Automatic Translation Research Articles

Russian Sign Language Database for Clinical Use: Data and Annotation Peculiarities

This work has been carried out by the members of the Laboratory of Speech and Multimodal Interfaces of the St. Petersburg Federal Research Center of the Russian Academy of Sciences within an interdisciplinary research project aimed at the creation of an automatic Russian sign language translation system. The paper presents the design of a Russian sign language digital database for a specific subject area, namely, «The first visit to doctor».Our database is meant to be used first of all a dataset for training neural network-based systems of automatic translation from Russian sign language. But also, it can be of interest for linguistics of sign languages in general since the approach elucidates solution of a number of distorting phenomena typical for continuous sign language tracking, such as epenthesis, assimilation, reduction, and hold deletion.The principal difference between the presented video data and other datasets developed for similar purposes is the use of continuous sign utterances and elements of Russian sign language proper instead of the so-called “signed Russian” (that is, the visual form of the Russian spoken language), popular in deaf schooling.One of the most challenging problems, dealt with in the paper, is an automatic segmentation of sign speech into separate meaningful units with clearly defined boundaries. Its solution is hampered by signs’ deformations in continuous speech. In this paper, the segmentation is carried out within the framework of the movement-hold model. This approach allows extraction of their functional core as well as annotation of possible changes likely to appear in different signs’ segments. Accordingly, each utterance was subdivided into segments of motion and hold, and the resulting sign schemes were then entered into a separate directory containing information about the main parameters of each hold, including the possibilities of change due to immediate environment. The result of this work is a full decomposition of the signs, forming the database which can find its application in different statistical models of Russian Sign Language.Among other linguistic peculiarities of the database should be noted lexical variability of signs, mouthing and code switching between Russian sign language and “signed Russian”. Also, our signers learned Russian sign language in different regions of Russia, thus the collected data is potentially a source for research in dialectal variability of Russian sign language.

Developing a Rule-Based Machine-Translation System, Ewondo–French–Ewondo

Machine translation (MT) significantly contributes to democratizing access to textual information across multiple languages and is established as a dynamic language service in the global multilingual society. Not surprisingly, the attractiveness of the MT market has stirred up spectacular innovations, driven by artificial intelligence in the digital technology industry. The commercial stakes of the industry has led to massive investments in the development of automatic translation systems for languages of wider communication and an increased marginalization of minority languages in this avenue. This article reports on the on-going development of a low-tech, rule-based MT system for Ewondo, a Bantu low-resourced language spoken in Cameroon. The project aims to fill the gap in access to MT services in the target minority language community and to generate parallel corpora from and into the Ewondo language.

Automated and Human Interaction in Written Discourse: A Contrastive Parallel Corpus-based Investigation of Metadiscourse Features in Machine-Human Translations

The rise of the internet has generated a need for fast online translations, which human translators cannot meet. Statistical tools such as Google and Baidu Translate provide automatic translation from one written language to another. This study reports the descriptive comparison of the machine-translation (MT) with human translation (HT), considering the metadiscoursal interactional features. The study uses a parallel corpus consisting of 79 texts translated from Chinese to English by professional human translators and machine translations (Baidu translate & Google translate) and a comparable reference corpus of non-translated English text. The statistical analysis revealed no statistically significant difference between Baidu and Google translate regarding all types of metadiscoursal indicators. However, the findings of this study demonstrate significant disparities in the interactional characteristics of various HT and MT groups. Compared to the metadiscourse features in non-translated English political texts, human translators were found to outperform machine translations in the use of attitude markers. In contrast, the distribution of directives in machine-translated texts is more native-like. In addition, MT and HT have utilized a significantly smaller number of hedges, self-mention, and readers than non-translated texts. Our results indicate that the MT systems, though still calling for further improvement, have shown tremendous growth potential and may complement human translators.

Recognize The Alphabet of Fingerspelling Using Statistical Classifiers to Facilitate Communication Between Hearing-Impaired Persons and Others

The predominant method of communication for hearing-impaired and deaf people is still sign language. It is a carefully constructed hand gesture language, and each motion denotes a certain meaning. The purpose of this paper is to create a system for Arabic Sign Language automatic translation. The proposed Arabic sign to Text System consists of five primary stages and serves as a translator for deaf and dumb persons and normal people to improve communication. This system depends on building a two datasets image features for Arabic sign language gestures alphabets from two resources: the Arabic Sign Language dictionary and gestures from different signers' humans. It also uses gesture recognition techniques, which lets the user interact with the outside world. Video and images capture, Video and images processing, Hand Signs construction, classification, and finally text transformation and interpretation. In this paper, we use a set of appropriate features in step hand sign construction and classification of based on different classification algorithms such as (KNN, MLP and C4.5) and compare these results to get better classifier. This system offers a novel technique of hand detection that detects and extracts hand gestures of Arabic Sign from Image or Video..

