# Intertextuality between French Literature Creation and Literature Translation Based on Feature Extraction and Gram Matrix

Authors: Ting Li
Journal: Wireless Communications and Mobile Computing
 (2022)

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## Abstract

In the 1980s, there was a cultural turn in translation studies. Since then, translation studies are no longer confined to the study of specific texts, but examine the texts in the context of culture and history, and study the production process of texts and the relationship between texts and related factors such as culture and history. When literary works are transplanted from one language to another, it is like moving plants and animals from one place to another. They must “adapt” and grow like individuals or nations, and they can only survive if they adapt to the new environment and change. Intertextuality is one of the important terms in contemporary literary criticism. Intertextuality is one of the seven textual features of a text (formal adaptation, semantic adaptation, intentionality, receptivity, informality, situational, and intertextuality). Gram incidence matrix is designed to count and merge feature words using feature extraction algorithm for text processing and feature extraction, so that feature words of different lengths can be extracted using fixed-length algorithm. A poststructuralist, Roland Barthes, deconstructs the traditional concept of text and extends it to the social and cultural context. The so-called intertextuality of literary creation and literary translation also incorporates cultural connotation and knowledge structure into the relationship between texts. Literary system is a system, social culture is a larger system, and literary system is a constituent factor of the social cultural system, according to the theory of multiple systems; literary creation and literary translation are factors in the literary system. The main focus of intertextuality theory is text comprehension. Bloom’s intertextuality theory, on the other hand, expounds on text creation. On the basis of a feature extraction algorithm, this paper investigates the types and techniques of intertextuality between creative text and translated text using a combination of objective description and theoretical analysis.


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## Body

## 1. Introduction

Chinese literary translation thought has experienced from Yan Fu’s “faithfulness, expressiveness and elegance” to Fu Lei’s “spirit likeness” to Qian Zhongshu’s “transformation”. Its commonality lies in that literary translation should not only be faithful to the original text but also rise to the level of artistic creation [1] The translation circles all over the world have talked about the relationship between literary translation and literary creation and think that the work done by translators is somewhat similar to that of literary creation [2]. However, that is all. Few translators have explored it in depth, let alone carried out detailed research work from different angles. Intertextuality, or intertextuality, also known as “intertextuality”, “intertextuality,” or “intertextuality”, is the text theory concept of western structuralism and poststructuralism [3]. In literary translation, whether the translator can be “faithful, expressive, and elegant,” besides his own language ability, also involves the temperament of the translator, that is to say, whether the translator can go deep into the text and blend with the thoughts, feelings, atmosphere, mood, and so on of the original author, which is related to the translator’s cognitive context differences [4]. Due to the explanatory power and theoretical tension of intertextuality openness, translation studies are interdisciplinary, cross-cultural and cross-lingual behaviors [5]. Therefore, intertextuality theory has been widely used in the field of translation studies and has made great progress in recent years. From the initial simple text written communication to the present application to all aspects of society, a large amount of text information is spread in computer readable form. In view of the great influence and fruitful achievements of text translation in the global scope, the study of text translation itself has been paid more and more attention by people [6].Text feature extraction extracts the features of information and expresses them in a unified way, which can effectively reduce the dimension of text vector space, simplify calculation, and prevent over fitting. It is the induction process of text commonness and rules [7]. The literary theory of creation is a great reference for the creative essence of literary translation, because literary translation is different from other kinds of translation, which requires that the translated works must also be literary works [8], and can convey all the author’s intentions, including thoughts, emotions, and language means [9]. While using this information, we must also fully consider the large number of synonymous and polysemy phenomena in language, as well as the tendency of praise and criticism, which often play a key role in feature extraction [10]. The feature selection algorithm generally constructs an evaluation function, evaluates and scores each feature independently, then sorts all features according to the size of the evaluation score, and then selects the best features with high evaluation score and a predetermined number as the feature subset [11]. Therefore, the text presents a multidirectional and multiangle intertextual relationship. There are not only the intertextual transformation of language symbols within the text but also the intertextual effect between the text and many external social, historical, cultural, and other texts, which is manifested in the intertextual reference formed between concrete texts, between abstract texts and concrete texts, and between abstract texts and abstract texts [12].Feature extraction is a transformation from the original feature space to a new low-dimensional feature space according to a certain principle, so that the classification information or discrimination information scattered among many original features can be concentrated on a few new features, and the dimension of the original feature space can be reduced, which is beneficial to the application of classification algorithms [13]. All kinds of classification algorithms can be applied to the text classification system, which provides conditions for choosing a better classification algorithm [14]. Any text is a “node” in the vast text network of intertextual transformation, and texts reflect their own cultural pursuit and value orientation through mutual reference, mutual derivation, and mutual blending. Because word separation is not obvious in Chinese, there are many ambiguities, and the degree of word order freedom is high, there are some challenges in applying semantic understanding and word segmentation technology [15] in a Chinese text environment. However, the feature extraction algorithm based on pure statistics can bypass the obstacle of excessive words and has high practicability. It emphasizes that only by placing the intertextual symbols in the text in the social and cultural framework and finding out the relevant intertextual references can we fully and accurately understand the intertextual symbols. There are interactions between systems, systems and factors, and factors and factors. According to the intertextuality theory, there is intertextuality between texts, and each text is the absorption and transformation of other texts.


