Search results for: Chinese natural language processing

Authors: Gabriel Montague, Max Vilgalys, Catherine H. Crawford, Jorge Ortiz, Dava Newman

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There has never been so much publicly accessible atmospheric and environmental data. The possibilities of these data are exciting, but the sheer volume of available datasets represents a new challenge for researchers. The task of identifying and working with a new dataset has become more difficult with the amount and variety of available data. Datasets are often documented in ways that differ substantially from the common English used to describe the same topics. This presents a barrier not only for new scientists, but for researchers looking to find comparisons across multiple datasets or specialists from other disciplines hoping to collaborate. This paper proposes a method for addressing this obstacle: creating a knowledge graph to bridge the gap between everyday English language and the technical language surrounding these datasets. Knowledge graph generation is already a well-established field, although there are some unique challenges posed by working with Earth data. One is the sheer size of the databases – it would be infeasible to replicate or analyze all the data stored by an organization like The National Aeronautics and Space Administration (NASA) or the European Space Agency. Instead, this approach identifies topics from metadata available for datasets in NASA’s Earthdata database, which can then be used to directly request and access the raw data from NASA. By starting with a single metadata standard, this paper establishes an approach that can be generalized to different databases, but leaves the challenge of metadata harmonization for future work. Topics generated from the metadata are then linked to topics from a collection of English queries through a variety of standard and custom natural language processing (NLP) methods. The results from this method are then compared to a baseline of elastic search applied to the metadata. This comparison shows the benefits of the proposed knowledge graph system over existing methods, particularly in interpreting natural language queries and interpreting topics in metadata. For the research community, this work introduces an application of NLP to the ecological and environmental sciences, expanding the possibilities of how machine learning can be applied in this discipline. But perhaps more importantly, it establishes the foundation for a platform that can enable common English to access knowledge that previously required considerable effort and experience. By making this public data accessible to the full public, this work has the potential to transform environmental understanding, engagement, and action.

Keywords: earth metadata, knowledge graphs, natural language processing, question-answer systems

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Authors: R. Henry, H. Andriamboavonjy, J. B. Paulin, S. Drahy, R. Gourichon

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During past decade, Chinese monetary policy has been to maintain stability of exchange rate CNY/USD by creating parity between the two currencies. This policy, against market equilibrium, impacts the exchange rate in having low Yuan currency, and keeping attractiveness of Chinese industries. Using macroeconomic and statistic approach, the impact of such policy onto CNY/USD exchange rate is quantitatively determined. It is also pointed out how Chinese banks respect Basel III ratios, in particular the foreign exchange ratio. The main analysis is focusing on how Chinese banks will respect these ratios in the future.

Keywords: macroeconomics models, yuan floating exchange rate, basel iii, china banking system

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Authors: Jin Chuhao

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Sustainable design is the key for the human to realize the harmonious development. However, sustainable design requires localization that combines their own regional culture’s characteristics, then forms the most common cultural identity. As a result, the concept of sustainable design integrates into social behavior and promotes the harmonious development. Chinese Confucian doctrine is one of the important thoughts of human culture, which is accepted by more and more people. This paper summarizes the sustainable concept from the Chinese traditional culture and local design, discusses how they change the life of human being and produces enlightenment and significance to China and world.

Keywords: sustainable design, Chinese traditional culture, harmonious development, Confucianism

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Authors: Burak Oksuzoglu, Savas Yildirim, Ferhat Kutlu

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Opinion mining (OM) is one of the natural language processing (NLP) problems to determine the polarity of opinions, mostly represented on a positive-neutral-negative axis. The data for OM is usually collected from various social media platforms. In an era where social media has considerable control over companies’ futures, it’s worth understanding social media and taking actions accordingly. OM comes to the fore here as the scale of the discussion about companies increases, and it becomes unfeasible to gauge opinion on individual levels. Thus, the companies opt to automize this process by applying machine learning (ML) approaches to their data. For the last two decades, OM or sentiment analysis (SA) has been mainly performed by applying ML classification algorithms such as support vector machines (SVM) and Naïve Bayes to a bag of n-gram representations of textual data. With the advent of deep learning and its apparent success in NLP, traditional methods have become obsolete. Transfer learning paradigm that has been commonly used in computer vision (CV) problems started to shape NLP approaches and language models (LM) lately. This gave a sudden rise to the usage of the pretrained language model (PTM), which contains language representations that are obtained by training it on the large datasets using self-supervised learning objectives. The PTMs are further fine-tuned by a specialized downstream task dataset to produce efficient models for various NLP tasks such as OM, NER (Named-Entity Recognition), Question Answering (QA), and so forth. In this study, the traditional and modern NLP approaches have been evaluated for OM by using a sizable corpus belonging to a large private company containing about 76,000 comments in Turkish: SVM with a bag of n-grams, and two chosen pre-trained models, multilingual universal sentence encoder (MUSE) and bidirectional encoder representations from transformers (BERT). The MUSE model is a multilingual model that supports 16 languages, including Turkish, and it is based on convolutional neural networks. The BERT is a monolingual model in our case and transformers-based neural networks. It uses a masked language model and next sentence prediction tasks that allow the bidirectional training of the transformers. During the training phase of the architecture, pre-processing operations such as morphological parsing, stemming, and spelling correction was not used since the experiments showed that their contribution to the model performance was found insignificant even though Turkish is a highly agglutinative and inflective language. The results show that usage of deep learning methods with pre-trained models and fine-tuning achieve about 11% improvement over SVM for OM. The BERT model achieved around 94% prediction accuracy while the MUSE model achieved around 88% and SVM did around 83%. The MUSE multilingual model shows better results than SVM, but it still performs worse than the monolingual BERT model.

