Contextual Proximity Research Articles

Study on the English Translation of Culture-Loaded Words in the Perspective of Relevance Theory in The Kingdom of Wine

It has gone through a period of development from the theory of relevance to the theory of relevance translation. The embodiment of relevance requires the constant efforts of translators. Cultural loaded words, as vocabulary with specific cultural colors, are not easy to find corresponding expressions in the target culture. This paper takes the English translation of Wine Country by Ge Haowen as a basis to study the translation of culturally loaded words from three dimensions: material culture, linguistic culture and social culture. It is found that Ge Haowen, the translator, greatly preserves the characteristics of the original text in the process of translation, and also endeavors to make the translation achieve contextual proximity, so as to convey the intention of the text to the readers.

WORD2VEC- И ГРАФОСЕМАНТИЧЕСКИЕ МОДЕЛИ ИСПОЛЬЗОВАНИЯ ЭМОТИКОНОВ И ЭМОДЗИ В ТЕКСТАХ ИНТЕРНЕТ-КОММУНИКАЦИИ

The article deals with emoticons and emoji use in textual computer-mediated communication. The research material is based on comments left by users of the social networking service VK. Using machine learning methods (Word2Vec), we build a model of contextual proximity of emoji use in the comments. This model reveals a closer contextual relationship of negative-tone emoji of different types with each other. The influence of personality’s psychological parameters on the use of icons of different tonality in similar communicative situations is considered as a hypothesis that explains the greater variability in the choice of negative-tone emoji when writing similar contexts. This hypothesis was tested by using a tagged corpus of texts left by users that had passed the psychological test to determine the BFI degree. We collected automatically and analysed the comments in the information system “Semograph”. It was found that variability in the use of negative-tone types of emoticons and emoji (namely the icons with the semantics of sadness, fear, apathy and bewilderment) in the same context is more characteristic of different psychological types of personality. Conversely, positive-toned icons (namely the icons with the semantics of joy, love, thankfulness and approval) are less variable in the same context.

Investigating Maps of Science Using Contextual Proximity of Citations Based on Deep Contextualized Word Representation

The citation intent extraction and classification has long been studied as it is a good measure of relevancy. Different approaches have classified the citations into different classes; including weak and strong, positive and negative, important and unimportant. Others have gone further from binary classification to multi-classes, including extension, use, background, or comparison. Researchers have utilized various elements of the information, including both meta and contents of the paper. The actual context of any referred article lies within the citation context where a paper is referred. Various attempts have been made to study the citation context to capture the citation intent, but very few have encoded the words to their contextual representations. For automated classification, we need to train deep learning models, which take the citation context as input and provides the reason for citing a paper. Deep neural models work on numeric data, and therefore, we must convert the text information to its numeric representation. Natural languages are much complex than computer languages. Computer languages have a pre-defined fixed syntax where each word has a unique meaning. In contrast, every word in natural language may have a different meaning and may well be understood by understanding the position, previous discussion, and neighboring words. The extra information provides the context of a word within a sentence. We have, therefore, used contextual word representation, which is trained through deep neural networks. Deep models require massive data for generalizing the model, however, the existing state-of-the-art datasets don’t provide much information for the training models to get generalized. Therefore, we have developed our own scholarly dataset, Citation Context Dataset with Intent (C2D-I), an extension of the C2D dataset. We used a transformers based model for capturing the contextual representation of words. Our proposed method outperformed the existing benchmark methods with F1 score of 89%.

Downsizing and Affective Organisational Commitment: A Contextual Proximity Perspective

This paper investigates the differential effect of various types of downsizing on employee commitment. While the literature on downsizing suggests that workforce reductions have a negative impact on employee commitment, knowledge of downsizing is limited by the undifferentiated ways in which various downsizing activities are usually approached. Moreover, there has been insufficient research looking at the different responses of survivors and victims of downsizing. This empirical study analyses the effects of four downsizing methods - layoffs, closure of units, divestment and voluntary redundancies- on employee commitment and explores differences on commitment outcomes depending on employees’ degree of exposure to a downsizing event. Using downsizing announcements between 2007-2010 and questionnaires, two distinct families of downsizing methods were identified. Layoffs and closure of units have a negative effect on commitment while voluntary redundancies and divestment have positive relationships. Employee proximity to the downsizing event accounts for significant differences on commitment outcomes. Studying downsizing in a differentiated way shows that assuming that all downsizing is detrimental to commitment is inadequate. I discuss the theoretical and practical implications of a more nuanced approach to downsizing.

Sensor-Based Proximity Detection in the Face of Active Adversaries

Contextual proximity detection (or, co-presence detection) is a promising approach to defend against relay attacks in many mobile authentication systems. We present a systematic assessment of co-presence detection in the presence of a context-manipulating attacker. First, we show that it is feasible to manipulate, consistently control and stabilize the readings of different acoustic and physical environment sensors (and even multiple sensors simultaneously) using low-cost, off-the-shelf equipment. Second, based on these capabilities, we show that an attacker who can manipulate the context gains a significant advantage in defeating context-based co-presence detection. For systems that use multiple sensors, we investigate two sensor fusion approaches based on machine learning techniques: features-fusion and decisions-fusion, and show that both are vulnerable to contextual attacks but the latter approach can be more resistant in some cases.

