Lexicon Features Research Articles

Use of the Thunkable Platform in Preparing a Digital Arabic-Indonesian Dictionary as a Learning Resource for Arabic Language Learners

This research aims to answer the anxiety that current Arabic language learners must immediately respond to the increasingly massive development of digital technology by presenting a thunkable platform in creating a digital Arabic-Indonesian dictionary application as an independent learning resource for Arabic language learners. With the millennial generation preferring digital over printed resources, this research explores using the Thunkable platform to create a digital Arabic-Indonesian dictionary as a self-learning tool. Using a waterfall and qualitative descriptive approach, the study outlines the creation and use of the app. The data in this research are data on developing a digital Arabic-Indonesian dictionary via a thunkable platform, application user data, data on using digital dictionary features, and evaluation data on Arabic language learning using a digital dictionary. The data collection technique this research uses involves qualitative methods, providing a comprehensive overview of how the Thunkable platform can develop digital dictionaries for Arabic language learning. Meanwhile, the data sources in this research were obtained from observations, interviews, questionnaires, learning tests, application log analysis, and documentation studies, which will produce in-depth data regarding the effectiveness and efficiency of the application as a learning resource for Arabic language learners. Then, the data obtained was analyzed in several stages: data collection, data reduction, data presentation, and drawing conclusions. Thanks to its translator feature and block-based programming, thunkable simplifies the process by eliminating the need for time-consuming vocabulary inventories and manual coding. The digital dictionary also works offline, offering flexible access for learners anytime, anywhere. This study demonstrates how technology can support independent learning and enhance Arabic language skills.

Analisis Penggunaan Kamus Online Al-Ma’any untuk Pengembangan Kosakata Mahasiswa Pendidikan Bahasa Arab di Universitas Islam Negeri Sumatera Utara Medan

This study aims to analyze the use of the Al-Ma’any Online Dictionary in supporting the development of Arabic vocabulary mastery among students in PBA 2 class at UIN Sumatera Utara. The primary issue observed among students is the lack of optimal utilization of the online dictionary due to certain challenges in its usage. The focus of this research is to explore how students utilize the online dictionary to enrich their vocabulary and its impact on their Arabic language proficiency. This study employs a qualitative approach with a descriptive design. Data were collected through interviews with students, observations of dictionary usage, and analysis of student assignments. The findings indicate that the Al-Ma’any Online Dictionary is effective in helping students expand their vocabulary, particularly in discovering word meanings and their usage in various contexts. However, some challenges were identified, such as limited understanding of subtle word meanings and dependency on internet connectivity. This study suggests that vocabulary development through the use of online dictionaries can be optimized by providing guidance and further training for students in utilizing the dictionary's features more comprehensively.

Sequential lexicon enhanced bidirectional encoder representations from transformers: Chinese named entity recognition using sequential lexicon enhanced BERT.

Lexicon Enhanced Bidirectional Encoder Representations from Transformers (LEBERT) has achieved great success in Chinese Named Entity Recognition (NER). LEBERT performs lexical enhancement with a Lexicon Adapter layer, which facilitates deep lexicon knowledge fusion at the lower layers of BERT. However, this method is likely to introduce noise words and does not consider the possible conflicts between words when fusing lexicon information. To address this issue, we advocate for a novel lexical enhancement method, Sequential Lexicon Enhanced BERT (SLEBERT) for the Chinese NER, which builds sequential lexicon to reduce noise words and resolve the problem of lexical conflict. Compared with LEBERT, it leverages the position encoding of sequential lexicon and adaptive attention mechanism of sequential lexicon to enhance the lexicon feature. Experiments on the four available datasets identified that SLEBERT outperforms other lexical enhancement models in performance and efficiency.

