Character Features Research Articles

음운론적인 면에서 살펴본 중세 국어 한자어의 성격

음운론적인 면에서 살펴본 중세 국어 한자어의 성격

PSYCHOLOGICAL PECULIARITIES, PRECONDITIONS AND WAYS OF STRESS STABILITY INCREASE IN ADOLESCENTS

Based on theoretical-methodological principles of cognition, the categorical-notion analysis of main terms (“stress”, “distress”, “stress stability”) that form the subject area of the considered problem is carried out in the paper. The study systematically generalizes main types of stress (informational, emotional, communicative) and its deployment phases (anxiety, resistance, exhaustion) and also outlines stress stability signs and its age formation peculiarities in adolescence. Stress stability is considered as a complicated integral feature of a person, mutually connected with a system of intellectual, cognitive, emotional, personal (motivational, character and other) features, providing his/her possibility to endure essential intellectual, physical, willing and emotional loads, keeping functioning efficiency at that. A level of stress stability is in the first turn conditioned by such factors as emotional stability, stress-resistance, frustration tolerance and so on. The study argues an idea that most effective ways of overcoming the destructive influence of stress of a person are: 1) objective elimination or lowering of the influence intensity of a cause/factor that conditioned the stress status; 2) internal adaptation to a stressor by transforming just an attitude to it in the subjective reality of a person (change of subjective interpretation frames); 3) acceptance of a problem as a given fact and conciliation with its irreversible results; 4) complex way as a combinatorics of the three previous variants with observance of their most optimal balance-proportion according to the psychosocial specificity of each concrete case and individual-typological features of a client. It has been empirically established, that the psychological profile of stress-stable persons is characterized by the high level of steadiness, emotional-willing self-control, self-confidence and low level of stress-sensitivity, personal and situation anxiety. It has been also explained in the research, that the most effective formation of stress stability takes place through the psychological mechanism of self-regulation that provides the synthesis of its structural components (personal, social and behavioral) and provides an adequate reaction on stress-generating factors.

Towards Chinese clinical named entity recognition by dynamic embedding using domain-specific knowledge

The task of electronic medical record named entity recognition (NER) refers to automatically identify all kinds of named entities in the medical record text. Chinese clinical NER remains a major challenge. One of the main reasons is that Chinese word segmentation will lead to the wrong downstream works. Besides, existing methods only use the information of the general field, not consider the knowledge from field of medicine. To address these issues, we propose a dynamic embedding method based on dynamic attention which combines features of both character and word in embedding layer. Domain knowledge is provided by word vector trained by domain dataset. In addition, spatial attention is added to enable the model to obtain more and more effective context encoding information. Finally, we conduct extensive experiments to demonstrate the effectiveness of our proposed algorithm. Experiments on CCKS2017 and Common dataset shows that the proposed method outperforms the baseline.

Chinese clinical named entity recognition with variant neural structures based on BERT methods.

Clinical Named Entity Recognition (CNER) is a critical task which aims to identify and classify clinical terms in electronic medical records. In recent years, deep neural networks have achieved significant success in CNER. However, these methods require high-quality and large-scale labeled clinical data, which is challenging and expensive to obtain, especially data on Chinese clinical records. To tackle the Chinese CNER task, we pre-train BERT model on the unlabeled Chinese clinical records, which can leverage the unlabeled domain-specific knowledge. Different layers such as Long Short-Term Memory (LSTM) and Conditional Random Field (CRF) are used to extract the text features and decode the predicted tags respectively. In addition, we propose a new strategy to incorporate dictionary features into the model. Radical features of Chinese characters are used to improve the model performance as well. To the best of our knowledge, our ensemble model outperforms the state of the art models which achieves 89.56% strict F1 score on the CCKS-2018 dataset and 91.60% F1 score on CCKS-2017 dataset.

Communicative Behavior via Gender Identity (Based on the English language “Love Stories”)

