Chinese Address Research Articles

The Influencing Mechanism and Motivation of the Bystander on the Choice of Chinese Address Forms.

Chinese address terms exhibits a high degree of variability. Guided by the extended context of situation framework within the field of systemic functional linguistics, and taking the dialogues of the modern Chinese TV drama In the Name of People as examples, this study investigated how the bystander influences the choice of address terms. It is found that the appearance of the bystander influences the choice of address terms. Yet, it is not a direct process but is mediated by conversational atmosphere. Specifically, the presence of a bystander triggers "situational synergy" or changes interpersonal relationship, and then change the conversational atmosphere, both paths ultimately impact the choice of address terms. The underlying forces that lead to the variability of Chinese address terms are guān běn wèi and miàn zǐ. This study suggests the intricate impact of context on language choice, as well as the interdependency of context variables. In addition, by taking the concept of face beyond its European/North American milieu, and relocating it following the Chinese point of view, group orientation is valued, which contributes to face study.

Study on the Generalization of Chinese Address Terms "X+Teacher"

Study on the Generalization of Chinese Address Terms "X+Teacher"

A new trend in Chinese address and its theoretical implications

This study examines the factors that affect people’s impressions of chat commerce in contemporary Chinese e-commerce, where various types of address terms and related expressions are used by online shop assistants. Our survey revealed that interpersonal distance was the strongest factor influencing the favourability of chat styles. Specifically, the distant type was rated the highest, whereas the close and mixed types were rated low, although they are used in practice increasingly frequently. We argue that this overt contradiction can be accounted for in terms of two perspectives: One is the district of residence, which was the second-strongest factor in our survey, and the other is the traditional Chinese “family culture”, in which people are treated both on a generational basis and on a closeness basis simultaneously. Moreover, we also discuss that the rusty pick-up lines, which express sympathetic proximity in content, combined with reverential distance in address form, perform a “bifocal” function.

Chinese terms of address in apology and request

The aim of the present study is to explore how Chinese terms of address are conventionally used in the speech acts of apology and request from a contrastive pragmatic angle. The study fills an important knowledge gap, considering that little attention has been paid to the use of address terms in the performance of speech acts. The research presented has the following bipartite structure. First, a multiple-choice Discourse Completion Tests (DCT) was administered to a group of young learners of Chinese as a foreign language (CFLLs) and a comparable group of Chinese native speakers (NSs). The DCT results reveal that Chinese NSs use address terms significantly more frequently in realising apologies and requests compared to the Hungarian CFLLs. Following the DCT, an online survey was conducted with Chinese linguacultural insiders on the use of address terms in apologies and requests. The findings from the online survey affirm that Chinese address terms serve pragmatic functions beyond mere attention-getters in the performance of speech acts. These findings not only identify the use of address terms as a prominent pragmatic feature in the speech acts performance in Chinese, but also hold strong implications for CFL education in fostering learners’ pragmatic competence.

A Novel Chinese Address Segmentation Method with Self-growth Feature

Chinese Address Segmentation (CAS) is a crucial step that can greatly enhance the performance, accuracy, and reliability of geo-coding technology. However, it presents a tremendous challenge due to the inherent lack of obvious word boundaries, complex grammatical and semantic features. To address this challenge, we propose a novel CAS method or model that starts from scratch, without relying on any pre-installed knowledge about Chinese addresses. Instead, it dynamically evolves and grows its knowledge library by leveraging contextual information and comparing addresses during the process of dividing them into address elements. Our approach does not rely on Chinese language or address-element dictionaries, nor does it depend on address statistics. The knowledge library is automatically extracted and organized in a tree data structure. This unique approach allows our method to effectively segment addresses from any area of China, including regions with intricate address expressions, such as the Inner Mongolia Autonomous Region. Experimental results demonstrate that our method achieves high precision in address segmentation.