DISEMINASI KAMUS DIGITAL KIRATU SEBAGAI MEDIA PEMBELAJARAN INOVATIF PENDUKUNG PENDIDIKAN INKLUSI DI SLB NEGERI JOMBANG

This community service activity aims to improve the skills of special school teachers in using digital dictionaries as teaching aids. This training focused on the introduction and application of digital dictionary technology specifically designed to support inclusive education, especially for deaf students. In this activity, SLB teachers were given training on how to utilize digital dictionary features such as interactive definitions, images, videos and sign language to enrich learning materials. In addition, the training includes a practical session on making digital dictionaries that suit the needs of the curriculum and students. The benefits of this training include improving teachers' ability to present more appropriate and adaptive teaching materials, as well as supporting students' independence in the learning process. This activity is expected to improve the quality of education and create a more inclusive and effective learning environment for students with special needs.

Improvement of a Machine Learning Model Using a Sentiment Analysis Algorithm to Detect Fake News

These days, the problem of fake news has grown to be a major social and personal concern. With the amount of information generated through social media, it is very crucial to be able to detect and properly take care of that fake information. Previous studies proposed a machine learning model to detect fake news in online Thai health and medical articles. Still, the problem of detecting fake news with similar content but different objectives exists, and the accuracy of the model needs improvement. Therefore, this study aims to solve these problems by adding 33 new features, including textual features, sentiment-based features, and lexicon features, i.e., herbs, fruits, and vegetables, to identify the objective of an article. We trained and tested the model's prediction accuracy on a new dataset containing 582 reliable and 435 unreliable (fake news) articles from eight Thai websites. Our improved classification model using XGBoost with Lasso, the best feature selection method, achieved an accuracy of 97.76% without over-fitting, reflecting a 7.16% improvement over our earlier model.

Breast imaging reporting and data system for sonography: Positive and negative predictive values of sonographic features in Kumasi, Ghana

BackgroundBreast cancer is the most common female cancer globally. The method of choice for screening and diagnosing breast cancer is mammography, which is not widely available in Ghana as compared to ultrasonography. This study aimed to evaluate the sonographic features of solid breast lesions using the new sonographic Breast Imaging- Reporting and Data System (BI-RADS-US) lexicon for malignancy with histopathology as the gold standard. MethodsThis was a prospective quantitative study that sonographically scanned female patients with breast masses and consecutively selected cases recommended for core biopsy from May 2018 to May 2021. Sixty (60) solid breast masses were described using the sonographic BI-RADS lexicon features. Lesion description and biopsy results from histopathology were compared and analyzed using Pearson's Chi-square test. Odds ratios, sensitivity, specificity, and predictive values were also calculated. Statistical significance level was set at p ≤ 0.05. ResultsIrregular shape (p < 0.0001), spiculated mass margins (p < 0.0001), and not parallel mass orientation (p= 0.0007) were more commonly associated with malignant masses. The sensitivity of breast ultrasound for malignancy was 93.9 % and the specificity was 55.6 % with an overall accuracy rate of 76.6 %. The negative predictive value was 88.7 % and the positive predictive value was 72.1 %. Descriptors like irregular shape, non-parallel orientation, angular and spiculated margins, echogenic halo, and markedly hypoechoic internal content, demonstrated higher odds ratios for malignancy. ConclusionsThis study adds valuable insights to the diagnosis of breast cancer using the sonographic BI-RADS lexicon features. The results demonstrate that specific sonographic descriptors can effectively differentiate between benign and malignant breast masses.

Detecting multiple coexisting emotions from public emergency opinions

To detect multiple coexisting emotions from public emergency opinions, this article proposes a novel two-stage multiple coexisting emotion-detection model. First, the text semantic feature extracted through bidirectional encoder representation from transformers (BERT) and the emotion lexicon feature extracted through the emotion dictionary are fused. Then, the emotion subjectivity judgement and multiple coexisting emotion detection are performed in two separate stages. In the first stage, we introduce synthetic minority oversampling technique (SMOTE) to enhance the balance of data distribution and select the optimal classifier to recognise opinion texts with emotion. In the second stage, the label powerset (LP)-SMOTE is proposed to increase the number of the minority category samples, and multichannel emotion classifiers and the decision mechanism are employed to recognise different types of emotions and determine the final coexisting emotion labels. Finally, the Weibo data about coronavirus disease 2019 (COVID-19) are collected to verify the effectiveness of the proposed model. Experiment results indicate that the proposed model outperforms state-of-the-art models, with the F1_macro of 0.8532, the F1_micro of 0.8333, and the hamming loss of 0.0476. The emotion detection results are conducive to decision-making for public emergency departments.