The Impact of Digital Technologies on the Structure of Translation Activities

Automated and machine translation tools have become integral elements of the translation process, but their place in translation models is still not defined. On the one hand, the trend towards automating translation practices is reducing the burden on human translators, freeing them from repetitive, tedious and low-tech tasks and freeing them up for more complex and creative tasks, on the other hand, it is also invading into the territory of professional translators at the conversion level. This article attempts to comprehend translation activity in the era of digital translation and modeling of translation activity taking into account digital technologies, as opposed to the model of traditional translation activity. The main result of this work can be considered as definition of the place of digital technologies in the actant model of translation activity as auxiliary means. With the development of digital technologies, the model of translation activity is developing towards automation and the emergence of new forms of translation activity. At the level of intercultural communication, since AI does not yet have the emotional and creative intelligence of a higher mind, as well as the biological and social characteristics inherent in human intelligence, automatic translation models are subject to limited cultural, contextual, historical and social norms. It is for this reason that machine translations require final editing, interpretation, adaptation, and other cultural adjustments by human translators.

Deep language algorithms predict semantic comprehension from brain activity

Deep language algorithms, like GPT-2, have demonstrated remarkable abilities to process text, and now constitute the backbone of automatic translation, summarization and dialogue. However, whether these models encode information that relates to human comprehension still remains controversial. Here, we show that the representations of GPT-2 not only map onto the brain responses to spoken stories, but they also predict the extent to which subjects understand the corresponding narratives. To this end, we analyze 101 subjects recorded with functional Magnetic Resonance Imaging while listening to 70 min of short stories. We then fit a linear mapping model to predict brain activity from GPT-2’s activations. Finally, we show that this mapping reliably correlates (mathcal {R}=0.50, p<10^{-15}) with subjects’ comprehension scores as assessed for each story. This effect peaks in the angular, medial temporal and supra-marginal gyri, and is best accounted for by the long-distance dependencies generated in the deep layers of GPT-2. Overall, this study shows how deep language models help clarify the brain computations underlying language comprehension.

Towards Sentiment Analysis for Romanian Twitter Content

With the increased popularity of social media platforms such as Twitter or Facebook, sentiment analysis (SA) over the microblogging content becomes of crucial importance. The literature reports good results for well-resourced languages such as English, Spanish or German, but open research space still exists for underrepresented languages such as Romanian, where there is a lack of public training datasets or pretrained word embeddings. The majority of research on Romanian SA tackles the issue in a binary classification manner (positive vs. negative), using a single public dataset which consists of product reviews. In this paper, we respond to the need for a media surveillance project to possess a custom multinomial SA classifier for usage in a restrictive and specific production setup. We describe in detail how such a classifier was built, with the help of an English dataset (containing around 15,000 tweets) translated to Romanian with a public translation service. We test the most popular classification methods that could be applied to SA, including standard machine learning, deep learning and BERT. As we could not find any results for multinomial sentiment classification (positive, negative and neutral) in Romanian, we set two benchmark accuracies of ≈78% using standard machine learning and ≈81% using BERT. Furthermore, we demonstrate that the automatic translation service does not downgrade the learning performance by comparing the accuracies achieved by the models trained on the original dataset with the models trained on the translated data.

Suffixation Rules of Manipuri Verbs in English to Manipuri Machine Translation

The suffixation of verbs plays an important role in the formation of a meaningful sentence in Manipuri language. All the verbs in Manipuri are bound roots. So, there is always a suffix at the verb in Manipuri sentences. In English language, the tense of a sentence is determined from the verb but in Manipuri language, the tense of a sentence is determined from the suffix(es) associated with the verb. English to Manipuri Machine Translation (EMMT) is a rule-based automatic translation system. In EMMT, the suffixation rules of Manipuri verbs are essential for the derivation of an appropriate suffix(es) of the translated verb in Manipuri from the input English sentence. In this MT, suffixation rules are proposed to use in the post-processing module as a new approach. This paper presents the process of EMMT, different suffixation rules of Manipuri verbs and implementation in EMMT according to the types of tense in English.