## 2. Related Work

Literature [16] holds that any work is formed in the intertextual knowledge cyberspace and is based on the remains or memories of other texts. Adaptability refers to the ability to make negotiable language choices from a range of possibilities, so as to achieve the purpose of communication. Reference [17] proposed a word segmentation method based on the improved source channel model. There is not only the demarcation between single word and morpheme but also the demarcation between words and phrases. Literature [18] first proposed the “Adaptation Theory”, which explains the process of language use from a new perspective. Literature [19] proposes that intertextuality can be characterized as the correlation between variables derived from the same parent, and the meaning of one text can be explained in the interwoven network of another intertextual text. Literature [20] further improves the adaptation theory and holds that the use of language is a process of continuous selection, which includes the selection of language, vocabulary, grammatical form, pronunciation, intonation, and other language structure levels. In the process of use, any language should make adaptive and dynamic choices according to context, language structure, and other aspects. Literature [21] proposed a rough segmentation model of Chinese words based on n-shortest path method to improve recall rate and accuracy. That is to say, the study of “intertextuality” in a broad sense focuses on the symbolic web fabric with infinite opening and extension in macro space and time, which means to discover the infinite meaning potential of the text. Literature [22] creatively combines Darwin’s theory of natural selection with the practice of language use, explains the role of society, culture, logic, and cognition in the process of human language communication, and asserts that language use is a process of continuous choice, with language users being able to make appropriate choices due to the H characteristics of language: variability, negotiability, and adaptability. Literature [23] discovered a polyphony theory in which all voices are triggered in the same way, and there is a dialogue between character and author expressions, which people can always hear between the lines of these dynamic exchanges. The term “dialogue” refers to the interaction of two or more texts. Literature [24] promotes text meaning certainty. The broad and narrow discourse domains of intertextuality have something in common, both objectively and dialectically: they all focus on the study of text meaning. However, differences in vision and research methods lead to differences in research opinions, namely, the determination and uncertainty of text meaning. Literature [25] develops intertextuality into a concept that truly accepts theory and distinguishes intertextuality from intertextuality from stylistic and rhetorical perspectives. On the basis of deep understanding of rhetorical phenomena, it regards previous texts in literary works as reference objects of other texts and holds that intertextuality is a phenomenon that guides reading and understanding and is an explanatory judgment.


## 3. Translation Studies Guided by Intertextuality Theory

### 3.1. The Intertextual Form of Literary Creation Text and Literary Translation Text

Intertextuality theory was first put forward as a text analysis method in France, its birthplace. With its continuous development and perfection, it has gradually been widely used in other fields of learning and scientific research, especially in translation studies, which has made great progress [26]. When extracting features from a document, we should first preprocess the documents of the corpus; then, the characteristics of the wood are extracted. Finally, the document can be classified and clustered. The process of text feature extraction can be represented by Figure 1.Figure 1
Text feature extraction process.Literary creation and translation activities are rich in intertextuality, and the three stages show different characteristics [27]. As an intertextuality theory liberated from the closed model of structuralism, it has the characteristics of inclusiveness, openness, and pluralism [28]. It emphasizes the interrelationship between the text and other texts and advocates the interactive and harmonious relationship of mutual penetration and interaction between the author and the translator, the translator and the reader, and the subject and the object, which brings new vitality and vitality to translation research, so that translation is no longer regarded as an isolated, static, and mechanical language conversion activity, and shows the infinite charm of intertextuality theory. In the first stage, the intertextual relationship between the creative text and the translated text is hypertext, and the specific intertextual expression is rewriting. The quality of feature word selection will affect the document processing, so feature word extraction is the key technology of document mining. At this stage, the creative text is the pretext and the translated text is the hypertext. The precreative text is transformed into the translated hypertext by rewriting; the second stage is the stage with the most abundant intertextuality in literary activities. Intertextuality, hypertext, and genre are the three types of intertextuality that exist between creative and translated texts. A narrow sense text is one with a fixed, closed, and clear meaning that can be investigated, analyzed, and “found the original meaning, that is, the author’s original intention.” Intertextuality is exemplified by quotation among them. The translated text is referenced in the creative text; adaptation [29] is the hypertext expression. The original text is adapted when translating French literary works, and the final text is a blend of translated and creative text. Pasting, or attaching derivative text to the main text, is how genre is expressed, with the main text being the creative text and the derivative text being the translated text. “It is not necessarily a complete paragraph of words and discourses, but materials for observation and analysis, such as whispers, interviews and dialogues, extracts of legal contracts, and so on,” say modern linguists of generalized text. This usage is actually opposite to those materials (often single sentences) that linguists fabricate to explain for debate . In the third stage, the type of intertextual relationship is still intertextual, and the expression of intertextuality is also paste. The introduction of intertextuality theory into the field of translation studies is to make a new attempt in epistemology and methodology under the background of advocating pluralism. However, whether the text is regarded as a work with clear meaning or as “natural discourse” or “interactive language,” we can still roughly extract its two basic properties: natural attribute and dialogue attribute [30].As far as the nature and characteristics of literature are concerned, literature should be the art of language. It meets people’s aesthetic needs by seeking and constructing beauty, revealing and expressing it. It realizes its existence value by acting on human’s spiritual world and cultural life. Therefore, as far as the basic nature of the text is concerned, there is no significant difference between the traditional and modern understanding, but the difference lies only in the analysis method adopted.


