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

共 15205 条记录，以下是211-220 订阅

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SNOBERT: A Benchmark for Clinical Notes Entity Linking in the SNOMED CT Clinical Terminology 27th

SNOBERT: A Benchmark for Clinical Notes Entity Linking in ...

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27th International conference on Pattern Recognition, ICPR 2024

作者： Kulyabin, Mikhail Sokolov, Gleb Galaida, Aleksandr Maier, Andreas Arias-Vergara, Tomas Pattern Recognition Lab Department of Computer Science Friedrich-Alexander-Universität Erlangen-Nürnberg Erlangen Germany Moscow Russia

ISBN: (纸本)9783031781186

The extraction and analysis of insights from medical data, primarily stored in free-text formats by healthcare workers, presents significant challenges due to its unstructured nature. Medical coding, a crucial process in healthcare, remains minimally automated due to the complexity of medical ontologies and restricted access to medical texts for training natural language processing models. In this paper, we proposed a method, "SNOBERT," of linking text spans in clinical notes to specific concepts in the SNOMED CT using BERT-based models. The method consists of two stages: candidate selection and candidate matching. The models were trained on one of the largest publicly available datasets of labelled clinical notes. SNOBERT outperforms other classical methods based on deep learning, as confirmed by the results of a challenge in which it was applied. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： natural language processing systems

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VisQ2SQL: Towards SQL-Driven Data Visualization via LLMs-Grounded Preference Learning

VisQ2SQL: Towards SQL-Driven Data Visualization via LLMs-Gro...

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2025 IEEE International conference on Acoustics, Speech, and Signal processing, ICASSP 2025

作者： Shi, Shengze Ren, Tao Hu, Jun State Key Laboratory of Intelligent Game Institute of Software Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China

ISBN: (纸本)9798350368741

Text-to-Visualization (Text2Vis) aims to democratize data insights for non-expert users by transforming natural language query (NLQ) into visualization specification (VS). In view of the high dependence of rule-based methods on predefined VS templates and poor NLQ understanding ability of small data-driven methods, recent works resort to leveraging pre-trained LLMs to perform NLQ-understanding and VS-generating in Text2Vis tasks via prompt-guided in-context learning. However, existing LLM-based methods still fall short of satisfactory end-to-end Text2Vis performances primarily owing to the limited ability of pre-trained LLMs in directly retrieving and operating NLQ-intended tabular data. Inspired by the SQL generating ability born with latest LLMs, this paper proposes the idea of harnessing LLMs for SQL-driven visualization data retrieval and operation. Nonetheless, there remains a nonneglectable gap between data visualization queries in Text2Vis tasks and SQL retrieving queries in LLM corpus. To fill the gap, this paper proposes a Visualization Query to SQL (VisQ2SQL) framework to obtain NLQ-intended data, primarily by fine-tuning LLMs through preference learning data-retrieval SQLs induced from VS and those generated by LLMs. We conduct extensive experiments to demonstrate the superiority of VisQ2SQL over SOTA methods, and various ablation studies to verify the efficacy of VisQ2SQL. © 2025 IEEE.

关键词： Large language Model Preference Learning Text to Visualization

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WIBA: What Is Being Argued? A Comprehensive Approach to Argument Mining 16th

WIBA: What Is Being Argued? A Comprehensive Approach to Argu...

引用

16th International conference on Social Networks Analysis and Mining

作者： Irani, Arman Park, Ju Yeon Esterling, Kevin Faloutsos, Michalis Univ Calif Riverside Riverside CA 92521 USA Ohio State Univ Columbus OH USA