The focus of the paper concerns the specific features of characters’ communicative behavior via gender identity. This study deals with two types of correlation and interaction between real and fictional text-creating subjects: the gender identity of the author and the image created in the literary text.
 The research procedure is based on the discourse analysis. The study proves the influence of psychological and socio-cultural factors on speech organization. Due to the combination of content- and elements of intent-analysis the author’s pragmatic intentions in the communicative process are identified. The main findings of this paper are based on the theoretical basis of gender-specified communicative behavior study and the statistical data analysis.
 As a result of the research the specific features of gender communicative style are defined. Besides it is claimed, that taboo words differentiate basic features of male and female speech. The further analysis of non-literary vocabulary proves that gender stereotypes in female-written novels determine the choice of language means for the creation of an ideal male and female image: neglect of moral rules (domination of taboo words provoking conflict and rivalry) or compliance of communicative norms (a rare usage of derogatory vocabulary, communication aimed at cooperation and interaction). Gender differentiation of taboo lexicon in male-written novels argues the conventional stereotype of using taboos mainly by men. The research paper represents the differences in male and female speech, connected with the phenomenon of gender stylization. It proves that style imitation of stereotypical features in the opposite-sex speech occurs in two directions: female authors imitate the masculine communicative style of characters by the domination of taboo words, whereas male authors imitate the feminine communicative style by softening expressions of both-sex characters.
 Thus, in conclusion the study reveals imitating and identifying features of the author’s natural gender identity, such as domination of taboo words like “damn” and “hell” in opposite-sex communication in male-written novels unlike communicative behavior of male and female characters in the novels written by women.

Using Chinese Glyphs for Named Entity Recognition (Student Abstract)

Most Named Entity Recognition (NER) systems use additional features like part-of-speech (POS) tags, shallow parsing, gazetteers, etc. Adding these external features to NER systems have been shown to have a positive impact. However, creating gazetteers or taggers can take a lot of time and may require extensive data cleaning. In this work instead of using these traditional features we use lexicographic features of Chinese characters. Chinese characters are composed of graphical components called radicals and these components often have some semantic indicators. We propose CNN based models that incorporate this semantic information and use them for NER. Our models show an improvement over the baseline BERT-BiLSTM-CRF model. We present one of the first studies on Chinese OntoNotes v5.0 and show an improvement of + .64 F1 score over the baseline. We present a state-of-the-art (SOTA) F1 score of 71.81 on the Weibo dataset, show a competitive improvement of + 0.72 over baseline on the ResumeNER dataset, and a SOTA F1 score of 96.49 on the MSRA dataset.

Analysis of Facial Feature Design for 3D Animation Characters

The facial features of animation characters convey information about the characters to audiences and make the characters more believable. Using multidimensional scaling, this study analyzed the differences in the facial features of 332 three-dimensional characters from the top-100 grossing animation films at the international box office. We also explored whether top character designers follow implicit design principles that vary with the gender, age, and role of the character. The study results indicate that (1) face aspect ratio, (2) nose length, and (3) distance from mouth corner to face edge are three crucial facial features in character design. The results of this study offer concrete suggestions for character design to help designers make suitable adjustments to the facial features of characters.

ПСИХОЛОГІЧНІ ОСОБЛИВОСТІ ОСІБ, СХИЛЬНИХ ДО УНИКАННЯ В СКЛАДНИХ ЖИТТЄВИХ СИТУАЦІЯХ


 
 
 A comparison and analysis of temperament peculiarities and character features, self-regulation, cognitive and activity style, communicative, perceptive and іnteractive competence, emotional intelligence and barriers in communication, emotional states and direction, life values, coping strategies for persons with different level of inclination to avoidance in complex life situations are made. Interconnections between inclination to avoidance and rigidity, reactivity, assertiveness, social contacting in complex life situations, asocial inclination, moral normativity, activity modeling and evaluation, chaotic and controlled cognitive and activity style, self-regulation, communicative competence, social intuition, conflict behavior, stress-resilience, aggressiveness, sadness, gnostic and gloristic emotional direction are analyzed and interpreted.
 
 

Naples As Seen by Musicians: Intus Et Extra Cultura

The influence of Neapolitan culture on the work of G. Donizetti and F. Poulenc is the main issue and purpose of this paper. Peculiarities of the southern region of the Apennine Peninsula, distinguished by the beauty and poetry of nature, mild climate, richness of the folk song tradition that arose at the crossroads of various ethnic groups and languages, inspired the creative imagination of artists, musicians, and writers from different countries. The artistic heritage contains many examples of works dedicated to Italy and Naples, in particular. In contrast to the processes of mixing heterogeneous elements and continuous renewal, universalism remains a permanent feature of Italian self-identity. Having been formed in antiquity, it was actualized in the era of humanism in a number of creative projects. One of them is the spread of the essentially regional genre of villanella (or Canzone alla napolitana) throughout the entire European music area in the 16th century. The cycle Nuits d’ete a Pausillipe by G. Donizetti becomes a kind of response to the processes of the past, demonstrating the sustainability of the Neapolitan song traditions in view of romantic vocal music and romanza genre. The composer’s deep comprehension of the features of national melos and character, with its characteristic poetry of experiencing nature and emotionality, determined the psychological mood of the composition. The hymnal praise of love and joys of life, the exaltation of the lyrical expression testify to the high degree of G. Donizetti’s immersion into the atmosphere of the city, which became his second small homeland. A different view of Naples appears in the piano suite Napoli by F. Poulenc. It is remarkable for the picturesque language and the immediacy of emotional expression, determined by deep interpenetration of the Italian and French cultures. The position of an onlooker is seen in composer’s praise of natural beauties of the city, liveliness and energy of its population, theatricality of events. The study makes it clear that the differences in the individual perception of Naples by G. Donizetti and F. Poulenc, who represent two types of Neapolitan images retranslation in music, are predetermined. The involvement of G. Donizetti tradition, acquired at the genetic level, is expressed in the choice of vocal type of expression, a special sense of musical and verbal intonation. F. Poulenc, representing a different national culture, focuses on the external representativeness of Neapolitan life, capturing it in a series of instrumental sketches.