Non-Standard Address Parsing in Chinese Based on Integrated CHTopoNER Model and Dynamic Finite State Machine

Information in non-standard address texts in Chinese is usually presented with rough content, complex and diverse presentation forms, and inconsistent hierarchical granularity, causing low accuracy in Chinese address parsing. Therefore, we propose a method for parsing non-standard address text in Chinese that integrates the Chinese Toponym Named Entity Recognition (CHTopoNER) model and a dynamic finite state machine (FSM). First, named entity recognition is performed by the CHTopoNER model. Sets of dynamic FSMs are then constructed based on the address hierarchical characteristics to sort and combine the Chinese address elements, thereby achieving address parsing on the Chinese internet. This method showed excellent accuracy in parsing both standard and non-standard placename addresses. In particular, this method performed better in address parsing for disordered or missing hierarchical elements than traditional methods using an FSM. Specifically, this method achieved accuracies of 96.6% and 96.8% for standard and non-standard placenames, respectively. These accuracies increased by 8.0% and 57.1%, respectively, compared with the integrated CHTopoNER model and traditional FSM, and by 7.4% and 19.8%, respectively, compared with the integrated CHTopoNER model and bidirectional FSM. After analysis, the address-parsing method showed good scalability and adaptability, which could be applied to various types of address-parsing tasks.

An Analysis of Chinese Address Terms From the Perspective of Speech Act Theory

An Analysis of Chinese Address Terms From the Perspective of Speech Act Theory

A graph‐based method for Chinese address matching

AbstractAddress matching is a substantial task in location‐based services. Currently, major address matching methods either perform rather badly on unstructured data or fail to extract adequate semantic information of address elements. In this article, we propose a graph‐based method that can deal with both sides of the problem. First, we use a pretrained transformer neural network to handle address tokenization. Then we parse address tokens into address elements according to their parts of speech. Then the node2vec and tf‐idf technique is used to generate node embeddings for each address element. Finally, an address matching graph convolutional network is applied to do the address matching work. We have carried out a series of experiments on a real‐world Chinese address corpus, to further evaluate the impacts of our methods. The experimental results indicate our method achieves higher scores than the state‐of‐the‐art methods.

LongArms: Fraud Prediction in Online Lending Services Using Sparse Knowledge Graph

Gang fraud, the major and primary security issue in online lending services, can be efficiently solved by the data-driven paradigm that is recognized as a promising solution for online lending gang fraud prediction. However, it is challenging that such predictions need to detect evolving and increasingly impalpable fraud patterns based on low-quality data, i.e., very preliminary and coarse applicant information. The technical difficulty mainly stems from two factors: the extreme <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">deficiency of information associations</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">weakness of data labels</i> . In this work, we mainly address the challenges by enhancing the utility of associations (i.e., <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">recovering missing associations</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">mining underlying associations</i> ) on a knowledge graph. Specifically, we first propose an efficient method of Chinese address disambiguation to recover some critical associations that are broken by the ambiguity of applicant information, e.g., address related information. Then, to mine the implicit associations, we design a novel association representation method, called <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Adaptive Connected Component Embedding Simplification Scheme</i> (ACCESS), which can adaptively implement embedding for different connected components depending on their sizes. Finally, we adopt the graph clustering algorithms and devised predicting schemes based on the above enhanced associations to predict gang fraud in the case of weakness of data labels. Moreover, we propose a framework called RMCP by integrating the above techniques, which is consists of four steps: <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Recovering</i> , <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Mining</i> , <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Clustering</i> , and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Predicting</i> , for efficiently predicting gang fraud. The good performance is validated by the experiments on a real-world dataset from a commercial lending company. Meanwhile, we provide a visual decision support system named <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">LongArms</i> over the RMCP framework.

Nikolay Кaramzin’s Dedication to the Emperor Alexander I as a Preamble to the “History of Russian State” in Manchu and Chinese Translation by Zakhar Leontievsky