Application of machine learning to mapping primary causal factors in self reported safety narratives

A new method for analysis of text-based reports in accident coding is suggested. This approach utilizes latent semantic analysis to infer higher-order structures between documents and provide an unbiased metric to the narrative analysis process. Results from this study on a small sample of aviation safety narratives demonstrates an unsupervised categorization accuracy of 44% for primary-cause within the existing taxonomy. If provided with a large sample set, the indication is that a significant increase in accuracy is possible along with the possibility of recoding between data sets. Demonstrated is the ability of LSA to capture contextual proximity of a narrative.

Towards Automatic Text Adaptation in Russian

This article describes ways of using original texts in the National Russian Corpus and news texts to teach Russian as a foreign language. The two-year work of a scientific group from the Higher School of Economics (from Nizhny Novgorod and Moscow), called CorpLings was analyzed. Special attention was paid to the automatic adaptation of acute news texts, which was the basic principle of the research part of the project. We also describe ways of simplifying syntactical and morphological structures that may seem difficult for students at an elementary level. The stages used for lexical simplification are described in detail, such as the creation of an algorithm to find the most appropriate synonyms based on morphological rules, and an analysis of the statistical model of words’ contextual proximity. This article also addresses the difficulties faced by developers and the final results of our research.

Bone morphogenetic protein antagonist gremlin-1 regulates colon cancer progression.

Bone morphogenetic proteins (BMP) are phylogenetically conserved signaling molecules of the transforming growth factor-beta (TGF-beta) superfamily of proteins, involved in developmental and (patho)physiological processes, including cancer. BMP signaling has been regarded as tumor-suppressive in colorectal cancer (CRC) by reducing cancer cell proliferation and invasion, and by impairing epithelial-to-mesenchymal transition (EMT). Here, we mined existing proteomic repositories to explore the expression of BMPs in CRC. We found that the BMP antagonist gremlin-1 (GREM1) is secreted from heterotypic tumor-host cell interactions. We then sought to investigate whether GREM1 is contextually and mechanistically associated with EMT in CRC. Using immunohistochemistry, we showed that GREM1-expressing stromal cells harbor prominent features of myofibroblasts (i.e., cancer-associated fibroblasts), such as expression of α-smooth muscle actin and laminin-beta-1, and were in contextual proximity to invasion fronts with loss of the tight junction protein occludin and parallel nuclear accumulation of β-catenin, two prominent EMT hallmarks. Furthermore, in vitro assays demonstrated that GREM1-dependent suppression of BMP signaling results in EMT induction, characterized by cadherin switching (loss of E-cadherin-upregulation of N-cadherin) and overexpression of Snail. Collectively, our data support that GREM1 promotes the loss of cancer cell differentiation at the cancer invasion front, a mechanism that may facilitate tumor progression.

Distortion in Screening and Spatial Preferences

This article describes ways to use original texts in the National Russian Corpus as well as news texts for teaching Russian as a foreign language. Two-year work of a scientific group of Higher School of Economics (Nizhny Novgorod-Moscow), which is called CorpLings is analyzed. Special attention is paid to the basic principles of research part of the project – automatic adaptation of acute news texts. We also describe ways to simplify structures, which seem difficult for students at the elementary level, from the point of view of their syntactic and morphological structure. Stages, needed for lexical simplification are described in detail. Among them there are creation of an algorithm to find the most appropriate synonyms, based on morphological rules, and analysis of a statistical model of contextual proximity of words. The article also addresses the difficulties faced by developers and the final result of research.

Stress and the Contextual Proximity of Residential Factors

Research is mounting showing the link between the built environment, well-being and health. We explored three factors: crowding, housing satisfaction, and neighborhood satisfaction to determine their correlation with housing stress, and in turn correlation with overall level of stress. A Simple Random Sample (SRS) of blocks was generated and a total of 180 residents were sampled. Correlation and regression models were used in the data analysis. Although a direct relationship between the residential environment and the overall level of stress was not found, we did find an indirect connection via stress from the struggle for a better house.

Contextual paradigm for ad hoc retrieval of user-centric web data

The choice of the term-weighting technique and the combination of term weights during retrieval is an important aspect of the web information retrieval (WebIR) system. At their heart, most WebIR models utilise some form of term frequency-based term-weighting technique. The notion is that the more often a query term occurs in a document, the more likely it is that the document meets an information need. We examine an alternative. We propose a model, which assesses the presence of a term in a document not by looking at the actual occurrence of that term, but by a set of non-independent supporting terms, that is context. We expound contextual proximity model (CPM), a novel context-based paradigm, which when used as a part of the web retrieval process, will improve retrieval effectiveness in response to a given multi-term input query. This work is concerned with a non-iterative retrieval process, that is one without query refinement or relevance feedback.