الحلم بين الأمثال والحكم في الشعر العربي

ABSTRACT The proverbs and maxims, being the summary of Arabs experiences, gist of their wits and fruit of their wisdom, hold an important place among the Arabs. The proverbs and maxims depict, like Arabic poetry, the nature of Arabs life and express the manifestations of their moral values. Proverbs were in common use in their daily conversation in pre Islamic era and were transferred to Islamic period as these were used by the later generations in their communications and writing for centuries. Multiple human virtues, moral values and good deeds were featured in Arabic proverbs and maxims as well as in poetry, one of greatest in dignity and best in ranking amongst these, is the virtue of forbearance (hilm). Forbearance can be called the mother of all virtues because no other moral feature of human character like suppression of anger, pardon, compassion and mercy, can be fully actualized in the absence of patience and tolerance (hilm). So the virtue of (hilm) became the necessary element of the leadership of a tribe and compulsory condition for it. So the Arabic proverbs and maxims spur on the virtue of hilm and encourage to suppress the insurgency, arrogance and ignorance. This research work highlights the phenomenon of hilm in the light of Arabic proverbs and maxims that has been used by Arab poets in their poetry.

Translation Strategies for Speech Features of Characters in Fictional Texts (a Case Study of Speech of Upper Class Representatives)

Introduction. The paper analyses different means used for conveying the phonetic features of the speech of upper class representatives in English fiction, in particular in the works of Charles Dickens and John Galsworthy. The author focuses on functions of phonographic stylisation of the aristocratic speech and discusses the techniques and strategies for rendering this phenomenon into the Russian language.Methodology and sources. When selecting examples for analysis, we used the method of corpus analysis, which made it possible to establish the frequency of using a particular phonographic means, as well as the continuous sampling method used to analyse a specific technique of phonographic stylisation. Methods of linguistic, sociolinguistic and translation analysis were used to analyse specific examples of phonographic stylisation and their translations into the Russian language. The novels by Charles Dickens and John Galsworthy were used to carry out the research.Results and discussion. When translating the speech of upper class representatives into the Russian language, phonographic means can be rendered only in those cases when they perform a parody function. Otherwise, phonographic means are not conveyed but might be substituted or compensated by lexical and syntactical means, which can be considered a natural consequence because of the difference in the traditions of representing the speech of aristocrats in English and Russian literary traditions.Conclusion. Phonographic means and phonographic stylisation are the most important component of the artistic representation of character’s speech in general, and the speech of upper class representatives in particular. Consequently, this phenomenon is a multifaceted linguistic problem in the theory of translation. This study outlines the main directions of analysis of phonographic stylisation while rendering the speech of aristocrats, but this problem is far from being solved. Other cases of using phonographic stylisation may be the subject of further research into contrasting and translation aspects.

«А ці зросли богатирі на київському полі: „Канева”, Сабо і „Базіль”, „Лобан”, Турянський, „Срібло”». (Прізвиська українських футболістів совєтського періоду)

