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检索条件"任意字段=6th International Conference on Machine Learning and Natural Language Processing, MLNLP 2023"

共 688 条记录，以下是11-20 订阅

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"study on the Grammatical Function of "la dhon"as Function Words in the Tibetan language processing" 6

"study on the Grammatical Function of "la dhon"as Function W...

引用

6th international conference on machine learning and natural language processing, mlnlp 2023

作者： Suonan, Cairang Gongque, Duojie Computer College of Qinghai Normal University Qinghai Xining810016 China Key Laboratory of Tibetan Information Processing Ministry of Education Qinghai Xining810008 China Tibetan Intelligent Information Processing and Machine Translation Key Laboratory Qinghai Xining810008 China

ISBN: (纸本)9798400709241

the study of Tibetan function words is an indispensable basic work in Tibetan natural language processing and has a wide range of practical application value. It is the core of Tibetan information processing and the basis of Tibetan natural language understanding, and has wide application prospects in Tibetan proofreading, information retrieval, bilingual translation, automatic classification and other technologies. On the basis of previous studies, this paper discusses the grammatical functions of Tibetan function words la dhon from the perspective of information processing, classification system and formal description of unfree function words, focuses on the grammatical functions of Tibetan function words la dhon, and puts forward the preliminary conception and processing methods of Tibetan function words. © 2023 ACM.

关键词： natural language processing systems

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Multivariate Time Series Co-evolution Shapelet learning Method 6

Multivariate Time Series Co-evolution Shapelet Learning Meth...

引用

6th international conference on machine learning and natural language processing, mlnlp 2023

作者： Wang, Ling Chu, Zhongkun Liu, Wei School of Automation and Electrical Engineering University of Science and Technology of Beijing Key Laboratory of Knowledge Automation for Industrial Process Ministry of Education Beijing China Shunde Innovation School University of Science and Technology Beijing Guangdong528399 China

ISBN: (纸本)9798400709241

Shapelets are discriminative subsequences in time series that effectively capture local shape characteristics. However, the time complexity of the existing shapelet extraction process remains high. To address this issue, this paper proposes an unsupervised shapelet extraction algorithm. the algorithm utilizes the coherence of multiple wavelets and constraints on multivariate collaborative evolution intervals to constrain the generation range of shapelets, thereby improving the efficiency of shapelet learning while preserving their significance. the superiority of the proposed algorithm is validated through experiments conducted on the UEA dataset. © 2023 ACM.

关键词： Extraction

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A Pedestrian Attribute Recognition Method Based on Cross-modal Fusion 6

A Pedestrian Attribute Recognition Method Based on Cross-mod...

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6th international conference on machine learning and natural language processing, mlnlp 2023

作者： Gu, Bing Liu, Wenxuan Li, Longfei Zhao, Lingyun Wu, Suwei Shandong University of Science and Technology TaiAn China

ISBN: (纸本)9798400709241

Pedestrian Attribute Recognition (PAR), propelled by advancements in computer vision and deep learning, involves predicting attributes to describe pedestrians, including gender, age, clothing, and posture. Recent research has unveiled the oversight of valuable textual information within attribute annotations by many traditional methods. To address this issue, we proposed a cross-modal fusion model integrating image-Textual features to enhance recognition capabilities. the model primarily consists of three parts: An image feature extraction module, a textual processing module, and a cross-modal fusion module, each serving its specific function. the dataset utilized in this paper is PA-100K which is the largest PAR dataset to date. Extensive experiments were conducted to demonstrate the performance disparity between the cross-modal approach and the only-visual method, confirming the necessity of integrating cross-modal fusion in pedestrian attribute recognition. © 2023 ACM.

关键词： Deep learning

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Device Health Management Based on machine learning 6

Device Health Management Based on Machine Learning

引用

6th international conference on natural language processing (ICNLP)

作者： Ma, Jiao Li, Bo Li, Yuji Qiao, Yani Xian Univ Posts & Telecommun Sch Commun & Informat Engn Xian Peoples R China

ISBN: (纸本)9798350349122;9798350349115

Battery life is a crucial factor in assessing the longevity of electronic devices and machines. To enhance the management of machine health, we propose a neural network-based method for predicting battery life in machine learning. Neural networks,a key aspect of machine learning, simulate the human brain's neural network to improve existing performance. Battery capacity,a visual indicator of battery life, is essential for the safe operation of machines. In this study, we predict battery capacity to estimate remaining battery life. We use a NASA-provided lithiumbattery dataset containing time-series data from multiple sensors and labeling information related to battery state. these sensors measure various battery parameters, such as voltage. Our method, implemented using the MATLAB neural network toolbox, provides a visual display of the predicted battery decay curve, aiding industrial production judgment and algorithm verification. Additionally, our approach, developed for a specific battery device, has the potential for broader application in other electronic and electric devices.