The first official history of the Russian state Istoriia gosudarstva Rossijskogo (History of Russian State) was compiled by Nikolay Karamzin in 12 volumes (published in 18161828). The first eight volumes were printed in 18161818 and were most probably taken to Beijing by the members of the 10th Ecclesiastic mission (18201830). Among the students of that mission was Zakhar Leontievsky (17991874), who had spent ten years in Beijing and had perfectly mastered the Chinese and Manchu languages. During his stay in China, Leontievsky has translated into Chinese three volumes of Karamzins History of Russian state. Nowadays, the manuscript copies of this translation are kept in the Oriental collections in St. Petersburg, Russia. The Russian original by Karamzin begins with the Dedication to the Russian tzar Alexander I. Only the manuscript from the Oriental department of the Scientific library of the St. Petersburg state university has the Dedication translated into two languages Manchu and Chinese. The comparison of the Russian original with the Manchu and Chinese versions shows that the translations turn to be Leontievskys interpretations of the original text written according to the rules of Chinese addresses to the throne. Additional translators comments were added to explain some episodes from the Russian history to the Chinese reader. Zakhar Leontievskys translation of the History of Russian state was the first introduction of Russian history to China.

Chinese Address Terms “Shifu” (师傅) vs. “Shifu” (师父): A Comparative and Diachronic Study Based on the BCC Corpus

This paper compares the usage of the address terms “shifu”(师傅) and “shifu”(师父) in Chinese in a diachronic manner. The study investigates the evolution and different usage of the two address terms and analyzes the possible reasons for the different choices to use these address terms. The data for this study comes from the BCC corpus (BLCU Corpus Center), a corpus with tens of billions of words built by Xun Endong et al. (2015, 2016) from Beijing Language and Culture University. The findings show that the highest frequency used for “shifu” (师傅) occured in 1963, while “shifu” (师父) appeared in 2001. The address term “shifu” (师父) is more often used in ancient Chinese, while “shifu” (师傅) is relatively more frequent than “shifu” (师父) in modern and contemporary Chinese. The original meanings between the two address terms are different: “shifu” (师傅) is used as official title to indicate a teacher, while “shifu” (师父) is more related to religions through giving non-evil life and knowledge to others. The research findings will surely benefit the corpus linguistic studies, translation studies and the study of address terms. By resorting to the abundant corpus data, this study could offer a relatively more comprehensive and systematic comparison of the two Chinese address terms by far. Furthermore, i t will help further distinguish the two address terms based on the empirical data and detailed case analysis . Keywords: address term, BCC corpus, comparative study, diachronic study. DOI: https://doi.org/10.55463/hkjss.issn.1021-3619.60.12

The Pattern and Translation of Chinese Address Terms in Contemporary Film Happiness Around the Corner

This paper discusses the translation and classification of Chinese address terms by selecting a representative film with a range of contexts. The data for this study were collected from a Chinese comedy film Happiness Around the Corner screened in 2018 with a length of 91.34 minutes. This film was chosen firstly because it was highly rated by the Chinese mainstream media People’s Daily Overseas Edition for its conveying of positive energy in a humorous manner. Furthermore, considering a range of contexts and interlocutors in this film, the data collected from this film would be sufficient to reach the expected goals of the study. This study employs a qualitative approach to explore a proper classification of Chinese contemporary address terms. The Chinese address terms found in the selected film were classified into seven types: namely nickname, professional title, kinship terms, fictive kinship terms, professional title with surname, offensive address terms and full name. The findings show that professional title, nickname and kinship terms appeared with higher frequency than offensive address terms and full name in this film, which could be explained from the perspective of sociolinguistics. Literal plus liberal translation strategies are recommended to translate address terms using homophonic puns, and equivalent words in target language are advised in most cases. These findings could not only throw light on the classification of address terms in Chinese, but also promote translation studies.

Deep Transfer Learning Model for Semantic Address Matching

Address matching, which aims to match an input descriptive address with a standard address in an address database, is a key technology for achieving data spatialization. The construction of today’s smart cities depends heavily on the precise matching of Chinese addresses. Existing methods that rely on rules or text similarity struggle when dealing with nonstandard address data. Deep-learning-based methods often require extracting address semantics for embedded representation, which not only complicates the matching process, but also affects the understanding of address semantics. Inspired by deep transfer learning, we introduce an address matching approach based on a pretraining fine-tuning model to identify semantic similarities between various addresses. We first pretrain the address corpus to enable the address semantic model (abbreviated as ASM) to learn address contexts unsupervised. We then build a labelled address matching dataset using an address-specific geographical feature, allowing the matching problem to be converted into a binary classification prediction problem. Finally, we fine-tune the ASM using the address matching dataset and compare the output with several popular address matching methods. The results demonstrate that our model achieves the best performance, with precision, recall, and an F1 score above 0.98.

