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检索条件"任意字段=International Conference on Natural Language Processing and Knowledge Engineering"

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Using Multinomial Naive Bayes Machine Learning Method to Classify, Detect, and Recognize Programming language Source Code 23

Using Multinomial Naive Bayes Machine Learning Method to Cla...

引用

23rd international Arab conference on Information Technology, ACIT 2022

作者： Odeh, Ayman Hussien Odeh, Munther Odeh, Nada Science- College of Engineering Al Ain University Software Engineering and Computer Al Ain United Arab Emirates Oldenburg University Department of Mathematics and Science Oldenburg Germany

ISBN: (纸本)9798350320244

processing programming languages are very similar to processing natural languages, especially high-level languages such as Python, Java, C#, C, C++, and others. Therefore, the natural language processing concepts can be applied as one of the most important branches of artificial intelligence in detecting, recognizing, and classification scripts written in different programming languages. The programming language script classification can be counted as a classical machine learning problem. This research aims to present a model using Multinomial Naïve Bayes (MNB) artificial intelligence algorithm to identify and classify the programming language used in writing the source code file provided as an input for the proposed model. A set of categorized files containing source codes will be used in training the proposed model, and then the model will be able to automatically detect and classify a new script into one of the already trained categories. The machine learning method called NB Multinomial will be used to implement this matter. This work is very important for Mufti-programming language editors such as Visual Studio Code, Notepad+, and others, where the user can paste the source code, and the editor will recognize the programming language automatically. © 2022 IEEE.

关键词： Machine learning

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natural language processing of Text-Based Metrics for Image Captioning 1

引用

international conference on Intelligent Systems and Sustainable Computing, ICISSC 2021

作者： Sengan, Sudhakar Sagar, P. Vidya Saravanan, N.P. Amarendra, K. Subburaj, Arjun Maheswari, S. Rangasamy, Rajasekar Department of Computer Science and Engineering PSN College of Engineering and Technology Tamil Nadu Tirunelveli627152 India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation A.P. Vaddeswaram India Department of Computer Science and Engineering Kongu Engineering College Tamil Nadu Perundurai638060 India Tranxit Technology Solutions Pvt. Ltd. Tamil Nadu Chennai India Department of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Chennai India Department of Computer Science and Engineering-AI & ML School Engineering Malla Reddy University Hyderabad India

ISBN: (数字)9789811900112

ISBN: (纸本)9789811900105

A rapidly increasing interest has been focused on natural language processing and computer vision study by automatically producing descriptive sentences for images in present trends. Image processing is a success factor, especially involving somaticized image knowledge and creating reliable, appropriately organized explanation phrases. In this paper, a dynamic program that uses the VCG16 platform to develop image descriptions and a long short-term memory (LSTM) to contain appropriate words with generated text keywords successfully is proposed. This paper determines the NLP method’s usefulness using Flickr8 K-statistical models and demonstrates that their model delivers accurate performance compared to the Bleu metric. The Bleu metric is an automated system for measuring a machine translation’s performance by monitoring the effectiveness of text transformed from natural to other languages. The results have been justified with images that satisfy the NLP algorithm. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Long short-term memory

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Enhancing language Models Through Prompt engineering - A Survey

Enhancing Language Models Through Prompt Engineering - A Sur...

引用

Intelligent Systems, Smart and Green Technologies (ICISSGT), IEEE international conference on

作者： Chinimilli Venkata Rama Padmaja S Lakshminarayana Computer Science & Engineering Institute of Aeronautical Engineering Hyderabad India Computer Science & Systems Engineering Andhra University Visakhapatnam India

ISBN: (数字)9798350304329

ISBN: (纸本)9798350304336

In recent years, language models have undergone significant advancements with models like GPT-3, showcasing impressive abilities in natural language processing and generation. However, these models often experience from issues such as generating biased or unreliable content, producing inappropriate responses, or failing to fulfill specific user needs. To address these challenges, prompt engineering emerged as a promising approach by providing explicit guidance to language models. It refers to the process of designing and constructing effective prompts or instructions to guide language models like GPT-3 in generating desired outputs. In this article an attempt was made to address the role of prompt engineering, its techniques, and its potential impact on enhancing the performance and reliability of language models.