关键词： machine learning neural network modeling health management life expectancy prediction

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Research on Multi-objective Optimal Scheduling Model of Industrial Text Information Based on machine learning 6

Research on Multi-objective Optimal Scheduling Model of Indu...

引用

6th international conference on natural language processing (ICNLP)

作者： Gao, Lili Ma, Shuaiyin He, Jinfeng Yin, Lei Kong, Xianguang Luo, Jianchao Xian Univ Posts & Telecommun Sch Comp Sci Xian Peoples R China Jingye Iron & Steel Co Shijiazhuang Hebei Peoples R China Xidian Univ Sch Electromech Engn Xian Peoples R China Northwestern Polytech Univ Shenzhen Res & Dev Inst Shenzhen Peoples R China

ISBN: (纸本)9798350349122;9798350349115

Aiming at flexible flow shop scheduling (FFSP) in industrial production process, this study introduces machine learning technology to build a multi-objective optimization algorithm model based on industrial text information. In order to improve the efficiency of industrial production and reduce energy consumption, an architecture based on Cyber-Physical System is proposed. Industrial text information is analyzed and extracted by natural language processing technology to obtain key information for multi-objective optimization in industrial production process. Within this framework, a problem for solving complex multi-objective optimization challenges in FFSP using Non-dominated Sorting Genetic Algorithm II (NSGA-II) is proposed. Finally, the validity of our proposed method is verified by using the industrial production process data of Hebei Jingye Group.

关键词： industrial text information Multi-objective optimization natural language processing (NLP) Non-dominated Sorting Genetic Algorithm II (NSGA-II) machine learning

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Linguistic-Aware WordPiece Tokenization: Semantic Enrichment and OOV Mitigation 6

Linguistic-Aware WordPiece Tokenization: Semantic Enrichment...

引用

6th international conference on natural language processing (ICNLP)

作者： Schoenle, Daniel Reich, Christoph Abdeslam, Djaffar Ould Furtwangen Univ IDACUS Inst Furtwangen Germany Univ Haute Alsace Inst IRIMAS Mulhouse France

ISBN: (纸本)9798350349122;9798350349115

the LinPair approach introduces an approach to tokenization, tackling challenges in processing textual data for machine learning applications. this method refines the Word-Piece Tokenization technique by incorporating linguistic insights into token-tag pairs, thereby enriching Large language Models (LLMs) with syntactic information. LinPair Tokenization is articulated through two distinct strategies: CompleteLinPair Tokenization, which is tailored for smaller datasets and enriches each token with part-of-speech (POS) information, and SmartLinPair Tokenization, designed to mitigate the impact of out-of-vocabulary (OOV) occurrences. the experimental analysis indicates that SmartLinPair Tokenization improves text classification tasks with BERT, showing an increase in F1-Score of up to 13.9% compared to a WordPiece baseline. the CompleteLinPair Tokenization method results in a decrease in classification performance. It is important to note that these findings are experiment specific and may not be generalizable to other contexts.

关键词： machine learning nlp tokenization llm linguistic

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Cancer Survival Prediction by Multimodal Disentangled Representation learning 6

Cancer Survival Prediction by Multimodal Disentangled Repres...

引用

6th international conference on machine learning and natural language processing, mlnlp 2023

作者： Xu, Ying Liu, Honglei Shi, Yi Li, Ao Wang, Minghui School of Information Science and Technology University of Science and Technology of China China

ISBN: (纸本)9798400709241

Precise prediction of cancer survival is of paramount importance in aiding clinicians in formulating tailored treatment strategies. Such strategies have the potential to enhance the quality of life for individuals with cancer and lower the mortality rates associated with this disease. Recent research has underscored the significance of integrating data from various sources, including genomics and histopathologic images, as a pivotal step towards enhancing the accuracy of cancer survival prediction. Although these studies have achieved promising results, utilizing the consensus and complementarity of multimodal data for efficient learning of comprehensive multimodal representations poses an enormous challenge. In response to this challenge, we propose a Multimodal Disentangled Representation learning (MMDR) method, aimed at acquiring modality-specific and shared representations, thus affording a comprehensive perspective of multimodal data. Empirical findings confirm that this approach yields notable enhancements in performance when compared to alternative methodologies. © 2023 ACM.