Application and Challenges of Information Theory in English Translation and Cross-cultural Communication

Abstract With the deepening of China's foreign exchange, the importance of translation is becoming more and more prominent, but some scholars and translators still have great misunderstandings about translation, and translation has been in a neglected position. Aiming at this phenomenon, this paper introduces information theory into English translation and cross-cultural communication. It proposes an automatic translation control method based on the information entropy model to improve the accuracy and efficiency of translation. It expands the lexicon based on the information entropy model, which is divided into basic lexicon, specialized lexicon, and neologism lexicon. Then, it constructs a semantic model to complete the automated extraction of lexicon features and realize automatic translation. The transfer of language information and the modification of terminological structure are discussed in cross-cultural communication to improve the challenges of English translation and cross-cultural communication. During the translation process, the correlation between the entropy of Chinese and English corresponding unitary, binary, and ternary words - sentence entropy decreases gradually (0.993-&gt;0.977-&gt;0.972). The largest value of information entropy loss in different cultural contexts is caused by the tonal-phonetic contrast of combining yinping and declination. The relative degree of information loss is 5.300585%. This study has provided new perspectives for translation research through the use of redundant information theory and discrete information theory.

Detecting offensive language using Chaotic Ant Lion optimization-based Ghost net in social media

Social media has become one of the most popular medium of communication and the post may be predominantly unstructured, informal, and frequently misspelled. It has become increasingly common for users to use abusive language in their comments. Detecting offensive language on social media platforms and the presence of such language on the Internet has become a major challenge for modern society. To overcome this challenge, Offensive Language Classification based on the Chaotic Antlion optimization algorithm has been proposed. Initially, the dataset is pre-processed using NLP languages for removing irrelevant data. Consequently, statistical, synthetic, and lexicon features are extracted using various feature extraction techniques. A Chaotic Antlion Optimization Algorithm is used to select the most relevant features during the feature selection phase. After selecting the features, a Ghost network classifies the input data into four classes namely offensive, non-offensive, swear, and offensive but not offensive. The proposed method was evaluated based on a number of variables, including precision, accuracy, specificity, recall, and F-measure. The best classification accuracy is achieved by the suggested method, which is 99.27% for the SOLID dataset and 98.99% for the OLID dataset. The suggested method outperforms the DCNN, Simple Logistics, and CNN methods in terms of overall accuracy by 4.99%, 8.72%, and 10.4%, respectively.

Natural Language Processing to Classify Caregiver Strategies Supporting Participation Among Children and Youth with Craniofacial Microsomia and Other Childhood-Onset Disabilities

Customizing participation-focused pediatric rehabilitation interventions is an important but also complex and potentially resource intensive process, which may benefit from automated and simplified steps. This research aimed at applying natural language processing to develop and identify a best performing predictive model that classifies caregiver strategies into participation-related constructs, while filtering out non-strategies. We created a dataset including 1,576 caregiver strategies obtained from 236 families of children and youth (11–17 years) with craniofacial microsomia or other childhood-onset disabilities. These strategies were annotated to four participation-related constructs and a non-strategy class. We experimented with manually created features (i.e., speech and dependency tags, predefined likely sets of words, dense lexicon features (i.e., Unified Medical Language System (UMLS) concepts)) and three classical methods (i.e., logistic regression, naïve Bayes, support vector machines (SVM)). We tested a series of binary and multinomial classification tasks applying 10-fold cross-validation on the training set (80%) to test the best performing model on the held-out test set (20%). SVM using term frequency-inverse document frequency (TF-IDF) was the best performing model for all four classification tasks, with accuracy ranging from 78.10 to 94.92% and a macro-averaged F1-score ranging from 0.58 to 0.83. Manually created features only increased model performance when filtering out non-strategies. Results suggest pipelined classification tasks (i.e., filtering out non-strategies; classification into intrinsic and extrinsic strategies; classification into participation-related constructs) for implementation into participation-focused pediatric rehabilitation interventions like Participation and Environment Measure Plus (PEM+) among caregivers who complete the Participation and Environment Measure for Children and Youth (PEM-CY).

