21,009 research outputs found

    Cultural evolution in Vietnam’s early 20th century: a Bayesian networks analysis of Franco-Chinese house designs

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    The study of cultural evolution has taken on an increasingly interdisciplinary and diverse approach in explicating phenomena of cultural transmission and adoptions. Inspired by this computational movement, this study uses Bayesian networks analysis, combining both the frequentist and the Hamiltonian Markov chain Monte Carlo (MCMC) approach, to investigate the highly representative elements in the cultural evolution of a Vietnamese city’s architecture in the early 20th century. With a focus on the façade design of 68 old houses in Hanoi’s Old Quarter (based on 78 data lines extracted from 248 photos), the study argues that it is plausible to look at the aesthetics, architecture, and designs of the house façade to find traces of cultural evolution in Vietnam, which went through more than six decades of French colonization and centuries of sociocultural influence from China. The in-depth technical analysis, though refuting the presumed model on the probabilistic dependency among the variables, yields several results, the most notable of which is the strong influence of Buddhism over the decorations of the house façade. Particularly, in the top 5 networks with the best Bayesian Information Criterion (BIC) scores and p\u3c0.05, the variable for decorations (DC) always has a direct probabilistic dependency on the variable B for Buddhism. The paper then checks the robustness of these models using Hamiltonian MCMC method and find the posterior distributions of the models’ coefficients all satisfy the technical requirement. Finally, this study suggests integrating Bayesian statistics in the social sciences in general and for the study of cultural evolution and architectural transformation in particular

    Sentiment analysis to measure quality and build sustainability in tourism destinations

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    The models used for analyzing and measuring quality in tourist destinations are changing with the incorporation of new techniques derived from data science and artificial intelligence. Recent studies show how social media and e-word of mouth (e-WoM) are playing key roles in the perception and image diffusion of tourist destinations. Thus, it is necessary to look for new methods for analyzing the tourist management and attractiveness of tourist spots. This includes conducting a sentiment analysis of tourists that modifies former research methods based on previously proposed model, supported by a survey, which obtained predefined and incomplete results. This study analyzed the quality of tourism in Spain, a major tourist destination that is considered to be the country with the greatest tourist competitiveness according to the World Economic Forum, and in China, the country with the greatest level of development and potential. A sentiment analysis was carried out to measure the quality of tourist destinations in Spain, and this involved three challenges: (1) the analysis of the sentiments of Chinese tourists obtained from e-WoM; (2) the use of new models to measure the quality of a destination based on information from Chinese social networks, and (3) the use of the latest artificial intelligence analytical technologies. Our findings demonstrate how sentiment analysis can be a determining factor in measuring WoM and identifying areas of development in tourist destinations in order to build a more sustainable destination. The results includes the following aspects: (1) the use of real images with more empirical evidence, (2) the use of impressive and disappointing sentiments, (3) a “no comment status”, (4) elimination of stereotypes, and (5) the identification of new opportunities and segments.This research was funded by the program of the Autonomous University of Madrid–Santander Bank, grant number 2017/Asia0

    Application of pre-training and fine-tuning AI models to machine translation: a case study of multilingual text classification in Baidu

