Words In Sentence Research Articles

Analisis Strategi Komunikasi dan Lingkungan Kerja dalam Meningkatkan Kinerja Karyawan Cipta Grafika

This research aims to understand and analyze the results of communication strategy and work environment analysis in improving employee performance at Cipta Grafika Karawang. The location of this research is at Cipta Grafika Karawang. The research method employs a qualitative descriptive approach, expressed through careful word selection, sentence composition, detailed narration, and visual representation. Data collection techniques involve interviews, observations, and documentation. The research findings indicate that communication, comprising downward communication, upward communication, horizontal communication, and interline communication at Cipta Grafika, are well-established and efficient. The work environment is also reported to be good and comfortable. Effective communication and a comfortable working environment contribute to enhancing employee performance. It is essential to continuously maintain good communication and a comfortable work environment to sustain and further improve employee performance at Cipta Grafika.

Catalysts and Challenges in Essay Writing Proficiency among College Students: Insights from Motivation, Literacy, Cognition, and Language Skills

Abstract. This study delves into the various factors that influence the essay writing skills of college students, including motivation, literacy, cognitive abilities, and language proficiency. A mixed-method approach was used, combining qualitative and quantitative methods. The data was collected through questionnaire surveys and interviews to analyze the impact of these factors on students' writing capabilities. The findings reveal that the majority of students find writing enjoyable, with over 90% expressing interest in essay composition. However, procrastination tendencies were observed, with 46% of students admitting to submitting essays on the last day. Furthermore, excessive instructor feedback negatively affected the motivation of more than 50% of respondents. Regarding literacy, approximately 66% of students engage in daily reading, a critical component for enhancing writing skills. A strong correlation between reading and writing was identified, as students often utilize relevant sources to support their essays. However, 75% of participants admitted to reading primarily within the confines of coursework. Cognitive capabilities played a significant role, with over 80% of students effectively planning and developing essay content. However, content comprehension proved to be challenging for 56%, affecting the coherence of their essays. Interviews revealed difficulties in paragraph development and topic selection. Language skills were deemed essential for coherent writing, with 80% of students constructing sentences adeptly and employing grammar effectively. Despite these skills, students struggled with syntax and word choice. Interviews highlighted issues with sentence construction and word selection, impacting the clarity of their messages. The research findings can potentially be implemented to design strategies, models, and learning media that focus on improving the essay writing proficiency of college students.Keywords: Motivation, Literacy, Cognitive Abilities, Language Skills, Essay Writing

Supervised weight learning-based PSO framework for single document extractive summarization

The need for automatic text summarization is natural: there is a huge volume of information available online, which prompts for a widespread interest in extracting relevant information in a concise and understandable manner. Here, automated text summarization has been treated as an extractive single-document summarization problem in the proposed system. To solve this problem, a particle swarm optimisation (PSO) algorithm-based approach is suggested, with the goal of producing good summaries in terms of content coverage, informativeness, and readability.This paper introduces XSumm-PSO: a new approach based on PSO optimization technique in a supervised manner for extractive summarization. Further, this paper also contributes a new feature “incorrect word” that captures misspelled words in the candidate sentences. This feature is combined with nine existing features used by proposed model to generate error free summaries. As a result, the proposed XSumm-PSO framework produces superior performance achieving improvements of +2.7%, +0.8%, and +0.8% for ROUGE-1, ROUGE-2, and ROUGE-L scores, respectively, on DUC 2002 dataset, over state-of-the-art techniques. The corresponding improvements on the CNN/DailyMail dataset are +0.97%, +0.25%, and +0.49%.We also performed sample t-test, showing the proposed approach is statistically consistent across various runs.

