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

共 15353 条记录，以下是4901-4910 订阅

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Exploring Advanced Hypothesis Generation in Astronomy Through the Implementation of a Mathematical Model of Linguistic Neural Networks 20

Exploring Advanced Hypothesis Generation in Astronomy Throug...

引用

20th International Scientific conference "Dynamical System Modeling and Stability Investigation", DSMSI 2023 - Volume 1: Mathematical Foundations of Information Technologies

作者： Zaitsev, Ievgen Golubenko, Oleksandr Tkachenko, Olha Pidmohylnyi, Oleksandr Antonenko, Artem Institute of Electrodynamics The National Academy of Sciences of Ukraine Kyiv Ukraine Academician Yury Bugai International Scientific and Technical University Kyiv Ukraine Taras Shevchenko Kyiv National University Kyiv Ukraine State University of Information and Communication Technologies Kyiv Ukraine National University of Life and Environmental Sciences of Ukraine Kyiv Ukraine

The current research conducts an exhaustive analysis into the integration of Large language Models (LLMs), focusing on GPT-4, within the domain of Astronomy. We deploy advanced in-context and adversarial prompting methods, harnessing a database of over 1000 articles from NASA's Astrophysics Data System, aiming to augment the model's performance in domain-specific literature comprehension. This comprehensive inquiry demonstrates a marked enhancement in the model's capability for generating robust and insightful hypotheses in astronomy. Furthermore, the study highlights the role of adversarial prompting in enabling GPT-4 to sift through extensive data to yield meaningful and informed scientific hypotheses, showcasing a pioneering advancement for the implementation of LLMs in Astronomy research. © 2023 Copyright for this paper by its authors.

关键词： natural language processing systems

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A Simple But Powerful Graph Encoder for Temporal Knowledge Graph Completion

A Simple But Powerful Graph Encoder for Temporal Knowledge G...

引用

Intelligent Systems conference (IntelliSys)

作者： Ding, Zifeng Ma, Yunpu He, Bailan Wu, Jingpei Han, Zhen Tresp, Volker Ludwig Maximilians Univ Munchen Munich Germany Siemens AG Munich Germany Tech Univ Munich Munich Germany

ISBN: (纸本)9783031477140;9783031477157

Knowledge graphs contain rich knowledge about various entities and the relational information among them, while temporal knowledge graphs (TKGs) describe and model the interactions of the entities over time. In this context, automatic temporal knowledge graph completion (TKGC) has gained great interest. Recent TKGC methods integrate advanced deep learning techniques, e.g., Transformers, and achieve superior model performance. However, this also introduces a large number of excessive parameters, which brings a heavier burden for parameter optimization. In this paper, we propose a simple but powerful graph encoder for TKGC, called TARGCN. TARGCN is parameter-efficient, and it extensively explores every entity's temporal context for learning contextualized representations. We find that instead of adopting various kinds of complex modules, it is more beneficial to efficiently capture the temporal contexts of entities. We experiment TARGCN on three benchmark datasets. Our model can achieve a more than 46% relative improvement on the GDELT dataset compared with state-of-the-art TKGC models. Meanwhile, it outperforms the strongest baseline on the ICEWS05-15 dataset with around 18% fewer parameters.

关键词： natural language processing Representation learning Temporal knowledge graph

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A Survey of Text Detection Algorithms in Images Based on Deep Learning 4

A Survey of Text Detection Algorithms in Images Based on Dee...

引用

4th International conference on natural language processing, ICNLP 2022

作者： Li, Linna Hu, Cuicui Liu, Ying Xi'an University of Posts and Telecommunications Network Public Opinion Monitoring and Analysis Center Shaanxi Xi'an710121 China Xi'an University of Posts and Telecommunications College Key Laboratory of Electronic Information Application Technology for Scene Investigation Ministry of Public Security Center for Image and Information Processing Shaanxi Xi'an710121 China

ISBN: (数字)9781665495448

ISBN: (纸本)9781665495448

Nowadays, text detection has infiltrated various industries like banking, education, criminal investigation, network public opinion, and more. However, the traditional way of text detection is largely dependent on the characteristics of manual design, which is time-consuming, laborious and inaccuracy. With the emergence of deep learning technology, text detection has already been transformed from text detection in static documents to more practical text detection in natural scenes. To address such challenges as complex background, improper acquisition, and multilingual text in natural scene images, there have been various improved neural network structures constructed. Especially, the performance of text detection in various scenes has been significantly improved. In learning in different scenes are summarized in detail. According to the different text objects in detection, the existing methods are classified into two categories as top-down and bottom-up, and the logic structure, advantages and disadvantages of various algorithms are summarized. In addition, the commonly used data sets and performance evaluation indexes are analyzed and explained, the practical application of text detection in different fields is introduced, and the future trend of research and development are indicated. © 2022 IEEE.