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    With the development of international information technology, we are producing a huge amount of information all the time. The processing ability of information in various languages is gradually replacing information and becoming a rarer resource. How to obtain the most effective information in such a large and complex amount of multilingual textual information is a major goal of multilingual information processing. Multilingual text classification helps users to break the language barrier and accurately locate the required information and triage information. At the same time, the rapid development of the Internet has accelerated the communication among users of various languages, giving rise to a large number of multilingual texts, such as book and movie reviews, online chats, product introductions and other forms, which contain a large amount of valuable implicit information and urgently need automated tools to categorize and process those multilingual texts. This work describes the Natural Language Process (NLP) sub-task known as Multilingual Text Classification (MTC) performed within the context of Baidu, a Chinese leading AI company with a strong Internet base, whose NLP division led the industry in deep learning technology to go online in Machine Translation (MT) and search. Multilingual text classification is an important module in NLP machine translation and a basic module in NLP tasks. It can be applied to many fields, such as Fake Reviews Detection, News Headlines Categories Classification, Analysis of positive and negative reviews and so on. In the following work, we will first define the AI model paradigm of 'pre-training and fine-tuning' in deep learning in the Baidu NLP department. Then investigated the application scenarios of multilingual text classification. Most of the text classification systems currently available in the Chinese market are designed for a single language, such as Alibaba's text classification system. If users need to classify texts of the same category in multiple languages, they need to train multiple single text classification systems and then classify them one by one. However, many internationalized products do not have a single text language, such as AliExpress cross-border e-commerce business, Airbnb B&B business, etc. Industry needs to understand and classify users’ reviews in various languages, and have conducted in-depth statistics and marketing strategy development, and multilingual text classification is particularly important in this scenario. Therefore, we focus on interpreting the methodology of multilingual text classification model of machine translation in Baidu NLP department, and capture sets of multilingual data of reviews, news headlines and other data for manual classification and labeling, use the labeling results for fine-tuning of multilingual text classification model, and output the quality evaluation data of Baidu multilingual text classification model after fine-tuning. We will discuss if the pre-training and fine-tuning of the large model can substantially improve the quality and performance of multilingual text classification. Finally, based on the machine translation-multilingual text classification model, we derive the application method of pre-training and fine-tuning paradigm in the current cutting-edge deep learning AI model under the NLP system and verify the generality and cutting-edge of the pre-training and fine-tuning paradigm in the deep learning-intelligent search field.Com o desenvolvimento da tecnologia de informação internacional, estamos sempre a produzir uma enorme quantidade de informação e o recurso mais escasso já não é a informação, mas a capacidade de processar informação em cada língua. A maior parte da informação multilingue é expressa sob a forma de texto. Como obter a informação mais eficaz numa quantidade tão considerável e complexa de informação textual multilingue é um dos principais objetivos do processamento de informação multilingue. A classificação de texto multilingue ajuda os utilizadores a quebrar a barreira linguística e a localizar com precisão a informação necessária e a classificá-la. Ao mesmo tempo, o rápido desenvolvimento da Internet acelerou a comunicação entre utilizadores de várias línguas, dando origem a um grande número de textos multilingues, tais como críticas de livros e filmes, chats, introduções de produtos e outros distintos textos, que contêm uma grande quantidade de informação implícita valiosa e necessitam urgentemente de ferramentas automatizadas para categorizar e processar esses textos multilingues. Este trabalho descreve a subtarefa do Processamento de Linguagem Natural (PNL) conhecida como Classificação de Texto Multilingue (MTC), realizada no contexto da Baidu, uma empresa chinesa líder em IA, cuja equipa de PNL levou a indústria em tecnologia baseada em aprendizagem neuronal a destacar-se em Tradução Automática (MT) e pesquisa científica. A classificação multilingue de textos é um módulo importante na tradução automática de PNL e um módulo básico em tarefas de PNL. A MTC pode ser aplicada a muitos campos, tais como análise de sentimentos multilingues, categorização de notícias, filtragem de conteúdos indesejados (do inglês spam), entre outros. Neste trabalho, iremos primeiro definir o paradigma do modelo AI de 'pré-treino e afinação' em aprendizagem profunda no departamento de PNL da Baidu. Em seguida, realizaremos a pesquisa sobre outros produtos no mercado com capacidade de classificação de texto — a classificação de texto levada a cabo pela Alibaba. Após a pesquisa, verificamos que a maioria dos sistemas de classificação de texto atualmente disponíveis no mercado chinês são concebidos para uma única língua, tal como o sistema de classificação de texto Alibaba. Se os utilizadores precisarem de classificar textos da mesma categoria em várias línguas, precisam de aplicar vários sistemas de classificação de texto para cada língua e depois classificá-los um a um. No entanto, muitos produtos internacionalizados não têm uma única língua de texto, tais como AliExpress comércio eletrónico transfronteiriço, Airbnb B&B business, etc. A indústria precisa compreender e classificar as revisões dos utilizadores em várias línguas. Esta necessidade conduziu a um desenvolvimento aprofundado de estatísticas e estratégias de marketing, e a classificação de textos multilingues é particularmente importante neste cenário. Desta forma, concentrar-nos-emos na interpretação da metodologia do modelo de classificação de texto multilingue da tradução automática no departamento de PNL Baidu. Colhemos para o efeito conjuntos de dados multilingues de comentários e críticas, manchetes de notícias e outros dados para classificação manual, utilizamos os resultados dessa classificação para o aperfeiçoamento do modelo de classificação de texto multilingue e produzimos os dados de avaliação da qualidade do modelo de classificação de texto multilingue da Baidu. Discutiremos se o pré-treino e o aperfeiçoamento do modelo podem melhorar substancialmente a qualidade e o desempenho da classificação de texto multilingue. Finalmente, com base no modelo de classificação de texto multilingue de tradução automática, derivamos o método de aplicação do paradigma de pré-formação e afinação no atual modelo de IA de aprendizagem profunda de ponta sob o sistema de PNL, e verificamos a robustez e os resultados positivos do paradigma de pré-treino e afinação no campo de pesquisa de aprendizagem profunda