Identifikasi Kerusakan Pada Landing Gear Pesawat Cessna C208B

One type of aircraft commonly used for flight training, patrol, and transportation in remote areas is the Cessna. It should be understood that each tool and component in the aircraft has its own level of importance and can experience failures in carrying out its functions. These failures often become a problem for users due to a limited understanding of the aircraft engine field. Issues arising from the aircraft's landing gear sometimes involve minor problems that do Does not require a high level of expertise. To solve this, it may be possible for someone with knowledge of landing gear to address the issue. By using the Case-Based Reasoning (CBR) method for fault identification, a technician's expertise in aircraft landing gear can be applied and integrated. The search for solutions or fault identification Can be obtained promptly In the testing results of the Fault System for Cessna C208B Aircraft Landing Gear using the CBR method, the system's calculations were consistent with the manual. However, the application still has some shortcomings in the system, such as the preprocessing stage, which has not been able to search for the root words in a question sentence.

A need analysis of the high-frequency words (HFW) dictionary for the Indonesian language for foreigners (ILF)-1 learning

This is a preliminary study of research development for creating a dictionary of high-frequency words (HFW). Thus, this study tries to analyze the need of the ILF learners as the users of a dictionary of HFW for their ILF learning. A list of high-frequency words should be used in ILF learning so that the learning objectives can be achieved effectively. Unfortunately, the word list is not yet available. The ILF learners, especially those at the beginner level, still have difficulty using the Great Dictionary of the Indonesian Language (KBBI). They prefer using translation apps to obtain the meaning of a word, even though these apps do not always help. Employing a descriptive survey, this study involved a total of 183 ILF teachers and learners. The data were gathered through Google-form questionnaires. In addition, interviews were administered with selected ILF teachers who had a teaching experience of more than 15 years. The findings revealed that the teachers and learners had different beliefs on the vocabulary forms needed for making a dictionary of HFW. The learners still did not understand the concepts of high-frequency words. According to the teachers, HFW was more needed for ILF learning. Meanwhile, for the learners, general words were more needed. Besides, digital dictionaries were more in demand than printed dictionaries. Bilingual dictionaries were more needed than monolingual dictionaries. Both the teachers and learners believed that the dictionary of HFW should comprise sentence examples. They both agreed that the dictionary of HFW should provide information about the use of words in sentence examples, affixes, synonymous phrases, antonyms, word classes, and phonetics. Therefore, the results of this research analysis can be used as a basis for developing a dictionary that fits the needs of ILF-1 learners.

A Literature Review of Language Characteristics in Patients with Vascular Dementia

Vascular dementia may present with language disorders due to damage to frontal subcortical circuits. This study aimed to review recent studies of language abilities in patients with vascular dementia. Sixty-nine studies were classified according to language modalities (spontaneous speech, auditory comprehension, repetition and naming). First, spontaneous speech was impaired in the ability to describe pictures, objects and in the ability to convey information in discourse. Second, the auditory comprehension was impaired in word comprehension, sentence comprehension, paragraph comprehension, and speech act comprehension tasks. Third, repetition was preserved in word-level tasks but impaired at the sentence level. Fourth, the naming performance was impaired in a generative naming task (phonemic fluency) rather than in a confrontational naming task. Therefore, when summarizing the results between patient groups, in spontaneous speech, to convey information through highly informative utterances was relatively good in vascular dementia (VaD), but the number of utterances was lower than dementia of Alzheimer’s type (DAT). In auditory comprehension, VaD’s performance was good in the task of word comprehension and speech act comprehension tasks. It is organized as in repetition, VaD’s performance was relatively good in the sentence-level task, and in naming, VaD’s performance was reported to be better than DAT in the oppositional naming and generative naming semantic category tasks. In the generative naming phoneme category, opposite result was reported. Further studies on the language abilities of patients with vascular dementia needs to consider factors such as subtype and language modality. Future studies should be evenly distributed across all language domains.

