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检索条件"任意字段=Conference on empirical methods in natural language processing"

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UTILIZING SYNTHETIC DATA FOR VV&C OF MACHINE LEARNING APPLICATIONS

UTILIZING SYNTHETIC DATA FOR VV&C OF MACHINE LEARNING APPLIC...

引用

Integrated Communication, Navigation and Surveillance conference (ICNS)

作者： Fox, Kevin L. Niewoehner, Kevin R. Rahmes, Mark D. Razdan, Rahul L3Harris Technol Melbourne FL 32919 USA Razdan Res Inst Ocala FL USA

ISBN: (纸本)9781665484190

Machine Learning (ML) offers a revolutionary method to build algorithms from data. This data-driven methodology has proven to be very effective in progressing fields ranging from natural language processing to autonomy. Simultaneously, the technique has also opened significant challenges in system validation with important issues when used in safety-critical systems. If data forms function, how does one know the generated function is safe? Further, fundamental to the ML approach is the availability of vast amounts of data for training a ML model. For a variety of important applications, the availability of data is challenging. In this paper, we propose a unique computational paradigm which combines the inherent advantages of ML with algorithmic transformational methods. Using this technique, one can generate a large amount of synthetic data which preserve the "signature" of the original dataset while building variations that aid the ML training process. Further, the nature of the algorithmic transformations offers insights into the validation and verification (V&V) scenario generation process. An example is provided of the methodology exercised in a maintenance application for the infrastructure delivering the safety critical Automatic Dependent Surveillance-Broadcast (ADS-B) services in the United States (US).

关键词： Training Machine learning algorithms Navigation Surveillance Machine learning Maintenance engineering natural language processing

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Machine Learning-based Automatic language Identification forIndian languages

Machine Learning-based Automatic Language Identification for...

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IoT, Communication and Automation Technology (ICICAT), International conference on

作者： Enjula Uchoi Utkarsh Solanki Udit Bhandral Sahil Patiyal Sahil Dogra Hargun Singh Khera Computer Science and Engineering Lovely Professional University Jalandhar India

ISBN: (数字)9798350368109

ISBN: (纸本)9798350368116

In multilingual civilizations like India, where many languages coexist, language identification is an essential problem in natural language processing. This study investigates the effectiveness of supervised learning techniques for language identification across a variety of Indian languages, including English, Hindi, Tamil, Punjabi, Kannada, Kangri, Assamese, Kokborok, Telugu, Marathi, Nepali, Malayalam, Bengali, and Oriya. These techniques include N-gram models, Naive Bayes (NB), Support Vector Machines, and Convolutional Neural Networks. The study makes use of a corpus that comprises representative text samples from every language and assesses how well the previously discussed techniques work in correctly categorizing text into the appropriate language. The results show that the various algorithms are not all that effective, with CNN showing promise in managing the intricacies of Indian languages. In addition, the study looks into how N-gram size affects classification accuracy and considers possible improvements to boost language recognition systems’ effectiveness in multilingual settings. Overall, by providing insights into efficient methods for automatic language identification across Indian languages, this research advances language technology in a variety of linguistic environments.

关键词： Support vector machines Training Smoothing methods Accuracy Supervised learning natural language processing Bayes methods Multilingual Naive Bayes methods Convolutional neural networks

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Breaking language Barriers for Real-Time Translation Solutions in Tourism Using Cloud Computing and AI 3

Breaking Language Barriers for Real-Time Translation Solutio...

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3rd International conference on Intelligent Systems, Advanced Computing, and Communication, ISACC 2025

作者： Jame, S. Lourdu Nageswari, C. Shobana Jayaprakash, S. Punarselvam, E. Meenakshi, R. Muthulekshmi, M. SRM Institute of Science and Technology Kattankulathur Department of Electrical and Electronics Engineering Tamil Nadu Chennai India R.M.D. Engineering College Department of Electronics and Communication Engineering Tamil Nadu Chennai India Sathyabama Institute of Science and Technology School of Electrical and Electronics Engineering Tamil Nadu Chennai India Department of Information Technology Tamil Nadu Namakkal India Chennai Institute of Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Saveetha Institute of Medical and Technical Sciences Saveetha University Saveetha School of Engineering Department of Biomedical Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798331523893

To improve tourism experiences and enable cultural communication in today's worldwide tourism industry, it is essential to communicate effectively across linguistic boundaries. Cloud computing and artificial intelligence (AI) are the focus of this paper to create tourism-specific real-time translation systems. These solutions use cloud computing and advanced AI algorithms to better connect visitors with local businesses and provide accurate and contextually aware translations. Important technical parts include cloud computing for scalability and accessibility, machine learning models for language comprehension, and natural language processing (NLP). It uses empirical research or case study techniques to assess the solutions' practicability, performance, and user acceptability. Through the efficient and effective removal of language barriers, the results highlight the promise of cloud-based AI translation systems to revolutionize the tourist industry. This research investigates real-time translation innovations, highlighting scalability, context awareness, and multilingual support, with implications for future advances in AI and cloud-based tourist applications. Also covered are potential avenues for further study and application, with the growth of the tourist industry's continuous development and use of AI-driven solutions. The proposed approach enhances real-time translation using cloud computing and AI, enabling scalability and context-aware translations. However, there are other challenges, such as overcoming latency and advanced, idiomatic expressions. © 2025 IEEE.