关键词： Genome

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natural language processing (NLP) Techniques: Usability in Human-Computer Interactions 6

Natural Language Processing (NLP) Techniques: Usability in H...

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6th international conference on natural language processing (ICNLP)

作者： Das, Satyesh Das, Divyesh Rajiv Gandhi Inst Petr Technol Comp Sci & Engn Amethi Uttar Pradesh India Thapar Inst Engn & Technol Elect & Comp Engn Patiala Punjab India

ISBN: (纸本)9798350349122;9798350349115

the study sheds light on the concept of natural language processing (NLP) which enables Human-Computer Interaction (HCI). Recent advances in Artificial Intelligence (AI) and brain architecture have led to significant advancements in NLP. these upgrades have made it possible to use more recent dialect models and computations. machines are now capable of understanding and producing human language with astounding accuracy and unmatched attentiveness due to the development of deep learning and the availability of large datasets. Evaluating the role of NLP in improving human-computer interaction is the main purpose of this paper. In regard to this, secondary methods have been used for data collection as well as thematic analysis process has been adopted for interpreting data. As results, it has been identified that NLP techniques enable computerised opinion research, supporting organisations in making information-driven decisions and boosting customer loyalty.

关键词： human-computer interaction NLP machine translation language complexity

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Optimizing natural language Understanding: A Comprehensive Study of Python's Top Libraries 6

Optimizing Natural Language Understanding: A Comprehensive S...

引用

6th international conference on Cybernetics, Cognition and machine learning Applications, ICCCMLA 2024

作者： Pandurangaraju, B. Vijayalakshmi, Balaraju Kumar, S. Ashok Reddy, K. Uday Kumar Karimullah, Shaik Shaik, Fahimuddin Annamacharaya University Department of Artificial Intelligence and Data Science India Mt. Poinsur S.V.P. Road Borivali West Mumbai400103 India Annamacharya university Ai&ds Department Rajampet India Annamacharya university Artificial intelligence and data science department Rajampet India Annamacharya university Ece Department Rajampet India

ISBN: (纸本)9798331505790

Competent human-computer communication relies heavily on the ability to master natural language, where a strong understanding of grammar and context is crucial. As conversational interactions move beyond traditional interfaces, the integration of speech and text into daily communication systems is becoming seamless. At the forefront of this transformation is natural language processing (NLP), the field that enables machines to comprehend, interpret, and respond to human language in a meaningful way. this study explores the evolving landscape of NLP, with a particular emphasis on the Python ecosystem and its libraries, which are essential tools for advancing language comprehension and processing. Python, due to its versatility and robust community support, has become a cornerstone in the development of NLP applications. By comparing and analyzing these Python libraries, this research offers a detailed examination of their functionalities, performance, and adaptability to various NLP tasks. © 2024 IEEE.

关键词： Artificial Intelligence machine learning natural language processing Python Python Libraries

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Few-shot learning with Prompting Methods 6

Few-shot Learning with Prompting Methods

引用

6th international conference on Pattern Recognition and Image Analysis (IPRIA)

作者： Bahrami, Morteza Mansoorizadeh, Muharram Khotanlou, Hassan Bu-Ali Sina University Department of Computer engineering Hamedan Iran

ISBN: (纸本)9798350314557

Today, in natural language processing, labeled data is important, however, getting adequate amount of data is a challenging step. there are many tasks for which it is difficult to obtain the required training data. For example, in machine translation, we need to prepare a lot of data in the target language, so that the work performance is acceptable. We may not be able to collect useful data in the target language. Hence, we need to use few-shot learning. Recently, a method called prompting has evolved, in which text inputs are converted into text with a new structure using a certain format, which has a blank space. Given the prompted text, a pre-trained language model replaces the space with the best word. Prompting can help us in the field of few-shot learning;even in cases where there is no data, i.e. zero-shot learning. Recent works use large language models such as GPT-2 and GPT-3, with the prompting method, performed tasks such as machine translation. these efforts do not use any labeled training data. But these types of models with a massive number of parameters require powerful hardware. Pattern-Exploiting Training (PET) and iterative Pattern-Exploiting Training (iPET) were introduced, which perform fewshot learning using prompting and smaller pre-trained language models such as Bert and Roberta. For example, for the Yahoo text classification dataset, using iPET and Roberta and ten labeled datasets, 70% accuracy has been reached. this paper reviews research works in few-shot learning with a new paradigm in natural language processing, which we dub prompt-based learning or in short, prompting.

关键词： few-shot learning natural language processing prompting prompt-based learning pre-trained language model

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