W-TextCNN: A TextCNN model with weighted word embeddings for Chinese address pattern classification

Geocoding is crucial to support location-based services and has become a widely accessible technique in geographic information systems (GIS). In a geocoding system, addresses are one of the main geographical reference texts as input. Address patterns refer to the organizational rules of combining address components into an address. In China, intricate rules and backwards address planning make address patterns not systematic and difficult to recognize, which creates significant challenges for database construction and address standardization. Inspired by deep learning methods, this paper provides a convolutional neural network for text with weighted word embeddings (W-TextCNN) for Chinese address pattern classification. Specifically, we define eight address patterns to represent the structures of addresses considering the characteristics of address components. For calculating addresses in the neural network, word embeddings with a weighted strategy are implemented for transforming address texts into real-valued vectors. The vectors are fed into a convolutional neural network for text (TextCNN) to train for classifying address patterns automatically. Furthermore, we apply W-TextCNN in the address corpus after fine-tuning the hyperparameters and compare it with several methods commonly used in text classification. We also design two tasks address segmentation and address matching to explore the effect of address pattern classification. The accuracy and F1 score of the model on classification achieve 97.45% and 96%, respectively, and W-TextCNN outperforms TextCNN because of the employment of the weighted word embeddings. Additionally, the results reveal the positive impact of address pattern classification on improving segmentation precision and address quality. The proposed model is expected to expand the toolkit of computational address study with deep learning methods.

Address inputting while driving: a comparison of four alternative text input methods on in-vehicle navigation displays usability and driver distraction

Objective Efficient and safe address entry is crucial to in-vehicle navigation systems. Although various text input methods (TIMs) are commercially available, to date, the details of the driver’s interactions with these TIMs in the vehicle are poorly understood. Therefore, the effect of four alternative TIMs conditions on in-vehicle navigation displays usability and driver distraction were directly compared. For reference, the baseline driving task (distraction-free) was also investigated. Methods A city expressway simulator experiment including 25 young drivers was launched. Under each condition, the driving task was lane-keeping with speed ranging between 40 and 60 km/h, and the navigation task was to enter a 14-characters Chinese address name. In the meantime, usability (text entry time, number of errors, and preference) and driver distraction (NASA-TLX, average speed, the standard deviation of lane position, total glance duration, number of glances, average glance duration, and number of glances exceeding 1.6 s) metrics were measured as dependent variables. A sequence of one-way repeated measure analyses of variance (ANOVA) was performed to examine which type of TIMs can maximize in-vehicle navigation displays usability and minimize driver distraction. Results Generally, lateral driving performance deteriorated with the addition of the address inputting task, and the four alternative TIMs might fall into three levels: Speech is optimal, Qwerty followed, Shape-writing and Handwriting ranked last. Specifically, word-based speech remains performed best on all observed metrics for Chinese address names. There was an insignificant difference in text entry time and total glance duration among Qwerty, Shape-writing, and Handwriting. However, Shape-writing and Handwriting are not suitable for young drivers since the nature of uninterruptible causes excessive errors, more considerable lane position variation, longer average glance duration, and more glances exceeding 1.6 s. Conclusions This study provides valuable insights into young drivers’ interactions with four alternative TIMs. Importantly, it is beneficial to the automotive user interfaces design of in-vehicle navigation displays and other sub-functions of in-vehicle information systems (IVISs), such as music playback and text messaging, which positively mitigate driver distraction and prevent traffic injuries.

Retracted] Econometric Analysis of the Hot Research of Marxist Theoretical Journals Based on Knowledge Map

In recent years, the research on Marxist theory has received a lot of attention. Many studies use quantitative research methods such as statistical keywords and constructing knowledge map to carry out Marxist theoretical analysis. In this paper, by constructing a self‐expanding Chinese word separation and self‐expanding address data knowledge graph, matching Marxist Chinese addresses based on the full‐text indexing knowledge graph, incorporating a weighted pinyin full‐text search mechanism to improve the matching accuracy of misspelled addresses, constructing a multiple matching mechanism for Marxist addresses by combining an online geographic parsing interface, and performing semisupervised matching for a small number of difficult addresses, a complete system of Marxist address matching methods is formed. By studying and analyzing the basic orientation and attention to academic hotspots, we can gain insight into the characteristics, rules, and problems of academic hotspots in Marxist theory journals.