Optimization and Application of English Word Memory Algorithm Based on Reinforcement Learning

The optimization and application of an English word memory algorithm based on reinforcement learning involve refining a system that utilizes reinforcement learning techniques to enhance vocabulary retention. By leveraging reinforcement learning, the algorithm can adapt its strategies for presenting and reviewing English words based on users' responses and performance. This adaptive approach enables personalized and effective learning experiences, wherein the algorithm adjusts the frequency and timing of word repetitions to maximize memorization efficiency. Applications of this optimized algorithm span language learning platforms, educational apps, and personalized tutoring systems, offering learners tailored support to strengthen their English vocabulary acquisition and retention skills. This paper introduces the Spider Swarm Optimized English Word Memory Algorithm (SSOEWMA) algorithm, a novel approach aimed at optimizing language processing and learning tasks. Leveraging principles of reinforcement learning and swarm intelligence, SSOEWMA demonstrates its effectiveness in enhancing various aspects of language processing, including word difficulty assessment, sentence estimation, memory retention, and student performance evaluation. Through a series of experiments and evaluations, we showcase SSOEWMA ability to optimize dataset attributes, accurately estimate sentence characteristics, and improve memory retention performance. Simulation results achieved an average word recall accuracy of 85% across multiple evaluations. Through its utilization of reinforcement learning principles and swarm intelligence, SSOEWMA significantly improved memory retention performance, reducing recall latency by an average of 20% compared to baseline measures. Additionally, the algorithm demonstrated strong learning efficiency, converging to optimal solutions with a speed increase of 30% compared to traditional methods.

Aspect-based sentiment analysis with multi-granularity information mining and sentiment hint

Most aspect-based sentiment analysis approaches at this stage primarily focus on extracting information from the comment sentence and analyzing the interaction between the comment sentence and aspect words while ignoring the importance of key phrase information. Sentences are composed of words and phrases following specific grammatical rules, and mining phrase information can provide valuable insights into short-distance collocations between words. Thus, integrating multi-granularities (sentences, phrases, and aspect words) in the learning process can lead to better vector representations. This paper proposes a multi-granularity information mining and sentiment hint model (MGSH). Specifically, we first use two phrase extraction techniques to separately extract a set of key phrases from comment sentences and use heuristic algorithms to merge and eliminate the redundancy of the two sets. In addition, we design a constrained multi-granularity fusion module to dynamically integrate information from different granularities, encouraging the model to capture semantic associations among these granularities. Finally, we conducted a statistical analysis on the number of aspect words in the comment sentences. We found a potential correlation between the overall emotion of the sentence and the aspect words. Therefore, we pre-assign sentiment pseudo-labels to the commented sentences to identify sentiment cues specific to aspects and potential sentiment relationships between sentences and aspects. Our experimental evaluations, conducted on three benchmark datasets, showcase the importance of multi-granularity information mining and interaction as well as the effectiveness of utilizing sentiment cue information.

Language Distance Moderates the Effect of a Mixed-Language Environment on New-Word Learning for 4-Year-Old Children.

As bilingual families increase, the phenomenon of language mixing among children in mixed-language environments has gradually attracted academic attention. This study aims to explore the impact of language mixing on vocabulary acquisition in bilingual children and whether language distance moderates this impact. We recruited two groups of bilingual children, Chinese-English bilinguals and Chinese-Japanese bilinguals, to learn two first-language new words in a monolingual environment and a mixed-language environment, respectively. The results showed that the participants could successfully recognize the novel words in the code-switching sentences. However, when we compared the performance of the two groups of bilingual children, we found that the gaze time proportion of the Chinese-English bilingual children under the code-switching condition was significantly higher than that of the Chinese-Japanese bilingual children, while there was no significant difference under the monolingual condition. This suggests that language mixing has an inhibitory effect on vocabulary acquisition in bilingual children and that this inhibitory effect is influenced by language distance, that is, the greater the language distance, the stronger the inhibitory effect. This study reveals the negative impact of language mixing on vocabulary acquisition in bilingual children and also implies that there may be some other influencing factors, so more research is needed on different types of bilingual children.