Keywords: BERT, MUSE, opinion mining, pretrained language model, SVM, Turkish

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Authors: Chayanika Hazarika Bordoloi

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This paper is an attempt to work on a highly inflectional language called Assamese. This is also one of the national languages of India and very little has been achieved in terms of computational research. Building a language processing tool for a natural language is not very smooth as the standard and language representation change at various levels. This paper presents inflectional suffixes of Assamese verbs and how the statistical tools, along with linguistic features, can improve the tagging accuracy. Conditional random fields (CRF tool) was used to automatically tag and train the text data; however, accuracy was improved after linguistic featured were fed into the training data. Assamese is a highly inflectional language; hence, it is challenging to standardizing its morphology. Inflectional suffixes are used as a feature of the text data. In order to analyze the inflections of Assamese word forms, a list of suffixes is prepared. This list comprises suffixes, comprising of all possible suffixes that various categories can take is prepared. Assamese words can be classified into inflected classes (noun, pronoun, adjective and verb) and un-inflected classes (adverb and particle). The corpus used for this morphological analysis has huge tokens. The corpus is a mixed corpus and it has given satisfactory accuracy. The accuracy rate of the tagger has gradually improved with the modified training data.

Keywords: CRF, morphology, tagging, tagset

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Authors: Jingsong Ma

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In political translation, ideology plays an important role in conveying implications accurately. Ideological collisions can occur in political translation when there existdifferences of political environments embedded in the translingual political texts in both source and target languages. To reach an accurate translationrequires the translatorto understand the ideologies implied in (and often transcending) the texts. This paper explores the conditions, procedure, and purpose of processingideological collision and resolution of such issues in political translation. These points will be elucidated by case studies of translating English and Chinese political texts. First, there are specific political terminologies in certain political environments. These terminological peculiarities in one language are often determined by ideological elements rather than by syntactical and semantical understanding. The translation of these ideological-loaded terminologiesis a process and operation consisting of understanding the ideological context, including cultural, historical, and political situations. This will be explained with characteristic Chinese political terminologies and their renderings in English. Second, when the ideology in the source language fails to match with the ideology in the target language, the decisions to highlight or disregard these conflicts are shaped by power relations, political engagement, social context, etc. It thus is necessary to go beyond linguisticanalysis of the context by deciphering ideology in political documents to provide a faithful or equivalent rendering of certain messages. Finally, one of the practical issues is about equivalence in political translation by redefining the notion of faithfulness and retainment of ideological messages in the source language in translations of political texts. To avoid distortion, the translator should be liberated from grip the literal meaning, instead diving into functional meanings of the text.

Keywords: translation, ideology, politics, society

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Authors: Aviv Yaniv, Ron Ben Arosh, Nadav Gasner, Michael Konviser, Arbel Yaniv

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This paper introduces two methods for the evaluation of Question Decomposition Meaning Representation (QDMR) as predicted by sequence-to-sequence model and COPYNET parser for natural language questions processing, motivated by the fact that previous evaluation metrics used for this task do not take into account some characteristics of the representation, such as partial ordering structure. To this end, several heuristics to extract such partial dependencies are formulated, followed by the hereby proposed evaluation methods denoted as Proportional Graph Matcher (PGM) and Conversion to Normal String Representation (Nor-Str), designed to better capture the accuracy level of QDMR predictions. Experiments are conducted to demonstrate the efficacy of the proposed evaluation methods and show the added value suggested by one of them- the Nor-Str, for better distinguishing between high and low-quality QDMR when predicted by models such as COPYNET. This work represents an important step forward in the development of better evaluation methods for QDMR predictions, which will be critical for improving the accuracy and reliability of natural language question-answering systems.

Keywords: NLP, question answering, question decomposition meaning representation, QDMR evaluation metrics

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Authors: Bastian Haarmann, Likas Sikorski

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In this paper, we introduce an NLG application for the automatic creation of ready-to-publish texts from big data. The fully automatic generated stories have a high resemblance to the style in which the human writer would draw up a news story. Topics may include soccer games, stock exchange market reports, weather forecasts and many more. The generation of the texts runs according to the human language production. Each generated text is unique. Ready-to-publish stories written by a computer application can help humans to quickly grasp the outcomes of big data analyses, save time-consuming pre-formulations for journalists and cater to rather small audiences by offering stories that would otherwise not exist.

