Modern Technologies and Hermeneutical Analysis of Poetic Text

Chemezova Ekaterina Rudol'fovna ORCID: 0000-0002-6229-2243 PhD in Philology Associate professor, Department of Foreign Philology and Teaching Technique, Academy of Pedagogy and Humanities (branch) of V. I. Vernadsky Crimean Federal University in Yalta 298600, Russia, Republic of Crimea, village. Massandra, Stakhanovskaya str., 11, Department of Philology and Teaching Methods chemezova4@gmail.com Other publications by this author

         Introduction. To date, literary disciplines continue to use classical scientific methods. Despite the abundance of innovative technologies, the analysis of a text of any nature depends on the skill of a literary critic and his desire for research.

        The purpose of this article is to form a pedagogical technology of literary methods of analysis of poetic text and modern artificial intelligence technologies. Innovative opportunities in connection with the high-quality technical equipment of most higher educational institutions make it possible to use such pedagogical technologies both independently and in practical classes when studying philological disciplines, thanks to the adaptability of the proposed technology.

         Presentation of the main material of the article. As V. Zaitsev notes, pedagogical technology is "a strictly scientific design and accurate reproduction of pedagogical actions that guarantee success. Since the pedagogical process is based on a certain system of principles, pedagogical technology can be considered as a set of external and internal actions aimed at the consistent implementation of these principles in their objective interrelation, where the personality of the teacher is fully manifested" ^[1]. The achievement of educational goals in pedagogical technology is primarily due to the possibility of flexible adaptation for more correct planning and the inclusion of technology in any form of education. Thus, pedagogical technology as a structured system can be optimized for any material. both in full-time and distance learning formats.

Philological education has the potential to vary the means and methods in order to form the general and professional competencies of a specialist. Due to the high adaptability of any logical process in the humanities, it is modern AI technology (artificial intelligence) that offers a wide base of verbal material, which, thanks to the development of professional skills of a philologist, can be made one of the sources for improving text analysis.

AI is a branch of computer science that develops algorithms and systems capable of performing tasks that normally require human intelligence, such as understanding natural language, pattern recognition, and decision making. In recent years, there has been a growing interest in the use of AI in the field of literature, including the creation of poetry.

The importance of AI, its application in the educational environment and its introduction into any scientific field is confirmed by the Decree of the President of the Russian Federation "On the development of artificial intelligence in the Russian Federation" dated 10.10.2019, No. 490. It notes that AI as "a set of technological solutions allows you to simulate human cognitive functions, including self-education and the search for solutions without a predetermined algorithm, obtaining results comparable to, at least, the results of human intellectual activity when performing specific tasks. The complex of technological solutions includes information and communication infrastructure, software (including those using machine learning methods), processes and services for data processing and solution search. The use of AI (artificial intelligence) technologies in the social sphere contributes to the creation of conditions for improving the standard of living of the population, including by improving the quality of educational services (including adapting the educational process to the needs of students and the needs of the labor market, a systematic analysis of learning performance indicators to optimize vocational guidance and early identification of children with outstanding abilities, automation of knowledge quality assessment and analysis of information on learning outcomes)" ^[5]. It follows that the use of AI in philological education is relevant.

A common example of the use of AI (artificial intelligence) is its use in multimedia. Text interactions between artificial intelligence and a philologist specialist were previously carried out only in the process of reproducing educational fragments by a voice assistant, especially in the niche of inclusive education, and machine translation training. The development of AI (artificial intelligence) is developing rapidly, and today we can see how "machine" translations are presented as a holistically formed text, consistent and full of appropriately applied artistic means.

         As M. Eia notes in his research, "rapid progress has led to significant changes in education, opening up new opportunities and challenges for teaching and learning, <...> which is facilitated by the use of AI methods. In any field of education, a strategy should be developed that defines goals for the implementation of AI and provides an algorithm for managing the results obtained by the teacher and students" ^[9]. Some researchers also emphasize that currently the level of AI progress obtained is less than human intelligence and always "requires additional correction and analysis when processing the received content" ^[8].