### 3.2. Internal and External References of Intertextual Forms in Literary Creation and Literary Translation

Literary creation texts and literary translation texts contain rich forms of intertextuality, which are manifested in a variety of intertextual types and techniques. Intertextuality is the exchange of symbols. A text is composed of the replacement, neutralization, and expansion of many other texts. All texts have the characteristics of intertextuality, and the whole world forms a huge intertextuality knowledge network. The main method of replacement, neutralization, and expansion is to take the joint conditional entropy as the fitness function of genetic algorithm and obtain the optimal feature phrase through genetic calculation. The main flow of genetic algorithm is shown in Figure2.Figure 2
Extraction of text feature words by genetic algorithm.A text is related to other texts by mutual reference and interpretation, so that when it is understood and interpreted in a specific social and historical context, it will produce “new meaning.” However, the intertextual forms of texts are complex and diverse, which are not only limited to the external morphological features presented by language symbols but also the interweaving of multiple related factors behind this superficial phenomenon, which reflect the complex internal relations of intertextual forms from different angles and in different degrees. Readable text generally means writing text that cannot be reproduced, while writable text is related to the interactive production of readers, that is, the text that can be formed only through the participation of readers and expressed in an infinite number of ways, which has the characteristics of “development.” According to intertextuality, no literary work can be called “original” because there are obvious or implicit connections between a given text and other texts. In terms of language form, it is expressed in terms of wording, sentence patterns, discourse structure, etc., which conform to the reading habits of the target language readers. In terms of cultural content, the cultural information familiar to the target readers is used to convey meaning.This reader acceptance oriented “rewriting” way reflects the individual’s cultural value orientation in literary creation and literary translation activities, that is, strive to bring the behavior mode of language into the cultural category of text readers. Therefore, the traditional text reading is only one-way consumption, while the modern text reading has changed. This kind of reading is a reproduction activity in the interaction between subject and object. To accurately understand the text unique to a culture with intertextual symbols, we need to be familiar with the potential relevance contained in the text and the quotation, excerpt and extension of one or more other texts. The interlingual communication from precreation text to translation hypertext is not only the transformation process of language symbol information but also the transplantation process of cultural information carried by language symbols. The transplantation process takes place in the writer’s individual psychological level, which is closely related to the writer’s environment, current emotion, and other factors. Each experience is unique, one-time, and cannot be copied. In the process of transformation from precreation text to translation hypertext, it is guided by the readers in the target language cultural system to avoid misunderstandings caused by the differences between Chinese and Western languages in syntactic structure, writing style, and cultural information. In the translation hypertext, the adjustment strategies such as addition, deletion, and replacement are adopted to effectively avoid the readers’ cultural misreading.Thus, the intertextuality between literary creation and literary translation reflects the individual cultural value orientation of the creator. There is no cultural boundary between texts, which are mutually open and cool, and the whole world is in the eternal movement of mutual penetration and integration between texts. The language and cultural norms embodied in the precreation text as the original language and the translated hypertext as the recipient language will influence and restrict the way of information conversion: or take the original language as the main body, or take the receiver as the main body, or take bilingual culture as the leading factor.