关键词： Surveys Ethics Technological innovation Privacy Refining Reliability engineering User experience natural language processing Prompt engineering Standards

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Sequence-to-Sequence Multi-Hop knowledge Reasoning Based on Retentive Network

Sequence-to-Sequence Multi-Hop Knowledge Reasoning Based on ...

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Computer Information Science and Application Technology (CISAT), international conference on

作者： Ruilin Su Wanshan Zhang Dunhui Yu Hubei University Wuhan China Wuhan China School of Computer Science and Information Engineering Hubei University Wuhan China Hubei Key Laboratory of Big Data Intelligent Analysis and Application Hubei University Wuhan China Engineering and Technical Research Center of Hubei Province in Software Engineering Wuhan China

ISBN: (数字)9798350375107

ISBN: (纸本)9798350375114

Multi-hop knowledge Reasoning is a task that involves generating an answer given a query and a knowledge graph. Existing sequence-to-sequence reasoning models use the Transformer to encode and decode sequences, but these models have some flaws, such as the inability to effectively handle long-sequence reasoning and susceptibility to exposure bias. To address these issues, we provides a sequence-to-sequence reasoning model named STSR, which is based on Retentive Network. Aiming at improving training efficiency through parallel training, this model leverages the advantages in Retentive Network, reduce time overhead, and enhance reasoning efficiency through iterative reasoning. It effectively mitigates the problems of high spatial overhead and low efficiency in long-sequence reasoning faced by the Transformer. Moreover, it retains and updates historical information during the encoding and decoding process, enhancing the model’s memory and generalization capabilities. Additionally, the Scheduled Sampling method is adopted to alleviate the exposure bias introduced during reasoning, which involves using the model’s own output as the input for the next moment with a certain probability during training, instead of using the true label. We conduct the experiment on six public datasets, with the results showing that the proposed model outperforms existing baseline models in terms of precision and generation quality. This paper provides a new solution for the task of sequence-to-sequence multihop knowledge reasoning and also demonstrates the potential application of the Retentive Network in natural language processing.

关键词： Training knowledge engineering Accuracy Spread spectrum communication knowledge graphs Transformers Sampling methods Cognition natural language processing Iterative decoding

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Integration of knowledge-Driven and Data-Driven Based Korean Phonetic Transcription 5

Integration of Knowledge-Driven and Data-Driven Based Korean...

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5th international conference on Pattern Recognition and Artificial Intelligence, PRAI 2022

作者： Cao, Dezhi Wu, Licheng Zhao, Yue Lu, Zhenna Minzu University of China School of Chinese Ethnic Languages and Literature Beijing China Minzu University of China School of Information Engineering Beijing China

ISBN: (数字)9781665499163

ISBN: (纸本)9781665499163

In the construction of resources for Korean phonetic information processing, automatic phonetic transcription technology plays a crucial role. However, because Korean word formation is extremely powerful and new words emerge regularly, it is impossible to create a database that contains all of the words. As a result, in addition to the words in the database, how to solve the pronunciation annotation of those unregistered words outside the database, referred to as OOV (out of vocabulary) words, has turned into a problem that must be resolved in the process of Korean natural language processing. The current academic approaches to grapheme-to-phoneme (G2P) conversion techniques have been commonly knowledge-based or data-based. Previously, the methods which were only knowledge-driven based are difficult to adapt to the actual situation of a large amount of data information. The data-driven approach relies solely on high-quality data, makes it difficult to reasonably determine the input variables, and necessitates the use of adequate and precisely selected model features. To address these issues, the paper proposes a knowledge-driven and data-driven fusion based an automatic Korean language G2P method. Firstly, we extract eigenvalues based on the pronunciation rules and the phonetic changing rules between words in Korean, and feed them in to the model for training. And then, the model is trained to achieve the automatic phonetic transcription for Korean using the data-driven model, which can better fit the mapping relationship between input and output variables. The proposed model can reflect the phonological changes in the Korean continuous speech stream, and can accurately obtain the phonemes corresponding to the graphemes. The method has been cross-validated for validity and superiority to improve model performance, and the average accuracy on grapheme-to-phoneme conversion can reach 94.63%. © 2022 IEEE.