Chinese Address Recognition Method Based on Multi-Feature Fusion

A place name is a textual identification of a specific spatial location by people and is an important carrier of geographical information. The recognition of Chinese place names is of great importance in information retrieval and event extraction. The traditional approach is to transform the recognition of Chinese place names into a sequential annotation problem, with commonly used classification models such as support vector machines and conditional random fields. In this paper, Chinese address recognition is converted into a sequential annotation task, and a multi-feature fusion approach to Chinese address recognition is proposed. A deep learning network architecture model based on the fusion of word, word and address features is constructed to convert word and word tokens and their features into vector representations; finally, the sequential annotation of sentences is performed by CRF to achieve the recognition and extraction of address information. On the autonomously constructed dataset, the proposed method MFBL (Multi-Feature-BiLSTM) improves in accuracy by 4 to 10 percentage points compared to other methods, demonstrating that the MFBL model has better performance in the address recognition task.

Korean-Chinese Culture Viewed through the Address Terms - Focusing on the Generalization of Korean ‘님’ and Chinese ‘亲’ -

As a foreign language education, Korean education should not only impart Korean languages and culture. By comparing with other languages and cultures, Korean education will be more effective. Different countries have different languages and cultures. Language and culture are interdependent and influence each other. Language is an important carrier of culture. On the other hand, language and culture also restrict language. The address terms strongly reflect the social attributes and values of both sides of communication, and are closely related to the social culture, national psychology, political background and moral tradition of both sides the of communication. Korean ‘님’and Chinese ‘亲’ are simple terms composed of one syllable. They are widely used by Internet users in Internet space, but they are increasingly extended to daily life. Thinking through the generalization of these two words, the characteristics of Korean and Chinese social culture can be seen clearly. Therefore, through the comparison of the present usage of the Korean address term ‘님’ and Chinese address term‘亲’, this paper aims at understanding the characteristics of Korean and Chinese social culture.

Deep Contrast Learning Approach for Address Semantic Matching

Address is a structured description used to identify a specific place or point of interest, and it provides an effective way to locate people or objects. The standardization of Chinese place name and address occupies an important position in the construction of a smart city. Traditional address specification technology often adopts methods based on text similarity or rule bases, which cannot handle complex, missing, and redundant address information well. This paper transforms the task of address standardization into calculating the similarity of address pairs, and proposes a contrast learning address matching model based on the attention-Bi-LSTM-CNN network (ABLC). First of all, ABLC use the Trie syntax tree algorithm to extract Chinese address elements. Next, based on the basic idea of contrast learning, a hybrid neural network is applied to learn the semantic information in the address. Finally, Manhattan distance is calculated as the similarity of the two addresses. Experiments on the self-constructed dataset with data augmentation demonstrate that the proposed model has better stability and performance compared with other baselines.

Chinese address standardisation of POIs based on GRU and spatial correlation and applied in multi-source emergency events fusion

ABSTRACT A large number of users’ microblogs and various Points of Interest (POIs) of public service facilities in social media provide abundant data resources for the emergency events detection, fusion analysis and post-incident rescue. With the correlation analysis of these complex data resources based on the address information or location, people can instantly understand, rescue and make decisions for emergency events. This paper aims to propose an unsupervised method of multi-source POIs addresses segmentation and standardisation based on the Gated Recurrent Unit (GRU) neural network and spatial correlation. First, we use GRU neural network to automatically segment Chinese POIs addresses. Then, according to the spatial correlation between address elements, we can remove incorrect address elements, and construct a hierarchy address element map with the semantic relationship. Finally, the addresses of POIs or emergency events will be standardised by fuzzy matching, which uses the multi-source emergency events fusion of the first step. The propsed method is verified to a relatively high accuracy rate of address segment and standardisation, and it can be applied for the emergency event fusion and spatio-temporal analysis from multi-social media sites.