Leveraging Domain Knowledge to Improve Depression Detection on Chinese Social Media

Depression is a prevalent and severe mental disorder that often goes undetected and untreated, particularly in its early stages. However, social media has emerged as a valuable resource for identifying symptoms of depression and other mental disorders as people are increasingly willing to share their experiences and emotions online. As such, social media-based depression detection has become an important area of research. Unfortunately, despite the growing number of cases in China, there are few Chinese social media-based resources for depression research. To address this gap, this article presents a dataset collected from Sina Weibo and approaches depression detection as a binary classification problem. A depression lexicon is developed based on domain knowledge of depression and the Dalian University of Technology Sentiment Lexicon (DUT-SL), which facilitates better extraction of lexical features related to depression. Then the lexical features are fused using a correlation-based metric. The effectiveness of this approach is verified using five classical machine learning methods and two boosting-based models, both on a public dataset and our dataset. Experimental results indicate that the depression domain lexicon features improve classification performance and fusing these features based on their correlations can further enhance prediction effectiveness. This study provides a method for future research in social media-based depression detection and contributes to the development of Chinese depression detection resources.

Принципы лексикографического описания терминологии словообразовательного синтеза (на материале глаголов звучания русского языка)

The purpose of this study is to develop the description principles for lexicographic terminology in word-formation (WF) synthesis of Russian verbs. The scientific novelty lies in the fact that for the first time the terminological system for WF synthesis of verbs is presented as the object of lexicographic description and enhanced with inter-active web-based elements. As a result, it has been proven that this terminological dictionary can serve not only as an informational and methodological instrument, but it also can become an effective teaching tool for studying Russian verb synthesis. The main macro-structure of the designed dictionary features the departure from the traditional alphabetic organization of the material, utilizing instead the principle of zoning by synthesis levels (the root and the word formation levels). Each lexicographic entry is augmented with interactive links to digital Russian explanatory dictionaries and to the Russian National Corpus (RNC) database.

Supervised Machine Learning Method for Ontology-based Financial Decisions in the Stock Market

For changing semantics, ontological and information presentation, as well as computational linguistics for Asian social networks, are one of the most essential platforms for offering enhanced and real-time data mapping, as well as huge data access across diverse big data sources on the web architecture, information extraction mining, statistical modeling and data modeling, database control, and so on. The concept of opinion or sentiment analysis is often used to predict or classify the textual data, sentiment, affect, subjectivity, and other emotional states in online text. Recognizing the message's positive and negative thoughts or opinions by examining the author's goals will aid in a better understanding of the text's content in terms of the stock market. An intelligent ontology and knowledge Asian social network solution can improve the effectiveness of a company's decision making support procedures by deriving important information about users from a wide variety of web sources. However, ontology is concerned primarily with problem-solving knowledge discovery. The utilization of Internet-based modernizations welcomed a significant effect on the Indian stock exchange. News related to the stock market in the most recent decade plays a vital role for the brokers or users. This article focuses on predicting stock market news sentiments based on their polarity and textual information using the concept of ontological knowledge-based Convolution Neural Network (CNN) as a machine learning approach. Optimal features are essential for the sentiment classification model to predict the stock's textual reviews' exact sentiment. Therefore, the swarm-based Artificial Bee Colony (ABC) algorithm is utilized with the Lexicon feature extraction approach using a novel fitness function. The main motivation for combining ABC and CNN is to accelerate model training, which is why the suggested approach is effective in predicting emotions from stock news.