## 3.1. The Intertextual Form of Literary Creation Text and Literary Translation Text

Intertextuality theory was first put forward as a text analysis method in France, its birthplace. With its continuous development and perfection, it has gradually been widely used in other fields of learning and scientific research, especially in translation studies, which has made great progress [26]. When extracting features from a document, we should first preprocess the documents of the corpus; then, the characteristics of the wood are extracted. Finally, the document can be classified and clustered. The process of text feature extraction can be represented by Figure 1.Figure 1
Text feature extraction process.Literary creation and translation activities are rich in intertextuality, and the three stages show different characteristics [27]. As an intertextuality theory liberated from the closed model of structuralism, it has the characteristics of inclusiveness, openness, and pluralism [28]. It emphasizes the interrelationship between the text and other texts and advocates the interactive and harmonious relationship of mutual penetration and interaction between the author and the translator, the translator and the reader, and the subject and the object, which brings new vitality and vitality to translation research, so that translation is no longer regarded as an isolated, static, and mechanical language conversion activity, and shows the infinite charm of intertextuality theory. In the first stage, the intertextual relationship between the creative text and the translated text is hypertext, and the specific intertextual expression is rewriting. The quality of feature word selection will affect the document processing, so feature word extraction is the key technology of document mining. At this stage, the creative text is the pretext and the translated text is the hypertext. The precreative text is transformed into the translated hypertext by rewriting; the second stage is the stage with the most abundant intertextuality in literary activities. Intertextuality, hypertext, and genre are the three types of intertextuality that exist between creative and translated texts. A narrow sense text is one with a fixed, closed, and clear meaning that can be investigated, analyzed, and “found the original meaning, that is, the author’s original intention.” Intertextuality is exemplified by quotation among them. The translated text is referenced in the creative text; adaptation [29] is the hypertext expression. The original text is adapted when translating French literary works, and the final text is a blend of translated and creative text. Pasting, or attaching derivative text to the main text, is how genre is expressed, with the main text being the creative text and the derivative text being the translated text. “It is not necessarily a complete paragraph of words and discourses, but materials for observation and analysis, such as whispers, interviews and dialogues, extracts of legal contracts, and so on,” say modern linguists of generalized text. This usage is actually opposite to those materials (often single sentences) that linguists fabricate to explain for debate . In the third stage, the type of intertextual relationship is still intertextual, and the expression of intertextuality is also paste. The introduction of intertextuality theory into the field of translation studies is to make a new attempt in epistemology and methodology under the background of advocating pluralism. However, whether the text is regarded as a work with clear meaning or as “natural discourse” or “interactive language,” we can still roughly extract its two basic properties: natural attribute and dialogue attribute [30].As far as the nature and characteristics of literature are concerned, literature should be the art of language. It meets people’s aesthetic needs by seeking and constructing beauty, revealing and expressing it. It realizes its existence value by acting on human’s spiritual world and cultural life. Therefore, as far as the basic nature of the text is concerned, there is no significant difference between the traditional and modern understanding, but the difference lies only in the analysis method adopted.


## 3.2. Internal and External References of Intertextual Forms in Literary Creation and Literary Translation

Literary creation texts and literary translation texts contain rich forms of intertextuality, which are manifested in a variety of intertextual types and techniques. Intertextuality is the exchange of symbols. A text is composed of the replacement, neutralization, and expansion of many other texts. All texts have the characteristics of intertextuality, and the whole world forms a huge intertextuality knowledge network. The main method of replacement, neutralization, and expansion is to take the joint conditional entropy as the fitness function of genetic algorithm and obtain the optimal feature phrase through genetic calculation. The main flow of genetic algorithm is shown in Figure2.Figure 2
Extraction of text feature words by genetic algorithm.A text is related to other texts by mutual reference and interpretation, so that when it is understood and interpreted in a specific social and historical context, it will produce “new meaning.” However, the intertextual forms of texts are complex and diverse, which are not only limited to the external morphological features presented by language symbols but also the interweaving of multiple related factors behind this superficial phenomenon, which reflect the complex internal relations of intertextual forms from different angles and in different degrees. Readable text generally means writing text that cannot be reproduced, while writable text is related to the interactive production of readers, that is, the text that can be formed only through the participation of readers and expressed in an infinite number of ways, which has the characteristics of “development.” According to intertextuality, no literary work can be called “original” because there are obvious or implicit connections between a given text and other texts. In terms of language form, it is expressed in terms of wording, sentence patterns, discourse structure, etc., which conform to the reading habits of the target language readers. In terms of cultural content, the cultural information familiar to the target readers is used to convey meaning.This reader acceptance oriented “rewriting” way reflects the individual’s cultural value orientation in literary creation and literary translation activities, that is, strive to bring the behavior mode of language into the cultural category of text readers. Therefore, the traditional text reading is only one-way consumption, while the modern text reading has changed. This kind of reading is a reproduction activity in the interaction between subject and object. To accurately understand the text unique to a culture with intertextual symbols, we need to be familiar with the potential relevance contained in the text and the quotation, excerpt and extension of one or more other texts. The interlingual communication from precreation text to translation hypertext is not only the transformation process of language symbol information but also the transplantation process of cultural information carried by language symbols. The transplantation process takes place in the writer’s individual psychological level, which is closely related to the writer’s environment, current emotion, and other factors. Each experience is unique, one-time, and cannot be copied. In the process of transformation from precreation text to translation hypertext, it is guided by the readers in the target language cultural system to avoid misunderstandings caused by the differences between Chinese and Western languages in syntactic structure, writing style, and cultural information. In the translation hypertext, the adjustment strategies such as addition, deletion, and replacement are adopted to effectively avoid the readers’ cultural misreading.Thus, the intertextuality between literary creation and literary translation reflects the individual cultural value orientation of the creator. There is no cultural boundary between texts, which are mutually open and cool, and the whole world is in the eternal movement of mutual penetration and integration between texts. The language and cultural norms embodied in the precreation text as the original language and the translated hypertext as the recipient language will influence and restrict the way of information conversion: or take the original language as the main body, or take the receiver as the main body, or take bilingual culture as the leading factor.