Keywords: big data, natural language generation, publishing, robotic journalism

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Authors: Qiong Li

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This study investigated in which condition computer-mediated communication (CMC) could promote pragmatic development. The focal feature included four Chinese sentence final particles (SFPs), a, ya, ba, and ne. They occur frequently in Chinese, and function as mitigators to soften the tone of speech. However, L2 acquisition of SFPs is difficult, suggesting the necessity of additional exposure to or explicit instruction on Chinese SFPs. This study follows this line and aims to explore two research questions: (1) Is CMC combined with data-driven instruction more effective than CMC alone in promoting L2 Chinese learners’ SFP use? (2) How does L2 Chinese learners’ SFP use change over time, as compared to the production of native Chinese speakers? The study involved 19 intermediate-level learners of Chinese enrolled at a private American university. They were randomly assigned to two groups: (1) the control group (N = 10), which was exposed to SFPs through CMC alone, (2) the treatment group (N = 9), which was exposed to SFPs via CMC and data-driven instruction. Learners interacted with native speakers on given topics through text-based CMC over Skype. Both groups went through six 30-minute CMC sessions on a weekly basis, with a one-week interval after the first two CMC sessions and a two-week interval after the second two CMC sessions (nine weeks in total). The treatment group additionally received a data-driven instruction after the first two sessions. Data analysis focused on three indices: token frequency, type frequency, and acceptability of SFP use. Token frequency was operationalized as the raw occurrence of SFPs per clause. Type frequency was the range of SFPs. Acceptability was rated by two native speakers using a rating rubric. The results showed that the treatment group made noticeable progress over time on the three indices. The production of SFPs approximated the native-like level. In contrast, the control group only slightly improved on token frequency. Only certain SFPs (a and ya) reached the native-like use. Potential explanations for the group differences were discussed in two aspects: the property of Chinese SFPs and the role of CMC and data-driven instruction. Though CMC provided the learners with opportunities to notice and observe SFP use, as a feature with low saliency, SFPs were not easily noticed in input. Data-driven instruction in the treatment group directed the learners’ attention to these particles, which facilitated the development.

Keywords: computer-mediated communication, data-driven instruction, pragmatic development, second language Chinese, sentence final particles

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Authors: Rohan Bhasin

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Agrammatism in non-fluent Aphasia Cases can be defined as a language disorder wherein a patient can only use content words ( nouns, verbs and adjectives ) for communication and their speech is devoid of functional word types like conjunctions and articles, generating speech of with extremely rudimentary grammar . Past approaches involve Speech Therapy of some order with conversation analysis used to analyse pre-therapy speech patterns and qualitative changes in conversational behaviour after therapy. We describe this approach as a novel method to generate functional words (prepositions, articles, ) around content words ( nouns, verbs and adjectives ) using a combination of Natural Language Processing and Deep Learning algorithms. The applications of this approach can be used to assist communication. The approach the paper investigates is : LSTMs or Seq2Seq: A sequence2sequence approach (seq2seq) or LSTM would take in a sequence of inputs and output sequence. This approach needs a significant amount of training data, with each training data containing pairs such as (content words, complete sentence). We generate such data by starting with complete sentences from a text source, removing functional words to get just the content words. However, this approach would require a lot of training data to get a coherent input. The assumptions of this approach is that the content words received in the inputs of both text models are to be preserved, i.e, won't alter after the functional grammar is slotted in. This is a potential limit to cases of severe Agrammatism where such order might not be inherently correct. The applications of this approach can be used to assist communication mild Agrammatism in non-fluent Aphasia Cases. Thus by generating these function words around the content words, we can provide meaningful sentence options to the patient for articulate conversations. Thus our project translates the use case of generating sentences from content-specific words into an assistive technology for non-Fluent Aphasia Patients.

Keywords: aphasia, expressive aphasia, assistive algorithms, neurology, machine learning, natural language processing, language disorder, behaviour disorder, sequence to sequence, LSTM

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Authors: Abdullah Khuwaileh

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The central aim of this paper is to investigate writing skills in the two languages involved, English and Arabic, and to see whether there is an association between poor writing across languages. That is to say, and it is thought that learners might be excellent in their L1 (Language 1: Arabic) but not in L2 (language 2: English). However, our experimental research findings resulted in an interesting association between L1 and L2. Data were collected from 150 students (chosen randomly) who wrote about the same topic in English and Arabic. Topics needed no preparation as they were common and well-known. Scripts were assessed respectively by ELT (English Language Teaching) and Arabic specialists. The study confirms that poor writing in English correlates with similar deficiencies in the mother tongue (Arabic). Thus, the common assumption in ELT that all learners are fully competent in their first language skills is unfounded. Therefore, the criticism of ELT programs for speakers of Arabic, based on poor writing skills in English and good writing in Arabic is not justified. The findings of this paper can be extended to other learners of English who speak Arabic as a first language and English as a foreign and/or second language. The study is concluded with several research and practical recommendations

Keywords: language, writing, culture, l1

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Authors: Ezgi Şendil

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Information extracted from social media has received great attention since it has become an effective alternative for collecting people’s opinions and emotions based on specific experiences in a faster and easier way. The paper aims to put data in a meaningful way to analyze users’ posts and get a result in terms of the experiences and opinions of the users during and after natural disasters. The posts collected from Reddit are classified into nine different categories, including injured/dead people, infrastructure and utility damage, missing/found people, donation needs/offers, caution/advice, and emotional support, identified by using labelled Twitter data and four different machine learning (ML) classifiers.