关键词： Tourism

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Pointwise Reliability Assessment: Density vs. Local Fit methods 24

Pointwise Reliability Assessment: Density vs. Local Fit Meth...

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24th IEEE International conference on Data Mining Workshops, ICDMW 2024

作者： Trovao, Joao Henriques, Jorge Petrella, Lorena Trindade, Madalena Loureiro, Joao Center for Informatics and Systems University of Coimbra CISUC Coimbra Portugal

ISBN: (纸本)9798331530631

Artificial intelligence tools have become commonplace across various domains and contexts, most notably decision-making, natural language processing, and computer vision. However, despite their proven performance and rapid development, concerns over the level of trustworthiness of these tools' predictions limit their implementation in critical scenarios, particularly healthcare. While standard metrics such as accuracy and sensitivity may be used to evaluate the performance of a predictive model, they are holistic and inadequate for evaluating individual data points. In addition, they require label knowledge, which may compromise their generalization to unseen instances during model deployment in a real-world *** this in mind, a measure of reliability along each individual prediction adds a layer of security for the end-user, increasing trust in human-AI interactions. Moreover, reliability assessment enables the identification of factors that hinder predictive performance, supporting model *** study analyzes a series of reliability measures applied to a cardiovascular risk assessment model. These measures provide a numerical estimate of the confidence associated with each individual instance in the deployment *** the analyzed methods behaved consistently, the relative class distance method displayed a virtually linear dependency between reliability and accuracy with a smooth instance distribution. The unsupervised, density principle method, mean neighbor similarity, produced the least clear relation. No significant differences were found between the performance of the studied local fit method and the density fit approaches. Population stratification was also possible through the reliability scores. © 2024 IEEE.

关键词： Risk assessment

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Overview of CCL24-Eval Task1: Chinese Frame Semantic Parsing Evaluation 23

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23rd Chinese National conference on Computational Linguistics, CCL 2024

作者： Yang, Peiyuan Li, Juncai Yan, Zhichao Su, Xuefeng Li, Ru School of Computer and Information Technology Shanxi University Shanxi Taiyuan030006 China Key Laboratory of Computational Intelligence and Chinese Information Processing Ministry of Education Shanxi University Shanxi Taiyuan030006 China School of Modern Logistics Shanxi Vocational University of Engineering Science and Technology Shanxi Jinzhong030609 China

ISBN: (纸本)9780000000002

Chinese Frame-semantic Parsing (CFSP) aims to extract fine-grained frame-semantic structures from texts, which can provide fine-grained semantic information for natural language understanding models to enhance their abilities of semantic representations. Based on the CCL-23 CFSP evaluation task, we introduce construction grammar to expand the targets, as basic units activating frames in texts, from word-style to construction-style, and publish a more challenging CFSP evaluation task in CCL-2024. The evaluation dataset consists of 22,000 annotated examples involving nearly 695 frames. The evaluation task is divided into three subtasks: frame identification, argument identification, and role identification, involving two tracks: close track and open track. The evaluation task has attracted wide attention from both industry and academia, with a total of 1988 participating teams. As for the evaluation results, the team from China University of Petroleum won the first place in the closed track with the final score of 71.34, while the team frome Suzhou University won the first place in the open track with the final socre of 48.77. In this article, we reports the key information about the evaluation task, including key concepts, evaluation dataset, top-3 results and corresponding methods. More information about this task can be found on the website of the CCL-2024 CFSP evaluation task 1 © 2024 China National conference on Computational Linguistics.

关键词： Semantics

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TFS: Revisiting Temporal language Grounding from Frequency Spiking Perspective

TFS: Revisiting Temporal Language Grounding from Frequency S...

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International conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Yifan Lyu Hongzhou Wu Lixiang Liu Chuxiong Sun Institute of Software Chinese Academy of Sciences University of Chinese Academy of Sciences Beijing China Parallel and Distributed Processing Lab National University of Defense Technology Changsha China Institute of Software Chinese Academy of Sciences Beijing China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Temporal language Grounding (TLG) aims to localize moments in untrimmed videos that are most relevant to natural language queries. While existing weakly-supervised methods have achieved significant success in exploring cross-modal relationships, they still face a critical bottleneck: the interference of task-irrelevant information in query embeddings. To address this issue, we propose TLG Frequency Spiking (TFS), a dimensional mask derived from the frequency domain that models the varying importance specific to different queries. By enhancing the understanding of queries, TFS effectively optimizes the cross-modal alignment of visual and textual modalities. Experimental results show that TFS significantly outperforms state-of-the-art baselines on both the Charades-STA and ActivityNet-Captions datasets.