## 4. Analysis of Feature Extraction Algorithm

### 4.1. Analysis of N-Gram Algorithm

The basic idea of N-gram algorithm is to operate the text content with a sliding window of sizeN according to the byte stream to form a sequence of byte segments of length N, each byte segment is called gram, and the frequency of all grams is counted and filtered according to the preset threshold value to form a key gram list, which is the feature vector space of the text content. Each gram in the list is a feature vector dimension. Sampling analysis shows that the acquisition accuracy rate of multiword feature words (that is, the proportion of multiword feature words that are semantically effective according to manual judgment) is 94% on average, and the dimension of feature vector is reduced by 15% on average. Figure 3 below shows the comparison of acquisition accuracy rates between multiword feature words and double-word feature words.Figure 3
Comparison of acquisition accuracy between multiword feature words and double-word feature words.Bigram uses the feature extraction function to calculate each word in the text data set, assigns a weight to each feature, and then chooses the best word to serve as the document’s feature word. The feature word and the weight associated with it can be used to represent a documentD:
(1)Dt1,t2,⋯,tn=t1,w1,t2,w2,⋯,tt,wt,⋯,tn,wn.At the same time, the understanding and expression of these messages need to be related to the context, because literary works are created under the complicated situation of simultaneously affirming and denying another text. After the optimization selection of genetic algorithm, finally, a word is selected as the feature word. Figure4 can be obtained by ranking the entropy values of each group from small to people.Figure 4
Joint conditional entropy of graphs.Feature selection does not change the nature of the original feature space but selects some important features from the original feature space to form a new low-dimensional space. N-gram algorithm has the following advantages: language independence, it can process Chinese and English texts and complex and simplified texts at the same time, without linguistic processing of text content, and has strong fault tolerance for spelling errors. If the similarity coefficient between documents is expressed as the distance between two vectors in dimensional space, it can be calculated by the inner product between vectors:(2)SimD1,D2=∑k=1nwik⋅w2k.Entries whose DF value is lower than a predetermined threshold are low-frequency words, and they do not contain category information. Removing such entries from the original feature space can reduce the dimension of the feature space without affecting the performance of the classifier. Since Chinese characters are double-byte characters, takeN=4, that is, divide byte segments in units of 4 bytes. When clustering is divided into 5 categories and 7 categories, the trend of broken lines is very similar, and the change trend of entropy value after clustering is similar. Explain that the text set is divided into more than one class, and the distribution of average joint conditional entropy is shown in Figure 5 below.Figure 5
Average joint conditional entropy after clustering.The computational complexity of document frequency is low and increases linearly with the increase of training set, which can be applied to large-scale corpus. Through the analysis of sentence groups, paragraphs, and chapters, we can get the contextual framework with different granularity and text features at different levels. This provides a broader perspective for translation, especially literary translation. Through intertextuality, the translator can better understand the “intertextual reference” in the original work, allowing the context knowledge of the original work to be gradually internalized and structured in the translator’s mind, allowing me to reproduce the original work's thought, emotion, atmosphere, and mood, and laying a firm foundation for target language readers to reproduce the original picture and build a spiritual resonance. If a word has a high TF, that is, it appears more frequently in one document and rarely in other documents, or for a word, it represents fewer documents, and the corresponding IDF is larger, which indicates that the word has a good ability to distinguish between categories. Convert the text into Boolean expression, and compare and calculate the relationship between documents according to the logical relationship between different document expressions. The expression is as follows:(3)Di=t1,t2,⋯,tn.Firstly, the text corpus should be segmented according to Chinese, English, and paragraph punctuation, and the original text should be segmented into paragraph sequences, that is, relatively logically independent single sentences or paragraphs. Then, bigram segmentation is performed on each paragraph to obtain the gram list. Because the contextual framework comprehensively describes the content of the text from three aspects: static category, semantic relationship between features, and favorable and unfavorable tendency among features, it can better reflect the internal relationship of the text by using it as a feature of the text. The result of composition is to generate a new domain, or to weight a domain. It describes the influence degree of characteristic words on different kinds of sample data. Because the N-gram algorithm uses a window with a fixed length ofn for segmentation, it is better to deal with the feature words with a length of just n (that is, the bigram method has a higher accuracy for the processing of double words), but the feature words with a length greater than or less than n will be segmented, which will cause some deviations in semantics and word order, and will produce wrong results in the subsequent retrieval or classification process.