关键词： Database systems

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Resource Adjustment processing on the DIKWP Artificial Consciousness Diagnostic System 25

Resource Adjustment Processing on the DIKWP Artificial Consc...

引用

25th IEEE international conferences on High Performance Computing and Communications, 9th international conference on Data Science and Systems, 21st IEEE international conference on Smart City and 9th IEEE international conference on Dependability in Sensor, Cloud and Big Data Systems and Applications, HPCC/DSS/SmartCity/DependSys 2023

作者： Wang, Yuxing Duan, Yucong Wang, Minhui Wei, Jiali Zhuang, Chunyu Liu, Yanfei School of Computer Science and Technology Hainan University Dikwp Research Group China Hainan Affiliated Hospital of Hainan Medical College China Haikou Hospital of the Maternal and Child Health China College of Intelligence and Computing Tianjin University China

ISBN: (纸本)9798350330014

Having confusion reduces the efficiency of human communication. Confusion arises from uncertainty during natural language interaction. To solve the confusion of doctor-patient communication in the medical field, we propose resource adjustment on the DIKWP Artificial Consciousness Diagnostic System(DIKWP-ACDS) to deal with uncertainty. With the DIKWP-ACDS, we uniformly map the dialogue text during doctor-patient communication to the Data, Information, knowledge, Wisdom, and Purpose of the DIKWP model. In the process of intra-module and inter-module fusion and conversion of each type of resource, we adopt two methods of migration transformation and bias processing for the problems of incompleteness, inconsistency, and imprecision in the case of uncertainty. Moreover, we validate the effectiveness of the methods by comparing the purpose before and after resource adjustment. © 2023 IEEE.

关键词： Diagnosis

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A Comparative Survey on Parts of Speech Taggers for the Marathi language 4

A Comparative Survey on Parts of Speech Taggers for the Mara...

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4th international conference on Inventive Research in Computing Applications, ICIRCA 2022

作者： Tambat, Ishita Narayankar, Minakshee Vidyalankar Institute of Technology Department of Computer Engineering Mumbai India

ISBN: (数字)9781665497077

ISBN: (纸本)9781665497077

natural language processing relies heavily on the POS tagger. The POS tagger is a useful tool for tagging each word in a phrase with parts of speech tags. NLP Applications performing various tasks use POS tagging as a crucial initial step. In terms of data tagging, the POS tagger for English is widely available, however, there is no similar tagger for Marathi. Marathi is a morphologically complex language with regional speech differences. Because of the ambiguity in the language, as well as its highly inflectional structure and free word order, establishing a successful POS tagger in Marathi is tough. This article provides a comprehensive overview of POS tagging for the Marathi language and its variations. Various POS Tagging models and approaches are investigated in this research. Tokenization in computer languages is similar to tagging in natural language processing. Choosing the appropriate tag for the scenario might be challenging for POS taggers. Research has been conducted to discover a solution to this conundrum. © 2022 IEEE.

关键词： Hidden Markov models

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Deep learning-based Video Captioning Technique using Transformer 8

Deep learning-based Video Captioning Technique using Transfo...