Interpretation bias for ambiguous scenarios among individuals with high and low levels of empathy

AbstractEmpathy is the ability to understand and resonate with the emotions of others, typically requiring individuals to infer others' emotional status through the information around them. However, this information is often ambiguous and it is unknown whether individuals with high empathy have a bias in interpreting contextual information. Therefore, this study investigated how individuals with low and high empathy interpret other‐relevant scenarios in Study 1 (N = 98) and self‐relevant scenarios in Study 2 (N = 95), by using the scenarios task and the Sentence Word Association Paradigm (SWAP) separately. Study 1 observed that, for interpretations of other‐relevant scenarios, the high‐empathy group showed greater likelihood ratings for negative interpretations than those with low‐empathy in both social and non‐social scenarios. Study 2 found that, for interpretations of self‐relevant scenarios, the high‐empathy group differed from the low‐empathy group only on non‐social scenarios but not on social scenarios. Specifically, individuals with high empathy were more likely to report a relationship between a negative word and an ambiguous scenario compared to those with low empathy in self‐relevant non‐social scenarios. The study first revealed a bias for highly empathetic individuals to regard the ambiguous scenarios as negative in other‐relevant and self‐relevant scenarios, except for self‐relevant social scenarios.

Aspect-based sentiment analysis with metaphorical information

Aspect-Based Sentiment Analysis (ABSA) has been the focus of increasing study in recent years. Previous research has demonstrated that incorporating syntactic information, such as dependency trees, can enhance ABSA performance. Despite the widespread use of metaphors in daily life to express emotions more vividly, few studies have integrated this literary device into ABSA. In this paper, we propose a novel ABSA model that utilizes Metaphor Identification Procedure (MIP) to encode both the sentence and aspect word as a single unit, thereby overcoming these limitations. Our experimental results demonstrate that our model achieves competitive performance in ABSA.

Capturing word positions does help: A multi-element hypergraph gated attention network for document classification

Over the last few years, graph-based methods have manifested a significant enhancement in document mining applications such as spam detection, news recommendation, and legal document classification. However, existing graph-based methods have a limited ability to utilize word position and multi-element information within the documents, limiting their effectiveness in practical application. To mitigate this limitation, we propose a novel multi-element hypergraph gated attention network that can capture word position and multi-element information for accurate document classification. Specifically, a new multi-element hypergraph is first proposed to describe the word position, sentence, and full content within the document. Then, a new multi-element homogenization module is applied to mitigate heterogeneity of constructed hypergraph. Meantime, a new hypergraph gated attention module is proposed to filter noise in the constructed hypergraph and derive various element representations that incorporate word position information. Finally, a new block-wise read-out module is designed to fuse learned element representations into comprehensive document representations for classification. Extensive experiments conducted on several real-world datasets demonstrate that the proposed method not only outperforms related state-of-the-art methods but is also faster, making it suitable for a wide range of practical applications. For instance, our method achieved an accuracy improvement of 1.1 % over the best comparative method on some datasets while also operating at a faster speed. Additionally, it demonstrated an impressive 14 % improvement in accuracy over the well-known Generative Pre-trained Transformer 3.5 (GPT-3.5) on one dataset.

DETERMİNANTLAR VƏ CÜMLƏ ÜZVLƏRİNİN QARŞILIQLI ƏLAQƏSİNƏ DAİR

On the Relationship of Determinants and Parts of Sentences Summary In order for the language to carry out any kind of communication, all the links of the syntax that make up the structure of the language must be in place. The syntactic system of the language is made up of word combinations, sentences and text. Syntactic units in the language are connected with each other by meaning and grammatical relations and form the structural regularity of syntactic units, its regulator. As in every world language, the syntactic structure of the modern Oghuz Turkic languages consists of an ordered system of syntactic units. In this ordered system, the determinants have their own place and position along with word combinations, sentence parts, sentences and text. Key words: determiner, sentence, syntax, adverb, verb

Multifractal analysis of Chinese literary and web novels

Multifractal analysis of literary works, utilizing measures such as sentence length and word length as time series, have revealed the prevalence of multifractal structures within these works. After constructing a corpus of 712 Chinese novels, we performed multifractal detrended fluctuation analysis (MF-DFA) and multifractal detrending moving average analysis (MF-DMA) on them. Both method revealed that non-monotonic generalized Hurst exponential curves and distorted spectral functions appeared in about half of the web novels, but were rarely found in literary novels. By conducting a meticulous examination of sentence length sequences and adjustment of the time series range covered by the computation, it becomes apparent that this irregular Hurst exponent curve stems from the uneven distribution of long sentences within the work. This uneven distribution mirrors the instability present in the author’s creative style, which is intimately linked to the characteristics of the publishing mechanisms for web novels.