The nicknames, which come into being as supplementing the person’s official name, are informal anthroponyms by which the environment individualizes or characterizes a person. Marking people with nicknames is a characteristic feature of the football discourse in the whole world. In Ukrainian linguistics, informal person’s names of football players have not been the object of study yet, that is why their analysis is still topical.
 The aim of the article is to analyze informal names of football players who played in the Ukrainian teams in the 20-80s of the ХХth century. It enables scholars to trace the tendencies in creating informal personal names and also can contribute to drawing social-linguistic conclusions about anthroponym usage in sports discourse.The basic method of the study is discursive analysis of the facts taken from printed sources (first of all from the Ukrainian sports media of that time) and their interpretation. Among diverse ways of creating football players’ nicknames in the Soviet period, the most effective were various transformations of players’ surnames, most often by truncating last names. A lot of nicknames were created by cutting football players’ first names or surnames and also by adding to them suffixes with diminutive semantics. Many informal names are the diminutive forms of the players’ names. Among this type of nicknames, there are those created according to the Ukrainian patterns and also in accordance with the Polish, Hungarian, and Russian patterns of creating diminutive forms of names. During that period, the patronymic names were used as nicknames for the first time. There were a lot of informal names which were created metaphorically on the basis of people’s appearance or character features, or both at the same time.
 Analysis of the Ukrainian football players’ nicknames in 20-80s shows that the motivational basis for those anthroponyms creation is the player's surname, its diminutive form, and features. The ways of creating those names involve metaphors and parts of surnames. The prospects of the studies are tracing dynamics of creating football players’ nicknames in various periods of the Ukrainian football development.

Isarn Dharma Word Segmentation Using a Statistical Approach with Named Entity Recognition

In this study, we developed an Isarn Dharma word segmentation system. We mainly focused on solving the word ambiguity and unknown word problems in unsegmented Isarn Dharma text. Ambiguous Isarn Dharma words occur frequently in word construction due to the writing style without tone markers. Thus, words can be interpreted as having different tones and meanings in the same writing text. To overcome these problems, we developed an Isarn Dharma character cluster–(IDCC) based statistical model and affixation and integrated it with the named entity recognition method (IDCC-C-based statistical model and affixation with named entity recognition (NER)). This method integrates the IDCC-based and character-based statistical models to distinguish the word boundaries. The IDCC-based statistical model utilizes the IDCC feature to disambiguate any ambiguous words. The unknown words are handled using the character-based statistical model, based on the character features. In addition, linguistic knowledge is employed to detect the boundaries of a new word based on the construction morphology and NER. In evaluations, we compared the proposed method with various word segmentation methods. The experimental results showed that the proposed method performed slightly better than the other methods when the corpus size increased. Using the test set, the proposed method obtained the best F-measure of 92.19, an F-measure that was better than the IDCC longest matching grouping at 2.85.

Resonant transport in Kekulé-distorted graphene nanoribbons

The formation of a superlattice in graphene can serve as a way to modify its electronic band structure and thus to engineer its electronic transport properties. Recent experiments have discovered a Kekulé bond ordering in graphene deposited on top of a copper substrate, leading to the breaking of the valley degeneracy while preserving the highly desirable feature of linearity and gapless character of its band dispersion. In this paper, we study the effects of a Kekulé distortion in zigzag graphene nanoribbons in both the subband spectrum and on its electronic transport properties. We extend our study to also investigate the electronic conductance in graphene nanoribbons composed of sequentially ordered ν=±1 Kek-Y superlattices. We find interesting resonances in the conductance response emerging in the otherwise energy gap regions, which scales with the number of Kek-Y interfaces minus one. Such features resemble the physics of resonant tunneling behavior observed in semiconductor heterostructures. Our findings provide a possible way to measure the strength of the Kekulé parameter in graphene nanoribbons.

정책 활동과 인물 특성에 대한 인식이 정치인 이미지와 선호에 미치는 영향

정책 활동과 인물 특성에 대한 인식이 정치인 이미지와 선호에 미치는 영향

Актуализация образа военного врага в политическом дискурсе М. Тэтчер

The article examines lexico-syntactic means, which serve to actualize the image of Britain’s armed enemy - Argentina - in M. Thatcher’s speech. Originality of the study is determined by the fact that this problem has not been previously investigated in studies on the British political discourse. The paper identifies close interrelation of tactics and strategies used for explicit or implicit representation of the enemy image. The conducted analysis has allowed the researcher to describe the image of a vanquished enemy and the winner image and to ascertain relation between the identified means of the enemy image actualization and features of the British national character.

Representation learning in a deep network for license plate recognition

The goal of license plate recognition (LPR) is to read the license plate characters. Due to image degradation, there are many difficulties in the way of achieving this goal. In this paper, the proposed method recognizes the license plate characters without employing the traditional segmentation and binarization techniques. This method uses a deep learning algorithm and tries to achieve better learning experience by engaging a multi-task learning algorithm based on sharing features. The features of license plate characters are extracted by a deep encoder-decoder network, and transferred to 8 parallel classifiers for recognition. To evaluate the current work, a database of 11,000 license plate images, collected from a currently working surveillance system installed on a dual carriageway, is employed. The proposed method achieved the correct character recognition rate of 96% for 4000 test images that is acceptable in comparison to the competing methods.