关键词： Deep learning

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Quasi-Orthogonal ECG-Frank XYZ Transformation with Energy-Based Models and Clinical Text 22nd

Quasi-Orthogonal ECG-Frank XYZ Transformation with Energy-Ba...

引用

22nd International conference on Artificial Intelligence in Medicine (AIME)

作者： Simone, Lorenzo Bacciu, Davide Gervasi, Vincenzo Univ Pisa Dept Comp Sci I-56122 Pisa Italy

ISBN: (纸本)9783031665349;9783031665356

The transformation of 12-Lead electrocardiograms to 3D vectorcardiograms, along with its reverse process, offer numerous advantages for computer visualization, signal transmission and analysis. Recent literature has shown increasing interest in this structured representation, due to its effectiveness in various cardiac evaluations and machine learning-based arrhythmia prediction. Current transformation techniques utilize fixed matrices, often retrieved through regression methods which fail to correlate with patient's physical characteristics or ongoing diseases. In this paper, we propose the first quasi-orthogonal transformation handling multi-modal input (12-lead ECG and clinical annotations) through a conditional energy-based model. Within our novel probabilistic formulation, the model proposes multiple transformation coefficients without relying on a single fixed approximation to better highlight relationships between latent factors and structured output. The evaluation of our approach, conducted with a nested cross validation on PTB Diagnostic dataset, showcased improved reconstruction precision across various cardiac conditions compared to state-of-the-art techniques (Kors, Dower, and QSLV).

关键词： generative models in healthcare energy-based models electrocardiography natural language processing signal analysis

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natural language processing (ICNLP), International conference on

作者： Tingwei Yao Fan Jiang Zidong Chen School of Communication and Information Engineering Xi’an University of Posts and Telecommunications Xi’an China

ISBN: (数字)9798350349115

ISBN: (纸本)9798350349122

This paper explores the caching problem with popularity prediction in fog radio access networks (F-RANs), which takes into account the Age of Information (AoI). To achieve a higher cache hit rate and a lower delay, we first employ a user-centric popularity prediction strategy to divide users with similar preferences and contextual information into multiple clusters to improve the regional cache hit rate. By updating the latest content according to the normalized content freshness information, the freshness of already cached content can be guaranteed. Moreover, we introduce a weight coefficient that considers both content popularity and freshness. Finally, to further improve the user quality of experience (QoE) and reduce the delay, we optimize the AoI of the requested content by considering the user requesting access probability and the packet arrival rate. Experimental results show that our proposed strategy can effectively improve the cache hit rate and reduce delay compared to existing methods.

关键词： Simulation Information age natural language processing Delays Quality of experience Radio access networks

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Accurate, Diverse and Multiple Distractor Generation with Mixture of Experts 12th

Accurate, Diverse and Multiple Distractor Generation with ...

引用

12th National CCF conference on natural language processing and Chinese Computing, NLPCC 2023

作者： Qu, Fanyi Wang, Che Wu, Yunfang National Key Laboratory for Multimedia Information Processing Peking University Beijing China School of Computer Science Peking University Beijing China

ISBN: (纸本)9783031446924

Given the background passage, question and answer, Distractor Generation (DG) aims to generate several incorrect options to confuse readers, which is an essential composition to build multiple choice question data. Most of the existing works apply naive methods to obtain multiple outputs with the sacrifice of the generation quality. In this paper, we propose an end-to-end one-to-many generation structure with mixture of experts (MoE) for DG, and explore how different data-to-expert routing strategies of MoE influence the performance of one-to-many generation models. Concretely, the model’s encoder calculates token-level attention vectors to mark important tokens from the source sequence, and the decoder generates multiple results with the guidance of the local attention. Moreover, we propose a minimal loss assignment mechanism and a stable routing strategy for diversity generation. Experimental results demonstrate that our proposed method is able to generate multiple distractors with good interpretability, which greatly outperforms the existing state-of-the-art DG models in quality, and achieves satisfactory lexical and semantic diversity. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Semantics

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DQMix-BERT: Distillation-aware Quantization with Mixed Precision for BERT Compression

DQMix-BERT: Distillation-aware Quantization with Mixed Preci...

引用

2023 IEEE International conference on Systems, Man, and Cybernetics, SMC 2023

作者： Tan, Yan Jiang, Lei Chen, Peng Tong, Chaodong Institute of Information Engineering Chinese Academy of Sciences Beijing China School of Cyber Security University of Chinese Academy of Sciences Beijing China

ISBN: (纸本)9798350337020

Transformer-based architecture models like BERT have performed excellently for various natural language processing (NLP) tasks. However, these models are usually computationally expensive with a large number of parameters. As a result, deploying them in edge devices has become a challenging task. The existing compression work on lower-precision quantization still has a severe accuracy decrease and rarely focuses on the information hidden in the different modules of the model. In this paper, we propose a distillation-aware quantization with mixed precision method combined with quantization and knowledge distillation. We achieve the ultra-low mixed precision quantization with the different sensitivity of different modules of BERT. Moreover, we leverage knowledge distillation to reduce the model accuracy degradation. We extensively test our method on four GLUE tasks. It shows that DQMix-BERT outperforms the other BERT compression methods and even achieves comparable performance to the original BERT model while achieving 8x compression. © 2023 IEEE.