Keywords: disaster, NLP, postdisaster management, sentiment analysis

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Authors: Xinyu Chen

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This article reviews Chinese psychoanalytic theorist, Dr. Huo Datong’s notion, the complex of generation, around which Huo conceptualizes a localized set to recapitulate the unconscious structure of Chinese people. Psychoanalysis underwent constant localization influenced by the socio-cultural milieu and endeavored by scholars receiving training backgrounds from different psychoanalytic schools. Dr. Huo Datong is one of the representatives with a Sino-French background of psychoanalytic training, whose enterprise has demonstrated psychoanalysis's cultural and ideological accommodability. Insufficient academic attention has been paid to this concept as the core of Huo’s re-framework. This notion is put forward by sharing a western psychoanalytic reading of Chinese mythologies to contour Chinese unconsciousness. Regarding Huo’s interpretation of the Chinese kinship network as the basis to propose an omnipotent symbolic mother rather than an Oedipal father, this article intends to review this notion in terms of its mythological root to evaluate the theoretical practicality.

Keywords: psychoanalysis, China, Huo Datong, mythology

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Authors: Mehrnaz Mostafavi

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The assessment and categorization of incidental lung nodules present a considerable challenge in healthcare, often necessitating resource-intensive multiple computed tomography (CT) scans for growth confirmation. This research addresses this issue by introducing a distinct computational approach leveraging radiomics and deep-learning methods. However, understanding local services is essential before implementing these advancements. With diverse tracking methods in place, there is a need for efficient and accurate identification approaches, especially in the context of managing lung nodules alongside pre-existing cancer scenarios. This study explores the integration of text-based algorithms in medical data curation, indicating their efficacy in conjunction with machine learning and deep-learning models for identifying lung nodules. Combining medical images with text data has demonstrated superior data retrieval compared to using each modality independently. While deep learning and text analysis show potential in detecting previously missed nodules, challenges persist, such as increased false positives. The presented research introduces a Structured-Query-Language (SQL) algorithm designed for identifying pulmonary nodules in a tertiary cancer center, externally validated at another hospital. Leveraging natural language processing (NLP) and machine learning, the algorithm categorizes lung nodule reports based on sentence features, aiming to facilitate research and assess clinical pathways. The hypothesis posits that the algorithm can accurately identify lung nodule CT scans and predict concerning nodule features using machine-learning classifiers. Through a retrospective observational study spanning a decade, CT scan reports were collected, and an algorithm was developed to extract and classify data. Results underscore the complexity of lung nodule cohorts in cancer centers, emphasizing the importance of careful evaluation before assuming a metastatic origin. The SQL and NLP algorithms demonstrated high accuracy in identifying lung nodule sentences, indicating potential for local service evaluation and research dataset creation. Machine-learning models exhibited strong accuracy in predicting concerning changes in lung nodule scan reports. While limitations include variability in disease group attribution, the potential for correlation rather than causality in clinical findings, and the need for further external validation, the algorithm's accuracy and potential to support clinical decision-making and healthcare automation represent a significant stride in lung nodule management and research.

Keywords: lung cancer diagnosis, structured-query-language (SQL), natural language processing (NLP), machine learning, CT scans

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Authors: Deawan Rakin Ahamed Remal, Sinthia Chowdhury, Sharun Akter Khushbu, Sheak Rashed Haider Noori

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Native Speech to text synthesis has its own leverage for the purpose of mankind. The extensive nature of art to speaking different accents is common but the purpose of communication between two different accent types of people is quite difficult. This problem will be motivated by the extraction of the wrong perception of language meaning. Thus, many existing automatic speech recognition has been placed to detect text. Overall study of this paper mentions a review of NSTTR (Native Speech Text to Text Recognition) synthesis compared with Text to Text recognition. Review has exposed many text to text recognition systems that are at a very early stage to comply with the system by native speech recognition. Many discussions started about the progression of chatbots, linguistic theory another is rule based approach. In the Recent years Deep learning is an overwhelming chapter for text to text learning to detect language nature. To the best of our knowledge, In the sub continent a huge number of people speak in Bangla language but they have different accents in different regions therefore study has been elaborate contradictory discussion achievement of existing works and findings of future needs in Bangla language acoustic accent.

Keywords: TTR, NSTTR, text to text recognition, deep learning, natural language processing

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Authors: Saiping An

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The spells in Chinese Buddhism are often regarded by current scholarship as syllables with mystical power, as a ritual and practice of oral chanting, or as texts engraved on cultural relics. This study hopes to point out that the spell as a kind of behavior and material also provokes the believers to interpret its soteriology with various Buddhist doctrines and philosophies. It will analyze Mahāvairocana Tantra which is the main classic of the tradition regarded by the academic circles as 'Esoteric Buddhism', two annotations of these scriptures composed in the Tang and Liao Dynasty respectively, as well as some works of monks and lay Buddhists in the late Ming and early Qing dynasties. It aims to illustrate that spells in Chinese Buddhism are not simply magical voices and the words engraved on the cultural relics; they have also enriched the doctrines and thoughts of Chinese Buddhism. Their nature and soteriological methods are far more abundant than current academic circles have revealed.