Named-Entity Recognition in Sports Field Based on a Character-Level Graph Convolutional Network

Traditional methods for identifying naming ignore the correlation between named entities and lose hierarchical structural information between the named entities in a given text. Although traditional named-entity methods are effective for conventional datasets that have simple structures, they are not as effective for sports texts. This paper proposes a Chinese sports text named-entity recognition method based on a character graph convolutional neural network (Char GCN) with a self-attention mechanism model. In this method, each Chinese character in the sports text is regarded as a node. The edge between the nodes is constructed using a similar character position and the character feature of the named-entity in the sports text. The internal structural information of the entity is extracted using a character map convolutional neural network. The hierarchical semantic information of the sports text is captured by the self-attention model to enhance the relationship between the named entities and capture the relevance and dependency between the characters. The conditional random fields classification function can accurately identify the named entities in the Chinese sports text. The results conducted on four datasets demonstrate that the proposed method improves the F-Score values significantly to 92.51%, 91.91%, 93.98%, and 95.01%, respectively, in comparison to the traditional naming methods.

Stroke Sequence-Dependent Deep Convolutional Neural Network for Online Handwritten Chinese Character Recognition.

We propose a novel model, called stroke sequence-dependent deep convolutional neural network (SSDCNN), which uses the stroke sequence information and eight-directional features of Chinese characters for online handwritten Chinese character recognition (OLHCCR). SSDCNN learns the representation of OLHCCs by incorporating the natural sequence information of the strokes. Furthermore, it naturally incorporates the eight-directional features. First, SSDCNN inputs the stroke sequence and transforms it into stacks of feature maps following the writing order of the strokes. Second, the fixed-length, stroke sequence-dependent representations of OLHCC are derived through convolutional, residual, and max-pooling operations. Third, the stroke sequence-dependent representation is combined with the eight-directional features via a number of fully connected neural network layers. Finally, the Chinese characters are recognized using a softmax classifier. The SSDCNN is trained in two stages: 1) the whole architecture is pretrained using the training data until the performance converges to an acceptable degree. 2) The stroke sequence-dependent representation is combined with the eight-directional features by a fully connected neural network and a softmax layer for further training. The model was experimentally evaluated on the OLHCCR competition tasks of International Conference on Document Analysis and Recognition (ICDAR) 2013. The recognition error was a maximum 58.28% lower in SSDCNN than in a model using the eight-directional features alone (5.13% versus 2.14%). Owing to its high accuracy (97.86%), the proposed SSDCNN reduced the recognition error by approximately 18.0% as compared with that of the winning system in the ICDAR 2013 competition. SSDCNN integrated with an adaptation mechanism, called the SSDCNN+Adapt model, and reached a new state-of-the-art (SOTA) standard with an accuracy of 97.94%. The SSDCNN exploits the stroke sequence information to learn high-quality OLHCC representations. Moreover, the learned representation and the classical eight-directional features complement each other within the SSDCNN architecture.

A Robust License Plate Recognition Model Based on Bi-LSTM

License plate detection and recognition are still important and challenging tasks in natural scenes. At present, most methods have favorable effect on license plate recognition under restrictive conditions, and most of such license plates are shot under good angle and light conditions. However, for license plates under non-restrictive conditions, such as dark, bright, rotated conditions etc. from the Chinese City Parking Dataset (CCPD), the performance of some methods of license plate recognition will be significantly reduced. In order to improve the accuracy of license plate recognition under unrestricted conditions, a robust license plate recognition model is proposed in this paper, which mainly includes license plate feature extraction, license plate character localization, and feature extraction of characters. First of all, the model can activate the regional features of characters and fully extract the character features of license plates. Then locate each license plate character through Bi-LSTM combined with the context location information of license plates. Finally, 1D-Attention is adopted to enhance useful character features after Bi-LSTM positioning, and reduce useless character features to realize effective acquisition of character features of license plates. A large number of experimental results demonstrate that the proposed algorithm has good performance under unrestricted conditions, which proves the effectiveness and robustness of the model. In CCPD-Base, CCPD-DB, CCPD-FN, CCPD-Tilt, CCPD-Weather, CCPD-Challenge and other sub-datasets, the recognition rates reach 99.3%, 98.5%, 98.6%, 96.4%, 99.3% and 86.6% respectively.