    Adaptation of supply management towards a hybrid culture: the case of a Japanese automaker

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    Purpose – This paper aims to identify problems caused by cultural differences between Japan and China that face supply chain managers by applying Japanese-style supply management practices within supply networks in China and present solutions to this problem. Design/methodology/approach – A single, longitudinal case study conducting two waves of data collection (i.e. interviews and observation) plus the collection of much archival data was performed. It goes beyond the dyad by examining supply management of a Japanese company’s supply chain up to three tiers in China. Findings – The four supply cultural differences between Japan and China, which caused the cultural clashes between JVCo and some of its suppliers were revealed and a model of adaptation of Japanese supply management to the Chinese business system was developed. Adaptation involves creating new supply management practices out of selective adaptation, innovation and change of existing Japanese and Chinese supply management practices rooted in different Japanese, Chinese and Western cultures. A list of organisational factors affecting the adaptation has also been provided. Research limitations/implications – Due to the adoption of a single case study method, caution should be given to generalising the findings to all Japanese firms. Practical implications – The Japanese, Chinese and Western managers were provided with insights on how to mitigate the problems caused by cultural differences within supply relationships in China and some innovative ideas on how managers from all three cultures could blend the elements of the three cultures to form a hybrid culture and reduce cultural clashes. Originality/value – This is one of the few attempts to study the transfer of Japanese supply management practice to China. Organizational theory (i.e. transfer of organizational practice and hybridization) is applied and provides a robust framework to explain the supply management practice. This study also answers the call for a global supplier relationship management paradigm

    A Named Entity Recognition Method Enhanced with Lexicon Information and Text Local Feature

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    At present, Named Entity Recognition (NER) is one of the fundamental tasks for extracting knowledge from traditional Chinese medicine (TCM) texts. The variability of the length of TCM entities and the characteristics of the language of TCM texts lead to ambiguity of TCM entity boundaries. In addition, better extracting and exploiting local features of text can improve the accuracy of named entity recognition. In this paper, we proposed a TCM NER model with lexicon information and text local feature enhancement of text. In this model, a lexicon is introduced to encode the characters in the text to obtain the context-sensitive global semantic representation of the text. The convolutional neural network (CNN) and gate joined collaborative attention network are used to form a text local feature extraction module to capture the important semantic features of local text. Experiments were conducted on two TCM domain datasets and the F1 values are 91.13% and 90.21% respectively
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