Keywords: spell, Chinese Buddhism, philosophy, Buddhist doctrines

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Authors: Chieh-Ning Lan, Tzu-Shin Lin, Kun-Hao Lin

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Chinese handwriting skill is one of the basic skills of school-age children in Taiwan, which helps them to learn most academic subjects. Differ from the alphabetic language system, Chinese written language is a logographic script with a complicated 2-dimensional character structure as a morpheme. Visuospatial ability places a great role in Chinese handwriting to maintain good proportion and alignment of these interwoven strokes. In Taiwan, school-age students faced the challenge to recognize and write down Chinese characters, especially in children with written expression difficulties (CWWDs). In this study, we developed an instructional app to help CWWDs practice Chinese handwriting skills, and we aimed to apply the mobile assisted language learning (MALL) system in clinical writing strategies. To understand the feasibility and satisfaction of this auxiliary instructional writing app, we investigated the perceive and value both from school-age students and the clinic therapists, who were the target users and the experts. A group of 8 elementary school children, as well as 8 clinic therapists, were recruited. The school-age students were asked to go through a paper-based instruction and were asked to score the visual expression based on their graphic preference; the clinic therapists were asked to watch an introductive video of this instructional app and complete the online formative questionnaire. In the results of our study, from the perspective of user interface design, school-age students were more attracted to cartoon-liked pictures rather than line drawings or vivid photos. Moreover, compared to text, pictures which have higher semantic transparency were more commonly chosen by children. In terms of the quantitative survey from clinic therapists, they were highly satisfied with this auxiliary instructional writing app, including the concepts such as visual design, teaching contents, and positive reinforcement system. Furthermore, the qualitative results also suggested comprehensive positive feedbacks on the teaching contents and the feasibility of integrating the app into clinical treatments. Interestingly, we found that clinic therapists showed high agreement in approving CWWDs’ writing ability with using orthographic knowledge; however, in the qualitative section, clinic therapists pointed out that CWWDs usually have relative insufficient background knowledge in Chinese character orthographic rules, which because it is not a key-point in conventional handwriting instruction. Also, previous studies indicated that conventional Chinese reading and writing instructions were lacked of utilizing visual-spatial arrangement strategies. Based on the sharing experiences from all participants, we concluded several interesting topics that are worth to dedicate to in the future. In this undergoing app system, improvement and revision will be applied into the system design, and will establish a better and more useful instructional system for CWWDs within their treatments; enlightened by the opinions related to learning content, the importance of orthographic knowledge in Chinese character recognition should be well discussed and involved in CWWDs’ intervention in the future.

Keywords: auxiliary instructional app, children with writing difficulties, Chinese handwriting, orthographic knowledge

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Authors: Dan Xie

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The use of discourse markers (DMs) can play a crucial role in representing discourse interaction and pragmatic competence. Learners’ use of DMs and differences between native speakers (NSs) and non-native speakers (NNSs) in the use of various DMs have been the focus of considerable research attention. However, some commonly used DMs, such as you know, have not received as much attention in comparative studies, especially in the Chinese context. This study analyses data in two corpora (COLSEC and Spoken BNC 2014 (14-25)) to investigate how Chinese learners differ from NNSs in their use of the DM you know and its functions in speech. The results show that there is a significant difference between the two corpora in terms of the frequency of use of you know. In terms of the functions of you know, the study shows that six functions can all be present in both corpora, although there are significant differences between the five functional dimensions, especially in introducing a claim linked to the prior discourse and highlighting particular points in the discourse. It is hoped to show empirically how Chinese learners and NSs use DMs differently.

Keywords: you know, discourse marker, native speaker, Chinese learner

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Authors: Lai Wenfang

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Tthis study will describe how to use artificial intelligence (AI) technology to build the user-oriented platform for integrated archival service. The platform will be launched in 2020 by the National Archives Administration (NAA) in Taiwan. With the progression of information communication technology (ICT) the NAA has built many systems to provide archival service. In order to cope with new challenges, such as new ICT, artificial intelligence or blockchain etc. the NAA will try to use the natural language processing (NLP) and machine learning (ML) skill to build a training model and propose suggestions based on the data sent to the platform. NAA expects the platform not only can automatically inform the sending agencies’ staffs which records catalogues are against the transfer or destroy rules, but also can use the model to find the details hidden in the catalogues and suggest NAA’s staff whether the records should be or not to be, to shorten the auditing time. The platform keeps all the users’ browse trails; so that the platform can predict what kinds of archives user could be interested and recommend the search terms by visualization, moreover, inform them the new coming archives. In addition, according to the Archives Act, the NAA’s staff must spend a lot of time to mark or remove the personal data, classified data, etc. before archives provided. To upgrade the archives access service process, the platform will use some text recognition pattern to black out automatically, the staff only need to adjust the error and upload the correct one, when the platform has learned the accuracy will be getting higher. In short, the purpose of the platform is to deduct the government digital transformation and implement the vision of a service-oriented smart government.

Keywords: artificial intelligence, natural language processing, machine learning, visualization

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Authors: Tiago do Carmo Nogueira, Cássio Dener Noronha Vinhal, Gélson da Cruz Júnior, Matheus Rudolfo Diedrich Ullmann

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Generating automatic image descriptions through natural language is a challenging task. Image captioning is a task that consistently describes an image by combining computer vision and natural language processing techniques. To accomplish this task, cutting-edge models use encoder-decoder structures. Thus, Convolutional Neural Networks (CNN) are used to extract the characteristics of the images, and Recurrent Neural Networks (RNN) generate the descriptive sentences of the images. However, cutting-edge approaches still suffer from problems of generating incorrect captions and accumulating errors in the decoders. To solve this problem, we propose a model based on the encoder-decoder structure, introducing a module that generates the weights according to the importance of the word to form the sentence, using the part-of-speech (PoS). Thus, the results demonstrate that our model surpasses state-of-the-art models.