### 4.2. Improved Algorithm Based on N-Gram

In view of the above problems of N-gram algorithm, especially the bigram segmentation method mainly used in Chinese applications, in order to obtain more accurate and effective feature vectors in the process of feature extraction, we propose an improved algorithm based on N-gram. In the process of interpreting the original work, the translator’s mind is gradually internalized in the process of structuring and interpreting the original work. With the deepening of reading, cognitive context is also enriched.T1 is evenly distributed in the document set. According to subjective judgment, the weight of T1 should be smaller than T2, T3, and T4, but the weight calculated by TFIDF is shown in Figure 6 below.Figure 6
Calculation results of feature weight.The improved algorithm based on N-gram is to count not only the occurrence frequency of gram but also the situation of a gram and its previous neighboring gram, and record this in gram incidence matrix. Therefore, it is effective for the feature words of the feature set shared by all classes, but it is not applicable for the feature words with individual feature sets of specific classes. Depending on the gain relationship between the information entropy of the training data set and the conditional entropy of the words in the document, the information amount that the words can provide in the classification is determined. The formula is as follows:(4)IGTi=HD−HD/Ti.There are two methods: the first is to start from each data, find similar data and combine them into a group, and finally put all the data into a group. The second is to divide all data into one category from top to bottom. Each iteration is divided into smaller parts. After all the texts are processed, the gram incidence matrix is processed to find out which grams appear frequently. If the frequency of continuous occurrence is greater than the preset threshold, they are combined into multiword feature words. Suppose there are two categories with five documents in each category, and only three feature itemsT1, T2, and T3 are considered. As shown in Figure 7, all logarithms in this paper are calculated based on 10.Figure 7
Document word frequency.Literary translation was aimed at fully reproducing the aesthetic meaning of the original text, and the translated text must have the same literary function as the original work. Readers expect to fully appreciate the original text’s emotions and thoughts while also enjoying reading as if they were the original text’s readers. Because the vast majority of multiwords fall into the three-word and four-word categories, words with more than five words are uncommon, and even if they do, they can be broken down into more than two words with less than four words without losing information, so this algorithm only considers the three-word and four-word categories. The weight of characteristic words with relatively uniform distribution within the category should be higher than that with uneven distribution, that is, words with uneven distribution within the category should have lower weight.(5)pd=d∑k=1Cidk.In text feature selection, for entryT and category C, information gain IG measures the information gain of t to C by examining the frequency of documents with and without T in C. The calculated information gain value of words with uniform distribution in category Ci is higher than that of words with uneven distribution in category Ci, and the obtained weight value will also be higher. The information gain of H (C/t1) and H (C/t2) is shown in Figure 8 below.Figure 8
Information gain.Two gram incidence matrixes are used for recording, respectively, and the gram incidence matrix for processing three words isa, which is a two-dimensional matrix. Gram incidence matrix for four-word processing is B, which is a three-dimensional matrix. Determine the type to be filled in each unit according to the expected knowledge of sentence classes. Only the unit containing objects is filled with specific concepts that meet the requirements; semantic blocks with content C are used to extract concrete concepts and abstract concepts. From the horizontal point of view, it shows the intertextual relationship between social and cultural system and literary creation text and literary translation text, and the intertextual relationship between literary system and literary creation text and literary translation text; from the vertical point of view, there are two ways: top-down and bottom-up, namely, social and cultural system-literary system-literary creation text and literary translation text. Finding the k partitions of the data set makes the criterion function converge, and the square error criterion is usually adopted, which is defined as follows:
(6)E=∑i=1k∑peCip−mi2.Mastering and skillfully using the target language is also an important condition for realizing the cultural cognition of the source language. Only in this way can the translator sensitively capture the subtle semantic gap between the original text and the translation and transmit the cultural information, thoughts, and emotions contained in the original text to the readers to the greatest extent, in the process of using N-gram algorithm, due to the considerable number of bigrams segmented. The gram incidence matrix belongs to two-dimensional or three-dimensional matrix, and the dimension vector is the cut bigram, which has high spatial complexity. Therefore, in the implementation of this algorithm, the gram incidence matrix is stored in the form of sparse matrix, which effectively reduces the space consumption.