Une Approche Linguistique pour l’Analyse, la Reconnaissance et la Traduction Automatiques des Verbes du Domaine Administratif et de Droit

A Linguistic Approach to The Analysis, Recognition and Automatic Translation of Administrative and Legal Verbs&#x0D; Due to globalization and with the advent of the new digital era, economic, social, and mostly administrative sectors are now required to participate in the digitization and automation of data. This will make several tasks that were previously totally performed by humans easier. Hence, in this context, the present study investigated the application of machine translation (MT) in the administrative field. Using a semantic-syntactic approach and the French Verbs (LVF) dictionary by Jean Dubois and Françoise Dubois-Charlier, we analyzed the verbs used in the administration and legal fields. Following this initial theoretical stage, and using the NooJ platform, we deployed the acquired linguistic data for the construction of a French-Arabic machine translation application. To implement this application, we will design formal grammars for the automatic analysis and recognition of the many semantic-syntactic schemes defining this class of verbs, as well as a bilingual dictionary of legal and administrative verbs. The utmost objective of this research work is the achievement of an accurate and trustworthy translation. In the first theoretical part, we will identify the linguistic characteristics of the different verbs in the administrative and legal fields. At this first level of our approach, we will examine the syntactic constructions of these verbs and then, we will try to delimit the patterns or the distinctive semantic-syntactic schemes of the verbs used in a very specific discourse, which is the administrative discourse. In the second stage of automatic processing, we will try to create a computer process which can:&#x0D; • Automatically analyze and recognize administrative verbs using the NooJ platform. • Properly translate the verbs studied in Arabic. For the creation of this MT process, the interest of which mainly concerns administrative documents, for social (divorce, inheritance, etc.) and even economic reasons, we will: a/ Integrate legal and administrative verbs into a bilingual French-Arabic electronic dictionary, describing them according to NooJ operators: inflection, conjugation, derivation and syntax operators, etc. In this step, we created: • Paraderivational.nof derivational paradigms. • “paraflexion.nof” bending paradigms. • “paradigmflexionnel.nof” conjugation paradigms • The syntactic formalization where we rewrote the syntactic schemes of the LVF. • The translation of each verbal entry into Arabic. Exp : &#x0D; V+Emploi=01+AUX=AVOIR+FLX=CHANTER+CONS=T11b0+N0VN1PREPN2+N0Hum+V+N1Hum+N2Abst+N2Vinf+PREP="de"+DRV=AV1e : ARTISTE +DRV=AV2: COUSIN+DRV=DN15: DIRECTOR+DRV=DN15a: TABLE+DOM=DRO +CLASS=F2c+OPER="ict qn D offence"+LEXI=1+AR="َََّتَهَم&#x0D; b/ Schematize the semantic-syntactic patterns by the formal grammars of NooJ, to automatically recognize the legal verbs used in an administrative corpus. The formal grammars used are called syntactic disambiguation grammars (Silberztein, Max 2015). In this phase, we produced disambiguation grammars that allowed us to analyze only the requested syntactic constructions. The disambiguation grammar is necessary and important, not only for the automatic analysis and recognition of the correct syntactic constructions, but also for producing a correct and reliable translation. c/ Create an automatic translation grammar that will allow us to obtain a suitable, reliable translation, which will respect the standards of the target language: Arabic. d/ Find solutions for the disambiguation of polysemic predicates using Gaston Gross's "object class" theory which allowed us to refine the recognition grammars and subsequently obtain encouraging results, where the verbs polysemous are suitably translated into Arabic. The originality of this study emanates from the fact that it provides, not only a linguistic description of the verbs of the administrative and legal domain, but especially of the integration of the various data in the NooJ software, in order to facilitate the treatment and the automatic translations of the two languages in question.