ISBN: (纸本)9783031785405;9783031785412

How can we effectively model arguments communicated in diverse environments? On the one hand, there is a great opportunity with the abundance of digitized speech across different contexts including online forums, official proceedings, or transcripts of spoken debates. On the other hand, there is a great challenge in correctly detecting arguments, especially since each medium has its own set of conventions, lingo, affordances, and styles of argumentative engagement. We propose WIBA, a novel framework and suite of methods that enable the comprehensive understanding of " What Is Being Argued" across contexts. Our approach develops a comprehensive framework that detects: (a) the existence, (b) the topic, and (c) the stance of an argument, correctly accounting for the logical dependence among the three tasks. Our algorithm leverages the fine-tuning and prompt-engineering of Large language Models. We evaluate our approach and show that it performs well in all the three capabilities. First, we develop and release an Argument Detection model that can classify a piece of text as an argument with an F-1 score between 79% and 86% on three different benchmark datasets. Second, we release a language model that can identify the topic being argued in a sentence, be it implicit or explicit, with an average similarity score of 71%, outperforming current naive methods by nearly 40%. Finally, we develop a method for Argument Stance Classification, and evaluate the capability of our approach, showing it achieves a classification F-1 score between 71% and 78% across three diverse benchmark datasets. Our evaluation demonstrates that WIBA allows the comprehensive understanding of What Is Being Argued in large corpora across diverse contexts, which is of core interest to many applications in linguistics, communication, and social and computer science. To facilitate accessibility to the advancements outlined in this work, we release WIBA as a free open access platform (***) and API.

关键词： Argument Mining Large language Models natural language processing

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An Exhaustive Survey on the methods and Applications of Graph Neural Networks 8th

An Exhaustive Survey on the Methods and Applications of Grap...

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8th International conference on Information Science, Communication and Computing

作者： Fan, Dong Cheng, Jieren Tang, Xiangyan Lu, Kai Hainan Univ Sch Comp Sci & Technol Haikou 570228 Hainan Peoples R China Hainan Blockchain Technol Engn Res Ctr Haikou 570228 Hainan Peoples R China Sch Cyberspace Secur Sch Cryptol Haikou 570228 Hainan Peoples R China Hainan Vocat Coll Polit & Law Dept Publ Safety Technol Haikou 571100 Hainan Peoples R China

ISBN: (纸本)9789819617838;9789819617814;9789819617807

The widespread use of deep learning has achieved unprecedented success. Neural networks have achieved remarkable results in natural language processing, image recognition and other application fields. The key to the success of neural networks lies in its efficient processing of Euclidean space data, but the results obtained when traditional neural networks are used in non-Euclidean spaces are often not satisfactory to researchers. Graph neural network has been widely developed as a neural network that can be used in non-Euclidean space. This paper starts with the basic knowledge of graphs, introduces graph neural networks, and makes a new classification of them. Then it illustrates its application from 18 application areas. Finally, I put forward my own views on the future development and challenges of graph neural networks.

关键词： Graph Neural Networks Convolution Recurrent Graph Generation Euclidean non-Euclidean

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Pseudo-Prompt Generating in Pre-trained Vision-language Models for Multi-label Medical Image Classification 7th

Pseudo-Prompt Generating in Pre-trained Vision-Language Mode...

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7th Chinese conference on Pattern Recognition and Computer Vision

作者： Ye, Yaoqin Zhang, Junjie Shi, Hongwei Sichuan Univ Coll Comp Sci Chengdu Peoples R China

ISBN: (纸本)9789819784950;9789819784967

The task of medical image recognition is notably complicated by the presence of varied and multiple pathological indications, presenting a unique challenge in multi-label classification with unseen labels. This complexity underlines the need for computer-aided diagnosis methods employing multi-label zero-shot learning. Recent advancements in pre-trained vision-language models (VLMs) have showcased notable zero-shot classification abilities on medical images. However, these methods have limitations on leveraging extensive pre-trained knowledge from broader datasets, and often depend on manual prompt construction by expert radiologists. By automating the process of prompt tuning, prompt learning techniques have emerged as an efficient way to adapt VLMs to downstream tasks. Yet, existing CoOp-based strategies fall short in performing class-specific prompts on unseen categories, limiting generalizability in fine-grained scenarios. To overcome these constraints, we introduce a novel prompt generation approach inspirited by text generation in natural language processing (NLP). Our method, named Pseudo-Prompt Generating (PsPG), capitalizes on the priori knowledge of multi-modal features. Featuring a RNN-based decoder, PsPG autoregressively generates class-tailored embedding vectors, i.e., pseudo-prompts. Comparative evaluations on various multi-label chest radiograph datasets affirm the superiority of our approach against leading medical vision-language and multi-label prompt learning methods. The source code is available at https://***/fallingnight/PsPG.