## 4.1. Analysis of N-Gram Algorithm

The basic idea of N-gram algorithm is to operate the text content with a sliding window of sizeN according to the byte stream to form a sequence of byte segments of length N, each byte segment is called gram, and the frequency of all grams is counted and filtered according to the preset threshold value to form a key gram list, which is the feature vector space of the text content. Each gram in the list is a feature vector dimension. Sampling analysis shows that the acquisition accuracy rate of multiword feature words (that is, the proportion of multiword feature words that are semantically effective according to manual judgment) is 94% on average, and the dimension of feature vector is reduced by 15% on average. Figure 3 below shows the comparison of acquisition accuracy rates between multiword feature words and double-word feature words.Figure 3
Comparison of acquisition accuracy between multiword feature words and double-word feature words.Bigram uses the feature extraction function to calculate each word in the text data set, assigns a weight to each feature, and then chooses the best word to serve as the document’s feature word. The feature word and the weight associated with it can be used to represent a documentD:
(1)Dt1,t2,⋯,tn=t1,w1,t2,w2,⋯,tt,wt,⋯,tn,wn.At the same time, the understanding and expression of these messages need to be related to the context, because literary works are created under the complicated situation of simultaneously affirming and denying another text. After the optimization selection of genetic algorithm, finally, a word is selected as the feature word. Figure4 can be obtained by ranking the entropy values of each group from small to people.Figure 4
Joint conditional entropy of graphs.Feature selection does not change the nature of the original feature space but selects some important features from the original feature space to form a new low-dimensional space. N-gram algorithm has the following advantages: language independence, it can process Chinese and English texts and complex and simplified texts at the same time, without linguistic processing of text content, and has strong fault tolerance for spelling errors. If the similarity coefficient between documents is expressed as the distance between two vectors in dimensional space, it can be calculated by the inner product between vectors:(2)SimD1,D2=∑k=1nwik⋅w2k.Entries whose DF value is lower than a predetermined threshold are low-frequency words, and they do not contain category information. Removing such entries from the original feature space can reduce the dimension of the feature space without affecting the performance of the classifier. Since Chinese characters are double-byte characters, takeN=4, that is, divide byte segments in units of 4 bytes. When clustering is divided into 5 categories and 7 categories, the trend of broken lines is very similar, and the change trend of entropy value after clustering is similar. Explain that the text set is divided into more than one class, and the distribution of average joint conditional entropy is shown in Figure 5 below.Figure 5
Average joint conditional entropy after clustering.The computational complexity of document frequency is low and increases linearly with the increase of training set, which can be applied to large-scale corpus. Through the analysis of sentence groups, paragraphs, and chapters, we can get the contextual framework with different granularity and text features at different levels. This provides a broader perspective for translation, especially literary translation. Through intertextuality, the translator can better understand the “intertextual reference” in the original work, allowing the context knowledge of the original work to be gradually internalized and structured in the translator’s mind, allowing me to reproduce the original work's thought, emotion, atmosphere, and mood, and laying a firm foundation for target language readers to reproduce the original picture and build a spiritual resonance. If a word has a high TF, that is, it appears more frequently in one document and rarely in other documents, or for a word, it represents fewer documents, and the corresponding IDF is larger, which indicates that the word has a good ability to distinguish between categories. Convert the text into Boolean expression, and compare and calculate the relationship between documents according to the logical relationship between different document expressions. The expression is as follows:(3)Di=t1,t2,⋯,tn.Firstly, the text corpus should be segmented according to Chinese, English, and paragraph punctuation, and the original text should be segmented into paragraph sequences, that is, relatively logically independent single sentences or paragraphs. Then, bigram segmentation is performed on each paragraph to obtain the gram list. Because the contextual framework comprehensively describes the content of the text from three aspects: static category, semantic relationship between features, and favorable and unfavorable tendency among features, it can better reflect the internal relationship of the text by using it as a feature of the text. The result of composition is to generate a new domain, or to weight a domain. It describes the influence degree of characteristic words on different kinds of sample data. Because the N-gram algorithm uses a window with a fixed length ofn for segmentation, it is better to deal with the feature words with a length of just n (that is, the bigram method has a higher accuracy for the processing of double words), but the feature words with a length greater than or less than n will be segmented, which will cause some deviations in semantics and word order, and will produce wrong results in the subsequent retrieval or classification process.