Design of an Automatic Evaluation System for English Translation Based on Artificial Intelligence Fusion Control Algorithm

In the process of globalization, language plays an increasingly important role in international communication. As a global language, English involves many fields, so the translation of English is very important. Not all translations can be used, and the quality of the translation must be accurate. Therefore, it is necessary to evaluate the quality of the translation of the translation system. Only when it reaches a certain level can it meet the application standard. The automatic evaluation system is a specific application of intelligent information processing technology. The system can automatically complete the compilation and feed back the system space-time resource information consumed by the user to the user in time, so that the user can check the pros and cons of the algorithm. The purpose of this article is to study the design and research of the English translation automatic evaluation system based on the artificial intelligence fusion control algorithm, and it was expected to combine the artificial intelligence fusion algorithm with the automatic evaluation of the English translation system. In this way, the traditional evaluation efficiency of translation could be further improved and the consumption of human resources can be reduced. Data fusion refers to an information processing technology that uses a computer to automatically analyze and synthesize several observational information obtained in time series under certain criteria to complete the required decision-making and evaluation tasks. This article proposed an automatic terminology translation method based on central word constraints aimed at the different linguistic characteristics of English and Chinese terms; the materials were sequenced before translation. The macroevaluation index was also introduced into the machine translation fusion method to achieve a balance between the robustness and effectiveness of the translation fusion technology. The experimental results of this article showed that under the traditional evaluation system, the response time of the system was 5.5 s and 6.3 s when the number of translations to be judged was 9 sentences and 11 sentences, respectively, whereas under the fusion control method, it was 4.1 s and 4.6 s, respectively. Therefore, the system evaluation efficiency under the fusion control algorithm was higher.

How to generate Muong speech directly from Vietnamese text: Cross-lingual speech synthesis for close language pair

The paper introduces a method for automatic translation of Vietnamese text into Muong speech in two dialects, Muong Bi - Hoa Binh and Muong Tan Son - Phu Tho, which are all unwritten dialects of the Muong language. Due to the very close relationship between the Vietnamese and Muong languages, the translation system was built to look like a cross-lingual speech synthesis system, in which the input is the text of one language (i.e., the Vietnamese) and the output is the speech of another language (i.e., the two Muong dialects). The system used the modern sequence-to-sequence TTS neural models Tacotron2 and WaveGlow. The evaluation results showed a high quality of translation (with a fluency score of 4.61/5.0 and an adequacy score of 4.79/5.0) and also synthesized speech quality (with naturalness on the MOS scale of 4.68/5.0 and intelligibility of 94.60%). The received results show that the applicability of the proposed system to other minority languages is promising, especially in the case of unwritten languages.

Harmonized procedure coding system for surgical procedures and analysis of surgical site infections (SSI) of five European countries

BackgroundThe use of routine data will be essential in future healthcare research. Therefore, harmonizing procedure codes is a first step to facilitate this approach as international research endeavour. An example for the use of routine data on a large scope is the investigation of surgical site infections (SSI). Ongoing surveillance programs evaluate the incidence of SSI on a national or regional basis in a limited number of procedures. For example, analyses by the European Centre for Disease Prevention (ECDC) nine procedures and provides a mapping table for two coding systems (ICD9, National Healthcare Safety Network [NHSN]). However, indicator procedures do not reliably depict overall SSI epidemiology. Thus, a broader analysis of all surgical procedures is desirable. The need for manual translation of country specific procedures codes, however, impedes the use of routine data for such an analysis on an international level. This project aimed to create an international surgical procedure coding systems allowing for automatic translation and categorization of procedures documented in country-specific codes.MethodsWe included the existing surgical procedure coding systems of five European countries (France, Germany, Italy, Spain, and the United Kingdom [UK]). In an iterative process, country specific codes were grouped in ever more categories until each group represented a coherent unit based on method of surgery, interventions performed, extent and site of the surgical procedure. Next two ID specialist (arbitrated by a third in case of disagreement) independently assigned country-specific codes to the resulting categories. Finally, specialist from each surgical discipline reviewed these assignments for their respective field.ResultsA total number of 153 SALT (Staphylococcus aureus Surgical Site Infection Multinational Epidemiology in Europe) codes from 10 specialties were assigned to 15,432 surgical procedures. Almost 4000 (26%) procedure codes from the SALT coding system were classified as orthopaedic and trauma surgeries, thus this medical field represents the most diverse group within the SALT coding system, followed by abdominal surgical procedures with 2390 (15%) procedure codes.ConclusionMapping country-specific codes procedure codes onto to a limited number of coherent, internally and externally validated codes proofed feasible. The resultant SALT procedure code gives the opportunity to harmonize big data sets containing surgical procedures from international centres, and may simplify comparability of future international trial findings.Trial registrationThe study was registered at clinicaltrials.gov under NCT03353532 on November 27th, 2017.