Keywords: gated recurrent units, caption generation, convolutional neural network, part-of-speech

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Authors: Haiping Zhu

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The purpose of this work-in-progress paper is to explore how the Chinese philosophic-religious tradition of Taoism impacts on the consumption behaviour of contemporary Chinese consumers. Although much cultural research has been conducted on Chinese consumption behaviours, most studies have approached the subject from Western perspectives. Examination of the limited literature indicates a gap in the knowledge of the relationship of traditional Chinese Taoism philosophy and Chinese consumption behaviour. To bridge this gap, this study examines Chinese consumption behaviour at a Taoist-related Chinese religious festival - the DuanWu festival - in order to seek some understanding of how the Taoism philosophic-religious tradition influences Chinese consumption behaviour from the point of view of the individuals involved. It focuses attention on their expression of Taoism cultural values, purchasing experience and subsequent consumption behaviours. This study undertook multiple methods for Taoist case study data collection: accompanied shopping with Taoists before DuanWu Festival; participant observations during DuanWu Festival; and in-depth interviews in order to explore Taoists consumption behaviours at the end of the Festival. Specifically, the finding from the Taoist case study corroborates and details the influence of the Taoism doctrine: man–nature orientation, Fenshui, ecological effect, and ecological knowledge, on their attitudes toward green purchasing behaviour. Findings from this Taoist case study - one of a series of three Chinese philosophic religious tradition case studies - contribute to the deeper understanding of contemporary Chinese consumers from a non-Western viewpoint and offer initial insights for global marketers to differentiate consumer needs and develop effective marketing strategies.

Keywords: consumer behaviour, culture values, green purchase behaviour, Taoism

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Authors: Mark Shiu-kee Shum, Dan Shi

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The paper aims to introduce the ‘Reading to Learn, Learning to Write’ (R2L) pedagogy and its application in teaching reading and writing to non-Chinese speaking (NCS) students in Hong Kong. Guided by the teaching and learning cycles accentuated in R2L pedagogy, sufficient scaffolding was provided for students with an explicit teaching method in literacy education. To understand the influence of using R2L pedagogy on students’ reading and writing abilities across different genres, quantitative data were collected by pre- and post-test of reading and writing tasks in the two different genres of narration and explanation. The pre-test and post-test were used to assess students’ writing performance based on the three textual components of context, discourse, and graphic features, while the reading abilities were assessed at the literal, inferred and interpretive levels of reading comprehension to measure the effectiveness of R2L pedagogy on their literacy improvement. The findings show the use of R2L pedagogy has been proven more effective in improving NCS students’ writing abilities than developing their reading capacity. It is hoped that the R2L-based pedagogic practices can serve as teaching references and pedagogic rationale for L1 language teachers and raise their metalinguistic awareness in teaching Chinese to non-Chinese speaking students in Hong Kong and beyond.

Keywords: pedagogical inclusiveness, literacy education, ethnic minority, reading and writing

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Authors: Charlotte Entwistle, Ryan Boyd

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Recent developments in information retrieval techniques and natural language processing have allowed for greater exploration of psychological and social processes. Linguistic analysis methods for understanding behaviour have provided useful insights within the field of mental health. One area within mental health that has received little attention though, is borderline personality disorder (BPD). BPD is a common mental health disorder characterised by instability of interpersonal relationships, self-image and affect. It also manifests through maladaptive behaviours, such as impulsivity and self-harm. Examination of language patterns associated with BPD could allow for a greater understanding of the disorder and its links to maladaptive thoughts and behaviours. Language analysis methods could also be used in a predictive way, such as by identifying indicators of BPD or predicting maladaptive thoughts, emotions and behaviours. Additionally, associations that are uncovered between language and maladaptive thoughts and behaviours could then be applied at a more general level. This study explores linguistic characteristics of BPD, and their links to maladaptive thoughts and behaviours, through the analysis of social media data. Data were collected from a large corpus of posts from the publicly available social media platform Reddit, namely, from the ‘r/BPD’ subreddit whereby people identify as having BPD. Data were collected using the Python Reddit API Wrapper and included all users which had posted within the BPD subreddit. All posts were manually inspected to ensure that they were not posted by someone who clearly did not have BPD, such as people posting about a loved one with BPD. These users were then tracked across all other subreddits of which they had posted in and data from these subreddits were also collected. Additionally, data were collected from a random control group of Reddit users. Disorder-relevant behaviours, such as self-harming or aggression-related behaviours, outlined within Reddit posts were coded to by expert raters. All posts and comments were aggregated by user and split by subreddit. Language data were then analysed using the Linguistic Inquiry and Word Count (LIWC) 2015 software. LIWC is a text analysis program that identifies and categorises words based on linguistic and paralinguistic dimensions, psychological constructs and personal concern categories. Statistical analyses of linguistic features could then be conducted. Findings revealed distinct linguistic features associated with BPD, based on Reddit posts, which differentiated these users from a control group. Language patterns were also found to be associated with the occurrence of maladaptive thoughts and behaviours. Thus, this study demonstrates that there are indeed linguistic markers of BPD present on social media. It also implies that language could be predictive of maladaptive thoughts and behaviours associated with BPD. These findings are of importance as they suggest potential for clinical interventions to be provided based on the language of people with BPD to try to reduce the likelihood of maladaptive thoughts and behaviours occurring. For example, by social media tracking or engaging people with BPD in expressive writing therapy. Overall, this study has provided a greater understanding of the disorder and how it manifests through language and behaviour.