A Hybrid Method on Emotion Detection for Indonesian Tweets of COVID-19

As a result of the COVID-19 pandemic, there have been restrictions on activities outside the home which has caused people to interact more and express their emotions through social media platforms, one of which is Twitter. Previous studies on emotion classification used only one feature extraction, namely the lexicon based or word embedding. Feature extraction using the emotion lexicon has the advantage of recognizing emotional words in a sentence while feature extraction using word embedding has the advantage of recognizing the semantic meaning. Therefore, the main contribution to this research is to use two lexicon feature extraction and word embedding to classify emotions. The classification technique used in this research is the Ensemble Voting Classifier by selecting the two best classifiers to try on both types of feature extraction. The experimental results for both types of feature extraction are the same, indicating that the best classifiers are Random Forest and SVM. Models using both types of feature extraction show increased accuracy compared to using only one feature extraction. The results of this emotional analysis can be used to determine the public's reaction to an event, product, or public policy.

Meditative Listening in the Pāli Buddhist Canon

Abstract This article addresses the intersection of the oral tradition with textual contents in Pāli Buddhist literature, by focusing mostly on the Suttapiṭaka. Lexicon and stylistic features can be seen as intentional strategies to enhance specific mnemonic and meditative qualities in the disciples, thus working symbiotically with the instructions they convey. By examining selected passages, I will demonstrate a functional interrelationship of three main factors: the lexicon of listening, the emphasis on attention, and meditative instructions. These three factors, in turn, rely upon the authoritative power of the scriptures, which is constructed and reinforced with specific rhetorical stratagems to raise and enhance meditative qualities in the disciples.

Prompt-Based Word-Level Information Injection BERT for Chinese Named Entity Recognition

Named entity recognition (NER) is a subfield of natural language processing (NLP) that identifies and classifies entities from plain text, such as people, organizations, locations, and other types. NER is a fundamental task in information extraction, information retrieval, and text summarization, as it helps to organize the relevant information in a structured way. The current approaches to Chinese named entity recognition do not consider the category information of matched Chinese words, which limits their ability to capture the correlation between words. This makes Chinese NER more challenging than English NER, which already has well-defined word boundaries. To improve Chinese NER, it is necessary to develop new approaches that take into account category features of matched Chinese words, and the category information would help to effectively capture the relationship between words. This paper proposes a Prompt-based Word-level Information Injection BERT (PWII-BERT) to integrate prompt-guided lexicon information into a pre-trained language model. Specifically, we engineer a Word-level Information Injection Adapter (WIIA) through the original Transformer encoder and prompt-guided Transformer layers. Thus, the ability of PWII-BERT to explicitly obtain fine-grained character-to-word relevant information according to the category prompt is one of its key advantages. In experiments on four benchmark datasets, PWII-BERT outperforms the baselines, demonstrating the significance of fully utilizing the advantages of fusing the category information and lexicon feature to implement Chinese NER.

Dictionary Learning With Low-Rank Coding Coefficients for Tensor Completion.

In this article, we propose a novel tensor learning and coding model for third-order data completion. The aim of our model is to learn a data-adaptive dictionary from given observations and determine the coding coefficients of third-order tensor tubes. In the completion process, we minimize the low-rankness of each tensor slice containing the coding coefficients. By comparison with the traditional predefined transform basis, the advantages of the proposed model are that: 1) the dictionary can be learned based on the given data observations so that the basis can be more adaptively and accurately constructed and 2) the low-rankness of the coding coefficients can allow the linear combination of dictionary features more effectively. Also we develop a multiblock proximal alternating minimization algorithm for solving such tensor learning and coding model and show that the sequence generated by the algorithm can globally converge to a critical point. Extensive experimental results for real datasets such as videos, hyperspectral images, and traffic data are reported to demonstrate these advantages and show that the performance of the proposed tensor learning and coding method is significantly better than the other tensor completion methods in terms of several evaluation metrics.