Semantic textual similarity for modern standard and dialectal Arabic using transfer learning.

Semantic Textual Similarity (STS) is the task of identifying the semantic correlation between two sentences of the same or different languages. STS is an important task in natural language processing because it has many applications in different domains such as information retrieval, machine translation, plagiarism detection, document categorization, semantic search, and conversational systems. The availability of STS training and evaluation data resources for some languages such as English has led to good performance systems that achieve above 80% correlation with human judgment. Unfortunately, such required STS data resources are not available for many languages like Arabic. To overcome this challenge, this paper proposes three different approaches to generate effective STS Arabic models. The first one is based on evaluating the use of automatic machine translation for English STS data to Arabic to be used in fine-tuning. The second approach is based on the interleaving of Arabic models with English data resources. The third approach is based on fine-tuning the knowledge distillation-based models to boost their performance in Arabic using a proposed translated dataset. With very limited resources consisting of just a few hundred Arabic STS sentence pairs, we managed to achieve a score of 81% correlation, evaluated using the standard STS 2017 Arabic evaluation set. Also, we managed to extend the Arabic models to process two local dialects, Egyptian (EG) and Saudi Arabian (SA), with a correlation score of 77.5% for EG dialect and 76% for the SA dialect evaluated using dialectal conversion from the same standard STS 2017 Arabic set.

Selecting a set of semantic labels to eliminate ambiguity for Vietnamese

The rapid development of automatic control systems in natural language, automatic translation systems based on semantic statistics have been receiving much attention from computer science researchers. However, this method requires a large bilingual corpus and accurate semantic tagging, the construction of which requires a lot of time and effort, because of the ambiguity of the natural language. For Vietnamese, automatic question-and-answer systems are increasingly developing in Vietnam, but the problem of semantic ambiguity has not yet received much attention from domestic studies. In this paper, we build a model to evaluate and select an effective and reasonable set of semantic labels from 3 commonly used sets of semantic labels: LLOCE (Longman Lexicon of Contemporary English), LDOCE (Longman Dictionary) of Contemporary English) and WordNet. And then, select the appropriate set of labels, apply it to automatic semantic labeling systems for Vietnamese, help eliminate semantic ambiguity, and support automatic translation, automatic question-and-answer systems efficiently.

Application of Convolutional Neural Network Based on Deep Learning in College English Translation Teaching Management

With the increased demand for English translators in recent years, more scholars and researchers have begun to focus on college English translation education. The demand for computers to perform automatic natural language translation is at an all-time high, presenting another potential for machine translation (MT) research. Although neural machine translation (NMT) technology has advanced significantly, it still has several flaws. The reply NN-based MT technology is not optimal in the field of long sentence translation, and there are situations such as missing translation and overtranslation. So this paper proposes college English translation based on a convolutional neural network (CNN). This paper proposes that CNNs have stronger feature extraction and information processing capabilities than other deep NNs. It successfully solves the situation of missing translation and overtranslation, lowering the quality of college English translation instruction. The experimental results of this research reveal that when the length of an English sentence grows longer, the translation accuracy based on the reply NN drops to 47.9%, while the translation accuracy based on the CNN is 89.7%. It can be seen that college English translation based on CNN can be integrated into English translation teaching as an innovation of traditional teaching methods, so as to create a learning environment for students and enhance learning effects. MT based on CNN has greatly improved students’ performance, which is conducive to improving the quality of college English translation teaching.

A Corpus for Evaluation of Cross Language Text Re-use Detection Systems

In recent years, the availability of documents through the Internet along with automatic translation systems have increased plagiarism, especially across languages. Cross-lingual plagiarism occurs when the source or original text is in one language and the plagiarized or re-used text is in another language. Various methods for automatic text re-use detection across languages have been developed whose objective is to assist human experts in analyzing documents for plagiarism cases. For evaluating the performance of these systems and algorithms, standard evaluation resources are needed. To construct cross lingual plagiarism detection corpora, the majority of earlier studies have paid attention to English and other European language pairs, and have less focused on low resource languages. In this paper, we investigate a method for constructing an English-Persian cross-language plagiarism detection corpus based on parallel bilingual sentences that artificially generate passages with various degrees of paraphrasing. The plagiarized passages are inserted into topically related English and Persian Wikipedia articles in order to have more realistic text documents. The proposed approach can be applied to other less-resourced languages. In order to evaluate the compiled corpus, both intrinsic and extrinsic evaluation methods were employed. So, the compiled corpus can be suitably included into an evaluation framework for assessing cross-language plagiarism detection systems. Our proposed corpus is free and publicly available for research purposes.