Keywords: behaviour analysis, borderline personality disorder, natural language processing, social media data

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Authors: Yuhang Wang

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As energy is vital to industrial productivity and human existence, ensuring energy security becomes a critical government responsibility. The Chinese government has implemented the energy transition to safeguard China’s energy security. Throughout this progression, the Chinese government has faced numerous obstacles. This article seeks to describe the causes of China’s energy transition barriers and the steps taken by the Chinese government to overcome them.

Keywords: energy transition, energy market, fragmentation, path dependency

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Authors: Manjitsingh Rajput

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Visual communication plays an important role in artificial intelligence (AI) because it enables machines to understand and interpret visual information, similar to how humans do. This abstract explores the importance of visual communication in AI and emphasizes the importance of various applications such as computer vision, object emphasis recognition, image classification and autonomous systems. In going deeper, with deep learning techniques and neural networks that modify visual understanding, In addition to AI programming, the abstract discusses challenges facing visual interfaces for AI, such as data scarcity, domain optimization, and interpretability. Visual communication and other approaches, such as natural language processing and speech recognition, have also been explored. Overall, this abstract highlights the critical role that visual communication plays in advancing AI capabilities and enabling machines to perceive and understand the world around them. The abstract also explores the integration of visual communication with other modalities like natural language processing and speech recognition, emphasizing the critical role of visual communication in AI capabilities. This methodology explores the importance of visual communication in AI development and implementation, highlighting its potential to enhance the effectiveness and accessibility of AI systems. It provides a comprehensive approach to integrating visual elements into AI systems, making them more user-friendly and efficient. In conclusion, Visual communication is crucial in AI systems for object recognition, facial analysis, and augmented reality, but challenges like data quality, interpretability, and ethics must be addressed. Visual communication enhances user experience, decision-making, accessibility, and collaboration. Developers can integrate visual elements for efficient and accessible AI systems.

Keywords: visual communication AI, computer vision, visual aid in communication, essence of visual communication.

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Authors: Wei Zhang, Lifen Zhao

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The present study aims to translate and validate the Career Motivation Scale among Chinese university and vocational college teachers. Exploratory factor analysis supported a three-factor structure that was consistent with the original structure of career motivation: career insight, career identity, and career resilience. Confirmatory factor analysis showed that a second-order three-factor model with correlated measurement errors best fit the data. Configural, metric, and scalar invariance models were tested, demonstrating that the Chinese version of the Career Motivation Scale did not differ across groups of school type, educational level, and working years in current institutions. The concurrent validity of the Chinese Career Motivation Scale was confirmed by its significant correlations with work engagement, career adaptability, career satisfaction, job crafting, and intention to quit. The results of the study indicated that the Chinese Career Motivation Scale was a valid and reliable measure of career motivation among university and vocational college teachers in China.

Keywords: career motivation scale, Chinese University, vocational college teachers, measurement invariance, validation

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Authors: Marta Blavia

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Magiciens de la terre, organized in 1989 by the Centre Pompidou, became 'the first worldwide exhibition of contemporary art' by presenting artists from Western and non-Western countries, including three Chinese artists. For the first time, West turned its eyes to other countries not as exotic sources of inspiration, but as places where contemporary art was also being created. One year later, Chine: demain pour hier was inaugurated as the first Chinese avant-garde group-exhibition in Occident. Among the artists included was Cai Guo-Qiang who, like many other Chinese artists, had left his home country in the eighties in pursuit of greater creative freedom. By exploring artistic non-Western perspectives, both landmark exhibitions questioned the predominance of the Eurocentric vision in the construction of history art. But more than anything else, these exhibitions laid the groundwork for the rise of the so-called phenomenon 'global contemporary art'. All the same time, 1989 also was a turning point in Chinese art history. Because of the Tiananmen student protests, The Chinese government undertook a series of measures to cut down any kind of avant-garde artistic activity after a decade of a relative openness. During the eighties, and especially after the Tiananmen crackdown, some important artists began to leave China to move overseas such as Xu Bing and Ai Weiwei (USA); Chen Zhen and Huang Yong Ping (France); or Cai Guo-Qiang (Japan). After emigrating abroad, Chinese overseas artists began to develop projects in accordance with their new environments and audiences as well as to appear in numerous international exhibitions. With their creations, that moved freely between a variety of Eastern and Western art sources, these artists were crucial agents in the emergence of global contemporary art. As other Chinese artists overseas, Cai Guo-Qiang’s career took off during the 1990s and early 2000s right at the same moment in which Western art world started to look beyond itself. Little by little, he developed a very personal artistic language that redefines Chinese ideas, symbols, and traditional materials in a new world order marked by globalization. Cai Guo-Qiang participated in many of the exhibitions that contributed to shape global contemporary art: Encountering the Others (1992); the 45th Venice Biennale (1993); Inside Out: New Chinese Art (1997), or the 48th Venice Biennale (1999), where he recreated the Chinese monumental social realist work Rent Collection Courtyard that earned him the Golden Lion Award. By examining the different stages of Cai Guo-Qiang’s artistic path as well as the transnational dimensions of his creations, this paper aims at offering a comprehensive survey on the construction of the discourse of global contemporary art.