引用

8th international conference on Advanced Computing and Communication Systems, ICACCS 2022

作者： Varma, Soumya Peter, J Dinesh Karunya Institute Technology and Sciences Department of Computer Science and Engineering Tamil Nadu Coimbatore India Karunya Institute Technology and Sciences Department of Cse Tamil Nadu Coimbatore India

ISBN: (纸本)9781665408165

Automatic Video captioning is the process by which a meaningful natural language sentence description is generated for a given video. Video understanding has got several applications in the field of automatic object identification, real-time calamity detection, navigation for visually impaired people, autonomous driving, human robot interaction and many more. The process of video captioning is a tedious task for machines, and it needs a collaborated effort of CV-Computer Vision techniques and NLP-natural language processing techniques. In the last decade, the technologies that were used to perform this task includes CNN, RNN, LSTM, GRU and its variants. Recently the focus has been shifted to Transformer networks which sounds more promising than any of these. This work proposes a transformer-based Video captioning architecture, and the evaluation has been made over standard dataset with metrics and is found to perform superior to existing methods. © 2022 IEEE.

关键词： natural language processing systems

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Intelligent-Based Extraction of Business Capabilities from Company Descriptions 7

Intelligent-Based Extraction of Business Capabilities from C...

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7th international conference on Information Technology, Information Systems and Electrical engineering, ICITISEE 2023

作者： Fahmi, Faisal Hamid, Siti Hafizah Ab Yuadi, Imam Airlangga University Faculty of Political and Social Science Department of Library and Information Science Indonesia Universiti Malaya Faculty of Computer Science & Information Technology Department of Software Engineering Malaysia

ISBN: (纸本)9798350382266

In the era of Big Data, Information can be generated, extracted, and utilized in diverse ways. In business, information about business capabilities can be a crucial aspect in understanding the strengths and competencies of organizations. This Information can empower stakeholders to make informed decisions regarding partnerships, acquisitions, and market positioning. Additionally, by comparing capabilities across companies and industries, organizations can identify their competitive advantages, industry trends, potential disruptions, and effective resource allocations, leading to improved strategies and better market opportunities. Traditional methods for extracting business capabilities primarily rely on keyword matching or rule-based techniques, which often fail to capture the complex relationships between words in a sentence. This paper presents a comprehensive study on extracting business capabilities using dependency parsing techniques in natural language processing (NLP) to overcome limitations of the traditional methods and improve the accuracy of business capability extraction. Dependency parsing utilizes pre-trained language models to analyze the grammatical structure of sentences, identifying the dependencies between words and capturing their relationships. The feasibility and effectiveness of the proposed approach is demonstrated through experiments conducted on more than 40K real-world company descriptions crawled from Wikipedia. © 2023 IEEE.

关键词： Competition

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Design and Implementation of Yipai App Based on Artificial Intelligence Technology 2

Design and Implementation of Yipai App Based on Artificial I...

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2nd international conference on Automation, Robotics and Computer engineering, ICARCE 2023

作者： Zheng, Lanqing Jiang, Pan Kong, Huafeng School of Information Engineering Wuhan Business University Wuhan China

ISBN: (纸本)9798350308341

With the increasing popularity and upgrading of mobile phone cameras, the pixel, sensor, aperture, lens and software algorithms of mobile phone cameras are constantly iterative. Taking photos and sharing life has been integrated into our lives, but there are still many people who cannot master the photography skills proficiently and are at a loss in the face of taking photos, so the market demand for photo systems is large. 'Yipai APP based on artificial intelligence technology' is a photo APP that guides users to take photos and provides booking and requesting photos. Its front-end is mainly developed by the system program using Java language, the back-end uses SQLite to store and manage data, and realizes artificial interaction by using natural language processing technology. Intelligent guidance is provided to users, and AI intelligent matching copy is performed on the photos taken. When the user does not want to take a selfie, this system also provides appointment service, of course, the photographer user can also publish help to take a post, looking for customers. Mobile phone photo APP has a broad prospect, and the function of easy APP is relatively rare in the photo market, which brings great opportunities and markets to the development of easy APP. © 2023 IEEE.

关键词： Android (operating system)

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