## 4.2. Improved Algorithm Based on N-Gram

In view of the above problems of N-gram algorithm, especially the bigram segmentation method mainly used in Chinese applications, in order to obtain more accurate and effective feature vectors in the process of feature extraction, we propose an improved algorithm based on N-gram. In the process of interpreting the original work, the translator’s mind is gradually internalized in the process of structuring and interpreting the original work. With the deepening of reading, cognitive context is also enriched.T1 is evenly distributed in the document set. According to subjective judgment, the weight of T1 should be smaller than T2, T3, and T4, but the weight calculated by TFIDF is shown in Figure 6 below.Figure 6
Calculation results of feature weight.The improved algorithm based on N-gram is to count not only the occurrence frequency of gram but also the situation of a gram and its previous neighboring gram, and record this in gram incidence matrix. Therefore, it is effective for the feature words of the feature set shared by all classes, but it is not applicable for the feature words with individual feature sets of specific classes. Depending on the gain relationship between the information entropy of the training data set and the conditional entropy of the words in the document, the information amount that the words can provide in the classification is determined. The formula is as follows:(4)IGTi=HD−HD/Ti.There are two methods: the first is to start from each data, find similar data and combine them into a group, and finally put all the data into a group. The second is to divide all data into one category from top to bottom. Each iteration is divided into smaller parts. After all the texts are processed, the gram incidence matrix is processed to find out which grams appear frequently. If the frequency of continuous occurrence is greater than the preset threshold, they are combined into multiword feature words. Suppose there are two categories with five documents in each category, and only three feature itemsT1, T2, and T3 are considered. As shown in Figure 7, all logarithms in this paper are calculated based on 10.Figure 7
Document word frequency.Literary translation was aimed at fully reproducing the aesthetic meaning of the original text, and the translated text must have the same literary function as the original work. Readers expect to fully appreciate the original text’s emotions and thoughts while also enjoying reading as if they were the original text’s readers. Because the vast majority of multiwords fall into the three-word and four-word categories, words with more than five words are uncommon, and even if they do, they can be broken down into more than two words with less than four words without losing information, so this algorithm only considers the three-word and four-word categories. The weight of characteristic words with relatively uniform distribution within the category should be higher than that with uneven distribution, that is, words with uneven distribution within the category should have lower weight.(5)pd=d∑k=1Cidk.In text feature selection, for entryT and category C, information gain IG measures the information gain of t to C by examining the frequency of documents with and without T in C. The calculated information gain value of words with uniform distribution in category Ci is higher than that of words with uneven distribution in category Ci, and the obtained weight value will also be higher. The information gain of H (C/t1) and H (C/t2) is shown in Figure 8 below.Figure 8
Information gain.Two gram incidence matrixes are used for recording, respectively, and the gram incidence matrix for processing three words isa, which is a two-dimensional matrix. Gram incidence matrix for four-word processing is B, which is a three-dimensional matrix. Determine the type to be filled in each unit according to the expected knowledge of sentence classes. Only the unit containing objects is filled with specific concepts that meet the requirements; semantic blocks with content C are used to extract concrete concepts and abstract concepts. From the horizontal point of view, it shows the intertextual relationship between social and cultural system and literary creation text and literary translation text, and the intertextual relationship between literary system and literary creation text and literary translation text; from the vertical point of view, there are two ways: top-down and bottom-up, namely, social and cultural system-literary system-literary creation text and literary translation text. Finding the k partitions of the data set makes the criterion function converge, and the square error criterion is usually adopted, which is defined as follows:
(6)E=∑i=1k∑peCip−mi2.Mastering and skillfully using the target language is also an important condition for realizing the cultural cognition of the source language. Only in this way can the translator sensitively capture the subtle semantic gap between the original text and the translation and transmit the cultural information, thoughts, and emotions contained in the original text to the readers to the greatest extent, in the process of using N-gram algorithm, due to the considerable number of bigrams segmented. The gram incidence matrix belongs to two-dimensional or three-dimensional matrix, and the dimension vector is the cut bigram, which has high spatial complexity. Therefore, in the implementation of this algorithm, the gram incidence matrix is stored in the form of sparse matrix, which effectively reduces the space consumption.


## 5. Conclusions

The intertextuality of literary texts and literary translation texts presents rich and complicated internal relations. Therefore, language users can make dynamic and adaptive choices on the basis of intertextual knowledge network by making use of the interrelation, intersection, and integration between texts. Intertextuality embodies Lin Yutang’s individual cultural value orientation of satisfying readers’ acceptance in their respective cultural systems through varying degrees of transformation of language form and content in specific literary creation texts and literary translation texts. Feature vectors include double-word feature words and multiword feature words, among which the accuracy and representativeness of multiword feature words are better than N-gram algorithm, especially for academic papers, technical articles, and other professional documents. Therefore, intertextuality provides the possibility and template of choice for language adaptation, that is, intertextuality can guide the translator to make adaptive choices on the basis of intertextuality, so as to convey the thoughts and emotions of the original text to the readers, and make the readers resonate with the original author. The combination of adaptation and intertextuality theory is no longer just a pragmatic overview of language use from macroperspectives of cognition, society, and culture but a theoretical method that actually applies and guides translation practice. The intertextuality study of literary text translation reveals that the constituent factors involved in literary text translation—the source text, author, reader, translator, target text, and critic—collaborated to plan and implement a series of interactive behaviors before, during, and after the occurrence of literary text intertextuality translation.



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