THE POTENTIAL OF DIGITAL GAME-BASED LEARNING FOR ENGLISH VOCABULARY ACQUISITION AMONG MALAYSIAN ADOLESCENTS

English is one of the mandatory subjects in primary and secondary school. However, few young Malaysians are proficient in the language, especially those living in rural areas. This study investigates the potential of using a word-search puzzle game as an alternative learning tool for English vocabulary acquisition. A word-search puzzle game was designed and developed. Additional dictionary features, such as words’ definitions, synonyms, antonyms, and pronunciation, are included in the word-search puzzle game. An online survey was conducted to evaluate the game experience (i.e. game flow and game challenge), perceived usefulness, and intention to use the game. The participants of this research comprised of a total of 30 adolescents recruited using convenient sampling among the contacts of the researchers. Participants were asked to install and play the word-search puzzle game before responding to the questionnaire. The findings indicate that the word-search puzzle game indeed has the potential to improve English vocabulary acquisition among adolescents.

Towards the characterization of the tumor microenvironment through dictionary learning-based interpretable classification of multiplexed immunofluorescence images

Objective. Histology image analysis is a crucial diagnostic step in staging and treatment planning, especially for cancerous lesions. With the increasing adoption of computational methods for image analysis, significant strides are being made to improve the performance metrics of image segmentation and classification frameworks. However, many developed frameworks effectively function as black boxes, granting minimal context to the decision-making process. Thus, there is a need to develop methods that offer reasonable discriminatory power and a biologically-informed intuition to the decision-making process. Approach. In this study, we utilized and modified a discriminative feature-based dictionary learning (DFDL) paradigm to generate a classification framework that allows for discrimination between two distinct clinical histologies. This framework allows us (i) to discriminate between 2 clinically distinct diseases or histologies and (ii) provides interpretable group-specific representative dictionary image patches, or ‘atoms’, generated during classifier training. This implementation is performed on multiplexed immunofluorescence images from two separate patient cohorts- a pancreatic cohort consisting of cancerous and non-cancerous tissues and a metastatic non-small cell lung cancer (mNSCLC) cohort of responders and non-responders to an immunotherapeutic treatment regimen. The analysis was done at both the image-level and subject-level. Five cell types were selected, namely, epithelial cells, cytotoxic lymphocytes, antigen presenting cells, HelperT cells, and T-regulatory cells, as our phenotypes of interest. Results. We showed that DFDL had significant discriminant capabilities for both the pancreatic pathologies cohort (subject-level AUC-0.8878) and the mNSCLC immunotherapy response cohort (subject-level AUC-0.7221). The secondary analysis also showed that more than 50% of the obtained dictionary atoms from the classifier contained biologically relevant information. Significance. Our method shows that the generated dictionary features can help distinguish patients presenting two different histologies with strong sensitivity and specificity metrics. These features allow for an additional layer of model interpretability, a highly desirable element in clinical applications for identifying novel biological phenomena.

Using Multi-Label Multi-Class Support Vector Machines with Semantic and Lexical Features for Aspect Category Detection

In contrast to the aspects, aspect categories are often coarser and don't always appear as terms in sentences. Besides, the typical way to element the types associated with part is generally grainier concerning factors and doesn't exist within verdicts. The primary intent of the study is to investigate the efficacy of Lexicon, linguistic, vector-based, and features correlated to semantics within the aspect of the responsibility built with the finding of aspect category detection ACD). Semantic and emotional data are captured via vector-based features. Further, it examines vector-based feature superiority issues within the compression of features of text-based characteristics. Study purposes to the linguistic efficacy with the Lexicon, linguistic, and semantic features, also vector-based dependent to the system. Also, the information led with vector-based features that capture the semantic with sentimental analysis characteristics. With the experimental outcomes, the performance efficacy with the vector-based features outperformed text-based features. The methodologies associated with deep learning have generated features within the vector orientation relevant to the word-based structures. Therefore, the proposed method achieved effectiveness with the determined constraints by applying the metrics of precision, recall, and F1 scores. Correlating with the performance of ABSA's state-of-the-art techniques, the proposed research process gained superior outcomes.