关键词： Prompt Learning Medical Image Recognition Multi-label Classification Vision-language Models

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Autonomous Agents in Software Development: A Vision Paper

Autonomous Agents in Software Development: A Vision Paper

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25th International conference on Agile Software Development (XP)

作者： Rasheed, Zeeshan Waseem, Muhammad Sami, Malik Abdul Kemell, Kai-Kristian Ahmad, Aakash Anh Nguyen Duc Systae, Kari Abrahamsson, Pekka Tampere Univ Tampere Finland Univ Jyvaskyla Jyvaskyla Finland Univ Helsinki Helsinki Finland Lancaster Univ Leipzig Leipzig Germany Univ South Eastern Norway Notodden Norway

ISBN: (纸本)9783031727801;9783031727818

Large language Models (LLM) are reshaping the field of Software Engineering (SE). They enable innovative methods for executing many SE tasks, including automation of entire process of Software Development Life Cycle (SDLC). However, only a limited number of existing works have thoroughly explored the potential of LLM based AI agents to automate the entire lifecycle in SE. In this paper, we demonstrate the success of our initial efforts in automating the entire lifecycle autonomously based on given software specification as input, which has shown remarkable efficiency and significantly reduced development time. Our preliminary results suggest that the careful implementation of AI agents can enhance the development lifecycle. We aim to streamline the SDLC by integrating all phases into an AI-driven chat interface, enhancing efficiency and transparency. Furthermore, we seek to enhance collaboration, creating an environment where stakeholders from various backgrounds can contribute, review, and refine ideas and requirements in real-time. This forward-looking direction guarantees to redefine the paradigms of SE and also make software creation more inclusive, collaborative, and efficient.

关键词： OpenAI AutoGPT Artificial Intelligence natural language processing Generative AI Software Engineering Large language Model

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A Comparison of Traditional natural language processing methods for Emotion Recognition Using Quantum Computing

A Comparison of Traditional Natural Language Processing Meth...

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2nd International conference on Machine Learning and Autonomous Systems, ICMLAS 2025

作者： Thota, Srirajarajeswari Dash, Sandeep Kumar National Institute of Technology Department of CSE Mizoram Aizawl India

ISBN: (纸本)9798331505745

Emotion Recognition plays an important role in recognizing human behavior. Human behavior may be represented in text, speech, and facial expressions. In traditional form, natural language processing known as NLP is used to extract features to evaluate emotions. With the evolution of Quantum Computing, Quantum Machine learning often called QML is one of the research areas used to evaluate emotions. This paper is designed to compare traditional ML models with QML models to analyze the sentiments and emotions of multimodal data. Here, NLP techniques are used for preprocessing. A hybrid model is built by combining NLP and QML. The preprocessed data are transmitted into quantum circuits for computation, and its results are compared with conventional machine learning models. This evaluation identifies QML as a promising future as it is useful for large and complex data sets with high dimensions. This study highlights the potential of QML in NLP and outlines the challenges such as encoding and hardware limitations. © 2025 IEEE.

关键词： Sentiment analysis

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Sentiment-Based natural language processing for Automotive Industry 5th

Sentiment-Based Natural Language Processing for Automotive I...

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5th International conference on Data Science, Machine Learning and Applications, ICDSMLA 2023

作者： Padmanabhan, P. Bhuvana, J. Naval, Preeti Faujdar, Pramod Kumar Noida Institute of Engineering and Technology Uttar Pradesh Greater Noida India Karnataka Bangalore India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh Lucknow India Department of Mechanical Engineering Vivekananda Global University Jaipur India