关键词： Bert compression knowledge distillation mixed precision quantization pre-training language model

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Design and Evaluation of Prompts for Large language Model Translations in Specialized Domains

Design and Evaluation of Prompts for Large Language Model Tr...

引用

Information Science and Education (ICISE-IE), International conference on

作者： Ruiyan Yang Ping Wang Xingyu Xiang Jiqiu Gu School of Education Shanghai International Studies University Shanghai China Key Laboratory of Multilingual Education With AI Shanghai International Studies University Shanghai China

ISBN: (数字)9798331506766

ISBN: (纸本)9798331506773

In recent years, Large language Models (LLMs) have made significant breakthroughs in natural language processing. However, effectively applying them in domain-specific translation remains a significant challenge. This study aims to enhance the translation quality of LLMs in specialized domains by investigating the impact of different prompt strategies. We constructed four domain-specific corpora and assessed the efficacy of four prompting strategies—code-based, natural language-based, CRISPE mode, and comprehensive mode—using automated metrics such as BLEU, ChrF, and TER on the ChatGPT 3.5 model. Results indicate that different prompting strategies significantly affect LLM translation quality. The natural language-based and CRISPE mode prompts showed the best performance, providing richer contextual information and clearer task instructions, effectively enhancing the performance of LLMs in specialized domain translations. This study offers crucial empirical evidence to improve translation performance in specific domains and propels new directions for LLM-based translation practice and research.

关键词： Measurement Information science Translation Accuracy Large language models Education Propulsion Chatbots

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MULTIPLE TEMPORAL CONTEXT EMBEDDING NETWORKS FOR UNSUPERVISED TIME SERIES ANOMALY DETECTION 47

MULTIPLE TEMPORAL CONTEXT EMBEDDING NETWORKS FOR UNSUPERVISE...

引用

47th IEEE International conference on Acoustics, Speech and Signal processing (ICASSP)

作者： Li, Hanhui Peng, Xinggan Zhuang, Huiping Lin, Zhiping Sun Yat Sen Univ Shenzhen Campus Shenzhen 518107 Peoples R China Nanyang Technol Univ Sch Elect & Elect Engn Singapore 639798 Singapore

ISBN: (纸本)9781665405409

Unsupervised anomaly detection for time series signals is challenging, due to the imbalanced distribution of data and the lack of ground-truth labels. Current methods on this topic are mainly based on deep neural networks, which are optimized by heuristic constraints or empirical priors. However, various patterns of anomalous data, especially those lasting for varying periods, are hard to be captured by plain networks. To tackle this problem, we propose a multiple temporal context embedding method. The core of our method is to construct a unified representation of the multiple temporal contexts of data, which is achieved by learning a set of base features to reconstruct the hidden features within existing anomaly detection networks. The proposed method can be implemented as a convenient plug-in module, and be combined with various network architectures, such as autoencoders and graph neural networks. Extensive experiments on multiple datasets demonstrate that the proposed method can boost the performance of baseline networks significantly.

关键词： Anomaly Detection Unsupervised Learning Temporal Context Time Series Signal processing

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Resume Analyzer Using natural language processing (NLP)

Resume Analyzer Using Natural Language Processing (NLP)

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Intelligent Systems and Advanced Applications (ICISAA), International conference on

作者： Sarika Pabalkar Prashant Patel Ramila Choudhary Vanshika Panoch Sahil Yadav Harsh Ghogale Department of Information Technology Dr.D.Y.Patil Institute of Technology Pune India Department of Instrumentation Dr.D.Y.Patil Institute of Technology Pune India

ISBN: (数字)9798331539948

ISBN: (纸本)9798331539955

Finding the best candidates for a position and informing users of their resume score and areas for growth are the two main objectives of Resume Screening After a thorough analysis of the literature on current methods, it was found that while traditional systems like manual screening may lead to incorrect assumptions and the wastage of human potential, they are not robust in terms of processing, accuracy, or efficiency. Software must score the resumes of the candidates in real-time and match and rate them using machine learning and natural language processing techniques in order to obtain accuracy. Applications’ resumes would be the input, and suggestions from the user side and an admin-side rated candidate resume list would be the output. By utilizing natural language processing techniques, output results are obtained instantly in real time. TF-IDF, Cosine Similarity NLP techniques were employed by the authors to match strings in the suggested system. It could be used in administrative agencies, government agencies, and multinational corporations where a large number of resumes need to be checked every day for multiple positions.

关键词： Accuracy Resumes Machine learning Manuals natural language processing Real-time systems Software Quality assessment Intelligent systems Recruitment

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