Currently, AI is "able" to create texts that are adaptable by keywords or by a keyword phrase. The most frequently used resources with AI (artificial intelligence) working with text are the following:

1) Balaboba – with the help of neural networks of the YaLM family, you can continue texts on any topic, maintaining coherence and a given style. It is rather entertaining in nature and difficult to adapt to a scientific text.

2) ruGPT-3 XL – the ruGPT-3 XL model contains 1.3 billion parameters and is able to continue texts in Russian and English, a program code that can be gradually trained and adapted to a scientific style.

3) Anyword resource creates a generated AI text that can be customized to your target audience. Capable of "learning", can be oriented to a scientific text in English.

4) CopyMonkey is a startup that creates text on the specified topic and keywords in Russian and English. Initially adapted for SEO and ecommerce, it quickly and efficiently adapts to scientific and artistic texts.

5) Inkforall is a resource that targets media content and informational texts. It is adaptive to students' work in English and Chinese and is able to analyze any academic text.

6) Gerwin.AI – a service that adapts texts by rewriting and selecting text on the subject set by the user in Russian and English.

This is just a small researched and tested list of AI resources. Despite the abundance of such services, human interaction with them as a recipient and corrector is not excluded. Speaking about the role of such resources in pedagogical science, it is worth noting the need for a comprehensive application of methods of working with AI and classical methods. One of these methods, which we will consider, is the hermeneutical analysis of the text.

Literary hermeneutics is a scientific method that allows you to interpret phenomena in a literary text. Hermeneutical text analysis becomes universal due to its principles – integrity, emotionality, contextuality, variability, dialogicality ^[2]. The uniqueness of hermeneutical analysis lies in the fact that it can be based on archetypal, general cultural, national and subjective knowledge of the recipient. Hermeneutical analysis always presupposes multilevelness and a research search of this nature can be continuous, given the involvement of more primary sources and research of various directions in the interpretation of the text. Despite this, the primary (carried out at the first reading of the text) and continuous analysis of the text in the educational process can be carried out when referring to the cultural dictionary of symbols and subjective knowledge.

The task of such a variant of literary analysis a priori solves the problem of the "hermeneutic circle": the difficulty of moving to a common meaning based on the study of text fragments and the reverse movement from the general meaning defined by the researcher to the interpretation of details, which can be systematized using AI. Thanks to hermeneutical analysis, we can search for semantic detonation in the systematization of all available connotations and choose the most relevant ones relative to the general semantic load when analyzing the text directly together with the students.

The use of AI in the process of hermeneutical analysis in teaching philological disciplines may be appropriate as one of the elements of pedagogical technology.

Chinese poetry is a rich and diverse literary tradition that has a long history dating back to the early Chinese dynasties. The texts of the early dynasties are characterized by the use of images, metaphors and allusions to convey complex emotions and ideas. Traditional forms of Chinese poetry such as shi, qi, and qu have strict structural rules regarding the number of characters per line and the use of rhyme..

Researchers have developed artificial intelligence models that can not only adapt existing texts to the modern user, but also generate Chinese poetry using various methods such as statistical machine translation, neural networks and systems based on the rules of the Chinese poetic language.

These models are trained on large datasets of existing Chinese poetry and can generate new poems and their interpretations that adhere to the traditional forms and style of Chinese poetry. However, the quality and creativity of the poetry created is still a matter of debate, and the potential impact of AI on the field of Chinese poetry remains an open question.

The study of Chinese poetry using AI is a new field that combines the traditional study of Chinese literature with advanced artificial intelligence techniques. The main goal of this field is to develop AI models that can understand, generate and analyze Chinese poetry. This research can be divided into three main areas:

1. Understanding Poetry: This field aims to develop AI models that can understand the meaning, structure, and style of Chinese poetry. Researchers use techniques such as natural language processing and machine learning to analyze the text of Chinese poetry and extract semantic and syntactic characteristics. These models can be used for tasks such as adapting poems to modern Chinese, translating and defining the author's style.

2. Poetry generation: This field aims to develop AI models that can generate new Chinese poetry adhering to traditional forms and style. Researchers use techniques such as neural networks, systems based on Chinese language rules, and statistical machine translation to train models on large datasets of existing Chinese poetry. The generated poems are then evaluated by human experts for creativity and quality, which can help in building new imaginative systems of historical Chinese poetry.