Keywords: Cai Guo-Qiang, Chinese artists overseas, emergence global art, transnational art

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Authors: Franklin Thambi Jose.S

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Computational Linguistics is a branch of linguistics, which deals with the computer and linguistic levels. It is also said, as a branch of language studies which applies computer techniques to linguistics field. In Computational Linguistics, Natural Language Processing plays an important role. This came to exist because of the invention of Information Technology. In computational syntax, the syntactic analyser breaks a sentence into phrases and clauses and identifies the sentence with the syntactic information. Tamil is one of the major Dravidian languages, which has a very long written history of more than 2000 years. It is mainly spoken in Tamilnadu (in India), Srilanka, Malaysia and Singapore. It is an official language in Tamilnadu (in India), Srilanka, Malaysia and Singapore. In Malaysia Tamil speaking people are considered as an ethnic group. In Tamil syntax, the sentences in Tamil are classified into four for this research, namely: 1. Main Sentence 2. Interrogative Sentence 3. Equational Sentence 4. Elliptical Sentence. In computational syntax, the first step is to provide required information regarding the head and its constituent of each sentence. This information will be incorporated to the system using programming languages. Now the system can easily analyse a given sentence with the criteria or mechanisms given to it. Providing needful criteria or mechanisms to the computer to identify the basic types of sentences using Syntactic parser in Tamil language is the major objective of this paper.

Keywords: tamil, syntax, criteria, sentences, parser

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Authors: Jiashu Shen

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Objectives: Spirometry is a basic reference for health evaluation which is widely used in clinical. Previous reference of spirometry is not applicable because of drastic changes of social and natural circumstance in China. A new reference values for the spirometry of the Chinese population is extremely needed. Method: Spirometric reference value was established using the statistical modeling method Generalized Additive Models for Location, Scale and Shape for forced expiratory volume in 1 s (FEV1), forced vital capacity (FVC), FEV1/FVC, and maximal mid-expiratory flow (MMEF). Results: Data from 236,606 healthy non-smokers aged 4–90 years was collected from the MJ Health Check database. Spirometry equations for FEV1, FVC, MMEF, and FEV1/FVC were established, including the predicted values and lower limits of normal (LLNs) by sex. The predictive equations that were developed for the spirometric results elaborated the relationship between spirometry and age, and they eliminated the effects of height as a variable. Most previous predictive equations for Chinese spirometry were significantly overestimated (to be exact, with mean differences of 22.21% in FEV1 and 31.39% in FVC for males, along with differences of 26.93% in FEV1 and 35.76% in FVC for females) or underestimated (with mean differences of -5.81% in MMEF and -14.56% in FEV1/FVC for males, along with a difference of -14.54% in FEV1/FVC for females) the results of lung function measurements as found in this study. Through cross-validation, our equations were established as having good fit, and the means of the measured value and the estimated value were compared, with good results. Conclusions: Our study updates the spirometric reference equations for Chinese people of all ages and provides comprehensive values for both physical examination and clinical diagnosis.

Keywords: Chinese, GAMLSS model, reference values, spirometry

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Authors: A. Van Staden, N. Purcell

Abstract:

Spelling development is complex and multifaceted and relies on several cognitive-linguistic processes. This paper explored the spelling difficulties of English second language learners with auditory processing delays. This empirical study aims to address these issues by means of an intervention design. Specifically, the objectives are: (a) to develop and implement a multi-sensory spelling program for second language learners with auditory processing difficulties (APD) for a period of 6 months; (b) to assess the efficacy of the multi-sensory spelling program and whether this intervention could significantly improve experimental learners' spelling, phonological awareness, and processing (PA), rapid automatized naming (RAN), working memory (WM), word reading and reading comprehension; and (c) to determine the relationship (or interplay) between these cognitive and linguistic skills (mentioned above), and how they influence spelling development. Forty-four English, second language learners with APD were sampled from one primary school in the Free State province. The learners were randomly assigned to either an experimental (n=22) or control group (n=22). During the implementation of the spelling program, several visual, tactile and kinesthetic exercises, including the utilization of fingerspelling were introduced to support the experimental learners’ (N = 22) spelling development. Post-test results showed the efficacy of the multi-sensory spelling program, with the experimental group who were trained in utilising multi-sensory coding and fingerspelling outperforming learners from the control group on the cognitive-linguistic, spelling and reading measures. The results and efficacy of this multi-sensory spelling program and the utilisation of fingerspelling for hearing second language learners with APD open up innovative perspectives for the prevention and targeted remediation of spelling difficulties.

Keywords: English second language spellers, auditory processing delays, spelling difficulties, multi-sensory intervention program

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