The translator, an endangered species?

The first public machine translation experiment took place in 1954. Led by researchers from IBM and Georgetown University in Washington, DC, it was destined to make possible high-quality automatic translation from Russian into English in a few years. Since this first attempt, claims that machines could soon be replacing translators became usual. In 2018, Microsoft announced that their Chinese to English news translations were of comparable quality to human translation. The paradox however, is that although translation systems are accessible to most people, the number of people working in the translation industry is higher than ever before – an approximate 600,000 people in the world. In this context, do professionals really have a reason to worry?

A prototype Malayalam to Indian Sign Language (ISL) Automatic Translator

Sign Language is one of the major mediums of communication for deaf people. It uses gestures which makes use of hand, facial expression and other body parts to convey meaning. The use of parallel modification as opposed to the sequential nature of spoken language makes it difficult for efficient representation. This calls for the development of animation techniques for representing signs which can easily model human gestures. This paper presents a prototype Malayalam language text to Indian Sign Language (ISL) automatic translation system. The proposed system takes Malayalam text as input and generates corresponding Indian Sign Language (ISL) signs as output. Output animation is rendered using a computer-generated model. This system will help to disseminate information to deaf people in public utility places like railways, banks, hospitals etc. This will also act as an educational tool.

Unreferenced English articles' translation quality-oriented automatic evaluation technology using sparse autoencoder under the background of deep learning.

Currently, both manual and automatic evaluation technology can evaluate the translation quality of unreferenced English articles, playing a particular role in detecting translation results. Still, their deficiency is the lack of a close or noticeable relationship between evaluation time and evaluation theory. Thereupon, to realize the automatic Translation Quality Assessment (TQA) of unreferenced English articles, this paper proposes an automatic TQA model based on Sparse AutoEncoder (SAE) under the background of Deep Learning (DL). Meanwhile, the DL-based information extraction method employs AutoEncoder (AE) in the bilingual words’ unsupervised learning stage to reconstruct the translation language vector features. Then, it imports the translation information of unreferenced English articles into Bilingual words and optimizes the extraction effect of language vector features. Meantime, the translation language vector feature is introduced into the automatic DL-based TQA. The experimental findings corroborate that when the number of sentences increases, the number of actual translation errors and the evaluation scores of the proposed model increase, but the Bilingual Evaluation Understudy (BLEU) score is not significantly affected. When the number of sentences increases from 1,000 to 6,000, the BLEU increases from 96 to 98, which shows that the proposed model has good performance. Finally, the proposed model can realize the high-precision TQA of unreferenced English articles.

Blockchain Smart Contract to Prevent Forgery of Degree Certificates: Artificial Intelligence Consensus Algorithm

Certificates are often falsified, such as fake diplomas and forged transcripts. As such, many schools and educational institutions have begun to issue diplomas online. Although diplomas can be issued conveniently anytime, anywhere, there are many cases wherein diplomas are forged through hacking and forgery. This paper deals with the required Blockchain diploma. In addition, we use an automatic translation system, which incorporates natural language processing, to perform verification work that does not require an existing public certificate. The hash algorithm is used to authenticate security. This paper also proposes the use of these security protocols to provide more secure data protection. In addition, each transaction history, whether a diploma is true or not, may be different in length if it is presented in text, but converting it into a hash function means that it is always more than a certain length of SHA-512 or higher. It is then verified using the time stamp values. These chaining codes are designed. This paper also provides the necessary experimental environment. At least 10 nodes are constructed. Blockchain platform development applies and references Blockchain standardization, and a platform test, measurement test, and performance measurement test are conducted to assess the smart contract development and performance measurement. A total of 500 nodes were obtained by averaging 200 times, and a Blockchain-based diploma file was agreed upon at the same time. It shows performance information of about 4100 TPS. In addition, the analysis of artificial intelligence distribution diagram was conducted using a four-point method, and the distribution chart was evenly distributed, confirming the diploma with the highest similarity. The verified values were then analyzed. This paper proposes these natural language processing-based Blockchain algorithms.