3. Analysis of poetry: This field aims to develop AI models that can analyze the literary and cultural significance of Chinese poetry. Researchers use techniques such as tone and form analysis of versification, thematic modeling, and online network analysis to explore the themes, style, and historical context of Chinese poetry. These models can be used for tasks such as literary criticism and the preservation of cultural heritage.

The study of Chinese poetry using AI is an interdisciplinary field involving information technology developers, literary scholars, and linguists. It can provide a new perspective on understanding and evaluating Chinese poetry, but it also raises important ethical and cultural questions about the role of AI in the field of literature.

Based on theoretical and practical knowledge of literary hermeneutics and modern AI resources, we can offer a pedagogical technology for continuous analysis of a poetic text in the discipline "History of Chinese Literature" using the example of Du Fu's poem " ()" translated by V. Alekseev:

«

» ^[4] –

"The stream is still circling, the wind is in the pines all the time.

Here the gray mouse in ancient times escaped the potsherd.

And I do not know: which lord's hall

I stayed standing there, under the sheer wall."^[3]

Hermeneutical analysis is a method of interpreting texts that aims to understand the meaning and significance of a work by studying its historical, cultural and literary contexts. For Du Fu's poems, hermeneutic analysis will include the study of the historical and cultural heritage of Tang Dynasty China, as well as the literary conventions and methods used by Du Fu.

To conduct a hermeneutic analysis of Du Fu's poems, one must begin by studying the historical and cultural traditions of the Tang Dynasty, including the political, social and economic conditions of that time. This would serve as a basis for understanding the themes and motifs present in Du Fu's poetry, such as the emphasis on the transience of life and the struggles of ordinary people.

The next step is to explore Du Fu's literary techniques, such as his use of images, metaphors, and allusions, as well as his commitment to traditional forms of poetry such as shi and qi. This would give an idea of how Du Fu used literary conventions to convey meaning and express his ideas. Finally, one can read and interpret Du Fu's poems in the light of historical and cultural context and literary techniques, seeking to understand the general meaning and significance of poetry. This interpretation will be supported by careful reading and analysis of specific lines, phrases and images in poetry.

Studying Du Fu's poetry through hermeneutical analysis can provide a deeper understanding of his work and its place in the literary and cultural context of the Tang Dynasty. It is an interdisciplinary approach that includes the study of literature, history and culture.

Taking into account the system of working with poetic text, we propose the following stages of applying pedagogical technology of working with AI:

1. The introductory stage. This stage involves the recipients reading the studied material for further detailed analysis. A fragment of Du Fu's poem is considered as an example.

2. Identification of common points of cultural image. Such points can always be deciphered and interpreted by an untrained reader. Having studied the text of the work line by line in the original language and in the translation by V. Alekseev, students identify key images.

3. Reconstruction of archetypal images. Such images can attract us to the element of which we are the bearers, regardless of national and cultural affiliation. The analysis of archetypal images attracts students to sources close to folklore and mythology, stimulates the application of previously acquired knowledge. Each previously identified image is processed using the knowledge already acquired and a system of images is compiled.

For example, according to the dictionary of symbols, the mouse (?) "symbolizes timidity. <...> In popular beliefs, mice are souls that ran out of the mouths of the dead (red – if the deceased were virtuous, black – if sinful), like pigeons, which, it was claimed, fly out of the mouths of saints when their souls leave dead bodies. <...> mice are used for predictions, as they are believed to know the secrets of the afterlife" ^[4]. We also analyze the remaining selected symbol images using additional sources.