Data Representation for Deep Learning - Based Arabic Text Summarization Performance Using Python Results

A sequence-to-sequence model is used as the foundation for a suggested abstractive Arabic text summarizing system. Our goal is to create a sequence-to-sequence model by utilizing multiple deep artificial neural networks and determining which one performs the best. The encoder and decoder have been developed using several layers of recurrent neural networks, gated recurrent units, recursive neural networks, convolutional neural networks, long short-term memory, and bidirectional long short-term memory. We are re-implementing the fundamental summarization model in this study, which uses the sequence-to-sequence framework. Using a Google Colab Jupiter notebook that runs smoothly, we have constructed these models using the Keras library. The results further demonstrate that one of the key techniques that has led to breakthrough performance with deep neural networks is the use of Gensim for word embeddings over other text representations by abstractive summarization models, along with FastText, a library for efficient learning of word representations and sentence classification.

Exploring Spanish writing abilities of children with developmental language disorder in expository texts.

Numerous studies have shown that children with developmental language disorder (DLD), in addition to oral language difficulties, exhibit impaired writing abilities. Their texts contain problems in grammar, organization, cohesion, and length of written output. However, most of these studies have been conducted with English speakers. English is characterized by complex phonological structure, opaque orthography, poor morphology and strict word order. The aim of this research is to observe the writing abilities of children with DLD in a language with simple phonological structure, transparent orthography, rich morphology and flexible word order like Spanish in the production of expository texts. Twenty-six children with DLD (mean age in months = 128.85) and 26 age-and sex-matched typically developing (TD) children (mean age in months = 124.61) wrote an expository text about their favorite animal. In order to analyze how the two groups plan and encode written texts, we looked at word frequency and sentence structure, grammatical complexity and lexical density, and omissions and errors. Compared to the TD group, the children with DLD omitted more content words; made more errors with functional words, verb conjugation and inflectional morphemes, and made a large number of spelling errors. Moreover, they wrote fewer words, fewer sentences, and less structurally and lexically complex texts. These results show that children with DLD who speak a transparent orthography language such as Spanish also have difficulties in most language areas when producing written texts. Our findings should be considered when planning and designing interventions.

Completing and Balancing Database Excerpted Chemical Reactions with a Hybrid Mechanistic-Machine Learning Approach.

Computer-aided synthesis planning (CASP) development of reaction routes requires an understanding of complete reaction structures. However, most reactions in the current databases are missing reaction coparticipants. Although reaction prediction and atom mapping tools can predict major reaction participants and trace atom rearrangements in reactions, they fail to identify the missing molecules to complete reactions. This is because these approaches are data-driven models trained on the current reaction databases, which comprise incomplete reactions. In this work, a workflow was developed to tackle the reaction completion challenge. This includes a heuristic-based method to identify balanced reactions from reaction databases and complete some imbalanced reactions by adding candidate molecules. A machine learning masked language model (MLM) was trained to learn from simplified molecular input line entry system (SMILES) sentences of these completed reactions. The model predicted missing molecules for the incomplete reactions, a workflow analogous to predicting missing words in sentences. The model is promising for the prediction of small- and middle-sized missing molecules in incomplete reaction records. The workflow combining both the heuristic and machine learning methods completed more than half of the entire reaction space.