关键词： Visualization Grounding Frequency-domain analysis Neural networks natural languages Interference Signal processing Speech processing Faces Videos

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ALE: A Simulation-Based Active Learning Evaluation Framework for the Parameter-Driven Comparison of Query Strategies for NLP 1

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4th International conference on Deep Learning Theory and Applications (DeLTA)

作者： Kohl, Philipp Freyer, Nils Kraemer, Yoka Werth, Henri Wolf, Steffen Kraft, Bodo Meinecke, Matthias Zuendorf, Albert FH Aachen Univ Appl Sci D-52428 Julich Germany Univ Kassel D-34121 Kassel Germany Laizee Ai Cologne Germany

ISBN: (数字)9783031390593

ISBN: (纸本)9783031390586;9783031390593

Supervised machine learning and deep learning require a large amount of labeled data, which data scientists obtain in a manual, and time-consuming annotation process. To mitigate this challenge, Active Learning (AL) proposes promising data points to annotators they annotate next instead of a subsequent or random sample. This method is supposed to save annotation effort while maintaining model performance. However, practitioners face many AL strategies for different tasks and need an empirical basis to choose between them. Surveys categorize AL strategies into taxonomies without performance indications. Presentations of novel AL strategies compare the performance to a small subset of strategies. Our contribution addresses the empirical basis by introducing a reproducible active learning evaluation (ALE) framework for the comparative evaluation of AL strategies in NLP. The framework allows the implementation of AL strategies with low effort and a fair data-driven comparison through defining and tracking experiment parameters (e.g., initial dataset size, number of data points per query step, and the budget). ALE helps practitioners to make more informed decisions, and researchers can focus on developing new, effective AL strategies and deriving best practices for specific use cases. With best practices, practitioners can lower their annotation costs. We present a case study to illustrate how to use the framework.

关键词： Active learning Query learning natural language processing Deep learning Reproducible research

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PLP 2021: Workshop on Programming language processing 21

PLP 2021: Workshop on Programming Language Processing

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27th ACM SIGKDD International conference on Knowledge Discovery and Data Mining (KDD)

作者： Xu, Chang Ma, Siqi Lo, David Univ Sydney Sydney NSW Australia Univ Queensland Brisbane Qld Australia Singapore Management Univ Singapore Singapore

ISBN: (纸本)9781450383325

The first international Workshop on Programming language processing presents interdisciplinary contributions that address programming language procession problems with machine learning and data mining techniques. Recently, there are lots of successful natural language processing methods. But the mining of programming languages could not exactly follow the manner of natural language processing. The difference between natural language and programming language brings in new research challenges and opportunities. The workshop will bring together researchers from machine learning, data mining and software engineering to discuss and debate the path forward for mining the value of programming languages.

关键词： Programming language natural language processing machine learning software engineering data mining

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Semantic Exploration from language Abstractions and Pretrained Representations 36

Semantic Exploration from Language Abstractions and Pretrain...

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36th conference on Neural Information processing Systems (NeurIPS)

作者： Tam, Allison C. Rabinowitz, Neil C. Lampinen, Andrew K. Roy, Nicholas A. Chan, Stephanie C. Y. Strouse, D. J. Wang, Jane X. Banino, Andrea Hill, Felix DeepMind London England

ISBN: (纸本)9781713871088

Effective exploration is a challenge in reinforcement learning (RL). Novelty-based exploration methods can suffer in high-dimensional state spaces, such as continuous partially-observable 3D environments. We address this challenge by defining novelty using semantically meaningful state abstractions, which can be found in learned representations shaped by natural language. In particular, we evaluate vision-language representations, pretrained on natural image captioning datasets. We show that these pretrained representations drive meaningful, task-relevant exploration and improve performance on 3D simulated environments. We also characterize why and how language provides useful abstractions for exploration by considering the impacts of using representations from a pretrained model, a language oracle, and several ablations. We demonstrate the benefits of our approach with on- and off-policy RL algorithms and in two very different task domainsone that stresses the identification and manipulation of everyday objects, and one that requires navigational exploration in an expansive world. Our results suggest that using language-shaped representations could improve exploration for various algorithms and agents in challenging environments.

关键词： Reinforcement learning

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Enhancing Crisis-Related Tweet Classification with Entity-Masked language Modeling and Multi-Task Learning 2

Enhancing Crisis-Related Tweet Classification with Entity-Ma...

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2nd Workshop on NLP for Positive Impact, NLP4PI 2022 held in conjunction with the 2022 conference on empirical methods in natural language processing, EMNLP 2022

作者： Seeberger, Philipp Riedhammer, Korbinian Technische Hochschule Nürnberg Georg Simon Ohm Germany

ISBN: (纸本)9781959429197

Social media has become an important information source for crisis management and provides quick access to ongoing developments and critical information. However, classification models suffer from event-related biases and highly imbalanced label distributions which still poses a challenging task. To address these challenges, we propose a combination of entity-masked language modeling and hierarchical multi-label classification as a multi-task learning problem. We evaluate our method on tweets from the TREC-IS dataset and show an absolute performance gain w.r.t. F1-score of up to 10% for actionable information types. Moreover, we found that entity-masking reduces the effect of overfitting to in-domain events and enables improvements in cross-event generalization. Our source code is publicly available on GitHub. © 2022 Association for Computational Linguistics.

关键词： Classification (of information)

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