4. Application of AI analytical tools. Let's turn to the CopyMonkey resource ^[6], conducting a line-by-line analysis of Du Fu's poem. Entering into the subject field "The stream is circling, the wind is always in the pines", we get the answer: "It has long been believed that the wind is a spirit that circles the world. And it is always present in the pines, because it is a tree of power and wisdom. Although pine trees can grow anywhere, they will always have the spirit of the wind in them." Next, we continue to perform line-by-line analysis via CopyMonkey:

"Here a gray mouse escaped in an ancient potsherd" ^[2] – "An ancient potsherd in the place of a gray mouse. Here she escaped from cats, from bison and from other enemies. Every day she came out of her burrow and spent time in the shade of the trees. At night, she would go out into the clearing and look for food. One day a mouse saw a cat. The cat was big and strong, and she ran up to the mouse. The mouse tried to run away, but the cat quickly grabbed it. The mouse tried to cry, but the cat was too fast. The mouse tried to run into its burrow, but the cat grabbed it and devoured it. The mouse was dead. The ancient shard remained in place of the gray mouse. She no longer came out of her burrow, and her body was covered with mold. The shard remained in place until it was covered with dust."^[6]

"And I do not know: the hall of which lord / remained standing there, under the sheer wall" ^[2] – "A lot of time has passed since then, but the hall remains unchanged. Who knows when it was built? It must have been ancient times. The lord who owned this hall must have been a very powerful man. Now the hall is empty, and no one knows when he will take life again."^[6]

The text obtained through CopyMonkey assumes further processing by students. So, you should pay attention to those images that are presented in the interpretation of AI.

5. Carrying out a paradigm between the images obtained during the initial reading by students and the images highlighted after analyzing the text through the AI resource CopyMonkey. Students mark their connotational levels in order to collect a complete picture of the artistic world of the analyzed text, explore new meanings proposed by AI, and draw parallels in reading between each fragment.

6. Final analysis. Students systematize the data obtained by grouping the layered meanings of images according to the principle of deduction, forming a common interpretative picture in the poem Du Fu. An example of such a final analysis can be the statements: "In Du Fu's poem " ()" the motive of death is determined, which is revealed in each analyzed fragment. The images highlighted in the course of interpretation, in hermeneutical analysis, turn us to the image of death, oblivion, and frailty. The trees, which are shaken by the wind, imitate a whisper that turns us to the other world, which is inaccessible to a living person. At the same time, the trees themselves can be cut down by a person or felled by the elements, which are also embedded in the image of the wind. The image of a mouse, so popular and archetypal, is the image of a gatherer who accumulates grain for a cold winter. The proposed development of an AI mouse image describes the death of a mouse that has not stored anything for the winter. At the same time, this image is antinomian: harvesting grains during the harvest season turns us to collecting souls during the allegorical Harvest – the Last Judgment, but at the same time, the mouse is only a perishable creature hiding from the slightest rustle. The author draws us to the majestic hall behind the sheer wall following the description of the image of the mouse, using the artistic trope of contrasting the big (ruler) and the small (mouse), while equalizing them in the face of the supreme Court, Death." An example of such a final analysis of a fragment of a Du Fu poem is a representation of the applied pedagogical technology of hermeneutical analysis of a poetic text, which is being improved through the use of AI.

One of the ways to conduct hermeneutical analysis of Du Fu poetry using AI is to use natural language processing (NLP) techniques to analyze the text. NLP algorithms can be used to identify patterns and themes in poetry, such as the adaptation of images, metaphors, and allusions. This can give an idea of the deep, general cultural meaning of the symbols of Chinese poetry, the analysis of which can be improved thanks to AI.

Conclusions. Thus, AI can be a valuable tool in the hermeneutical analysis of poetic texts such as Du Fu poetry. Using techniques such as artistic speech processing and continuous machine learning, AI can help identify patterns and themes in poetry, providing a deeper understanding of literary conventions and methods used by the poet. In addition, AI can help analyze the historical and cultural context of poetry by providing computational support for tasks such as data analysis and text analysis. However, it is important to note that the use of AI in hermeneutical analysis should not replace traditional methods of interpretation, but rather complement and support them. The final interpretation should always be made by a researcher who can take into account the broader context and nuances of the work and the culture to which it belongs. The developed pedagogical technology of working with AI in the analysis of poetic text can be adapted both for classroom practical classes and for independent work of students. Our selected material (a fragment of a poem by the Chinese poet of the Tang Du Fu era) and an AI-based resource, CopyMonkey, can be replaced by other resources and text of any nature. This kind of pedagogical technology can become a new milestone in the teaching methodology through the interpretative tools of literary hermeneutics.