The Effectiveness of Using an H5P Pathway to Enhance College Students' Academic Style in Writing

This study investigates the effectiveness of using an H5P pathway to enhance college students' academic style in writing. The study was conducted at XJTLU, a Sino-foreign university, where the language barriers of first-year students posed difficulties in their transition from informal language to the formal tone required in academic writing. Through a careful experimental design, participants were assigned to either an experimental or a control group, with the former engaging in a structured H5P pathway including five specific academic writing style rules based on learner needs analysis. The five rules are in aspects of word choice, sentence structure, transition, hedging and redundancy. The latter received lesson PPTs with the same content. This paper discusses the results of the study, which show that the application of the H5P pathway effectively assisted Year 1 students at XJTLU in narrowing the gap between the two writing styles. Preliminary analysis indicates a significant improvement in the experimental group's academic writing style specifically in aspects of using formal words and complex sentence structure. By comparison, other than word choice, there is no significant improvement in the control group. The findings of this study highlight the potential of H5P pathways as an effective tool for enhancing college students' academic writing style. Through interactive and tailored activities, students’ challenges at academic writing conventions could be addressed. This research provides implications for innovative pedagogical strategies that can promote students' academic development.

Canadian Practice and Recommendations on Functional MRI to Lateralize Language in Epilepsy.

Identifying a patient's dominant language hemisphere is an important evaluation performed prior to epilepsy surgery and is commonly assessed using functional magnetic resonance imaging (fMRI). However, the lack of standardization and resultant heterogeneity of fMRI paradigms used in clinical practice limits the ability of cross-center comparisons to be made regarding language laterality results. Through surveying Canadian Epilepsy Centres in combination with reviewing supporting literature, current fMRI language lateralization practices for the clinical evaluation of patients with epilepsy were assessed. To encourage standardization of this practice, we outlined a two-part paradigm series that demonstrates widespread acceptance, reliability and accessibility in lateralizing various aspects of language functioning in individuals with average or near-average IQ and normal literacy skills. The collected data confirm a lack of standardization in fMRI laterality assessments leading to clinical heterogeneity in stimulation and control tasks, paradigm design and timing, laterality index calculations, thresholding values and analysis software and technique. We suggest a Sentence Completion (SC) and Word Generation (WG) paradigm series as it was most commonly employed across Canada, demonstrated reliability in lateralizing both receptive and expressive language areas in supporting literature, and could be readily intelligible to an inclusive population. Through providing recommendations for a two-part paradigm series, we hope to contribute to the standardization of this practice across Canada to reduce clinical heterogeneity, encourage communicability between institutions, and enhance methodologies for the surgical treatment of epilepsy for the benefit of all individuals living with epilepsy in Canada.

Testing the Continuum/Spectrum Model in Russian-Speaking Children With and Without Developmental Language Disorder.

Previously, Lancaster and Camarata (2019) showed that the continuum/spectrum model of the developmental language disorder (DLD) best explained the high heterogeneity of symptoms in children with DLD. We hypothesize that the continuum/spectrum approach can include not only children with DLD but also typically developing (TD) children with different timelines and patterns of language acquisition. This model can explain individual language profiles and deficits in children. We assessed language abilities in a group of Russian-speaking children with DLD aged 4-7 years (n = 53) and their age- and gender-matched peers without speech and language diagnoses (n = 53, TD). We evaluated the children's performance at four language levels in production and comprehension domains, using 11 subtests of the standardized language assessment for Russian: Russian Child Language Assessment Battery (RuCLAB). Using the k-means cluster method and RuCLAB scores, we obtained two clusters of children and analyzed their language performance in individual subtests. The analysis revealed that the two clusters of children both included DLD and TD participants: Group 1, with higher test scores (TD = 45, DLD = 24 children), and Group 2, with lower scores (TD = 8, DLD = 29). Children from Group 1 mostly had lower scores at one of the language levels, whereas those from Group 2 struggled at several language levels. Furthermore, children with DLD from both groups tended to be more sensitive to linguistic features such as word length, noun case, and sentence reversibility compared to TD children. The presence of two mixed groups shows that children with diagnosed DLD could perform on par with TD children, whereas some younger TD children could perform similarly to children with DLD. Our findings support the continuum/spectrum model: Linguistic skills in preschool children are a continuum, varying from high to poor skills at all language levels in comprehension and production. To describe a child's language profile, the tasks assessing all language levels should be used. https://doi.org/10.23641/asha.25521400.