ISBN: (纸本)9789819780426

The automobile industry is an unexpectedly changing sector that has embraced the modern era to take advantage of competitive benefits. natural language processing (NLP) performs a crucial position in information patron sentiment, as it can hit upon diffused nuances in textual content that people can also leave out or misinterpret. This paper explores how NLP can be used to tune and analyze client sentiment inside the automotive industry. We describe the exclusive types of sentiment analysis processes currently being applied in this enterprise, along with sentiment dictionaries, device learning-primarily based methods, and deep getting-to-know-primarily based strategies. We then offer numerous examples of how sentiment evaluation may be applied in the car industry, such as enhancing customer service, detecting and responding to patron reviews, and growing marketing campaigns. Finally, we speak of some of the challenges associated with sentiment evaluation inside the automotive enterprise and provide pointers for overcoming those demanding situations. Sentiment-primarily based natural language processing (NLP) is a modern utility of NLP techniques for extracting sentiment from text-based resources. The utility of NLP for sentiment evaluation can provide automotive enterprise stakeholders with critical insights into customer sentiment concerning their emblem, competition, product lines, and different marketplace factors. Using sentiment-based NLP, automotive industry stakeholders can benefit from essential insights into patron sentiment and leverage them to serve their customers better. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Sales

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Leveraging Secure Social Media Crowdsourcing for Gathering Firsthand Account in Conflict Zones 16th

Leveraging Secure Social Media Crowdsourcing for Gathering F...

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16th International conference on Social Networks Analysis and Mining

作者： Orojo, Abanisenioluwa Bhagat, Pranish Wilburn, John Donahoo, Michael Vishwamitra, Nishant Baylor Univ Waco TX 76798 USA Univ Texas San Antonio San Antonio TX USA

ISBN: (纸本)9783031785375;9783031785382

The Russo-Ukrainian conflict underscores challenges in obtaining reliable firsthand accounts. Traditional methods such as satellite imagery and journalism fall short due to limited access to zones. Secure social media platforms such as Telegram offer safer communication from conflict zones but lack effective message grouping, hindering insight collection. The proposed framework aims to enhance firsthand account gathering by crowdsourcing secure social media data. We gathered 250,000 Telegram messages on the conflict and developed a language model-based framework to identify contextual groupings. Evaluation reveals 477 new groupings from 13 news sources, enriching firsthand information. This research emphasizes the significance of secure social media crowdsourcing in conflict zones, paving the way for future advancements.

关键词： Crowdsourcing Social Media Conflict Zones Firsthand Accounts Secure Communication Data Analysis natural language processing

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Construction and empirical analysis of data governance system based on knowledge graph 2

Construction and empirical analysis of data governance syste...

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2nd International conference on Big Data, Computational Intelligence, and Applications, BDCIA 2024

作者： Zhao, Linyan Yao, Zhen Liu, Cuiling Du, Yan Hou, Haoyu Hu, Zhiyuan Information and Communication Branch of State Grid Anhui Electric Power Co. Ltd. Hefei230000 China

ISBN: (纸本)9781510689053

In the context of the burgeoning era of big data, characterized by the exponential growth of information and the increasing complexity of data structures, this study introduces an innovative intelligent data governance model that harnesses the power of knowledge graph technology to surmount the multifaceted challenges associated with data management and utilization. The proposed model is a synthesis of advanced computational methodologies, including distributed crawler technology for comprehensive data collection, sophisticated data quality detection algorithms to ensure the integrity and reliability of the dataset, cutting-edge natural language processing techniques for the extraction of semantic insights, robust graph database technology for the structured representation of data relationships, and nuanced machine learning algorithms for predictive analytics and pattern recognition. Through rigorous empirical analysis, the model's efficacy is substantiated across critical phases of data governance, including acquisition, sanitization, knowledge extraction, graph construction, and intelligent analysis. The outcomes indicate a marked enhancement in data quality metrics and a streamlined data processing workflow, thereby optimizing the overall efficiency of data governance practices. This research not only contributes a novel approach to the advancement of big data technology but also furnishes a comprehensive framework that serves as an invaluable resource for scholars, practitioners, and policymakers in the realm of data governance. The theoretical underpinnings and practical applications of this study hold significant implications for the strategic management of data assets, the enhancement of decision-making processes, and the fostering of data-driven innovation across diverse sectors and industries. © 2025 SPIE.

关键词： Knowledge graph

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