, understandings and depth of experi-ences. In order to increase the impact on the perception of the text, to give it greater drama and expressiveness, modern writers widely use anaphora, or monologue. In addition, artistic texts are not devoid of such a stylistic fi gure as polysyndeton, or multiple conjunction, which is a means of demonstrating solemnity, expressing and enhancing lyricism and showing the author’s thoughts. At the time, the authors consciously used the analyzed technique, which, in our opinion, is fully justifi ed and motivated.The analysis of the given examples proved: despite the possibility of avoiding repetitions, the authors consciously use fi gures of excess, which are fully justifi ed and motivated. They are a means of artistic expressiveness of current events and the au-thor’s objective attitude to the world; contribute to revealing the nature of inner feel-ings; enrich the text with expression, as well as clarify what was said, intensify signs and actualize the actions presented in the text and at the syntactic level manifested in various types of complex multi-component sentences.

Error Analysis in EFL Students’ Writing Skill

"Error Analysis" looks at actual errors, explains them linguistically, and explains some of the possible causes of the errors to provide an understanding of the processes underlying the second language acquisition area. The purpose of this current study is to assess, describe and analyze errors committed by students. The researchers followed the error analysis methodology proposed by Corder (1973). It was to recognize, describe and explain errors in terms of their causes. After that, errors will be evaluated. For this purpose, a written test was performed on the study samples. The aim of the study is to analyze the linguistic errors in the writing skills of EFL students in an Egyptian high school, namely the Future high school. Two instruments were applied in this study: written tasks and a semi-structured interview. For error analysis, eighty written tasks by a group of 20 students were collected and analyzed. The results showed that the frequently committed errors were at the sentential level as well as the word level. The most frequent errors were the errors of spelling, and the least were the errors of demonstratives. The marked errors were categorized as errors of omission, addition, misinformation, and disordering. Further analysis of the collected data from the semi-structured interview indicated interlingual interference, intralingual interference, inadequate practice in English writing and carelessness of students as the major sources of the errors. Hence, the study has pedagogical implications as it will help teachers to devise appropriate measures for improving the students’ writing proficiency.

Dependency-based BERT for Chinese Event Argument Extraction

Existing event extraction methods independently identify and classify each argument role separately, ignoring the interdependence between different parameter roles. Further, these methods rely on simple vectors to represent word embeddings. By embedding explicit syntactic constraints in the attention mechanism, we address these shortcomings by using dependency syntax to guide the text modeling. Specifically, we use dependency syntax to guide the BERT model for Chinese event argument role extraction, which mainly consists of three stages. First, the self-attention method guided by a dependency syntactic parsing tree is embedded in the Transformer computing framework of the BERT model. In addition to obtaining a deep two-way linguistic representation of a word according to its context information, this method also expresses the long-distance syntactic dependency relationships between words based on context information. Secondly, the designed conditional layer normalization method is applied to the event argument extraction model in order to integrate the semantic information of trigger words into the text, thereby improving the accuracy of the argument role extraction. Lastly, conditional random fields (CRFs) are used to determine the optimal sequence of labels at the sentence level based on the dependence relationships between adjacent labels. According to the experimental results, the constructed model outperforms several strong baselines in the Chinese event argument extraction task on the ACE2005 dataset and the iFLYTEKA.I.2020 dataset.