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

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Automatic Extraction and Formalization of Temporal Requirements from Text: A Survey 28th

Automatic Extraction and Formalization of Temporal Require...

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28th International conference on Enterprise Design, Operations, and Computing, EDOC 2024

作者： Barrientos, Marisol Winter, Karolin Rinderle-Ma, Stefanie Technical University of Munich Garching Germany Eindhoven University of Technology Eindhoven Netherlands

ISBN: (纸本)9783031783371

natural language processing has opened new paths for business process management and requirements engineering, particularly in automating the extraction and formalization of temporal requirements from diverse documents such as system specifications, legal texts, and business process descriptions. Recently, approaches have been introduced to automate this task, employing various document formats as input and targeting different formal specifications. However, a key challenge persists: effectively comparing these approaches and choosing the most suitable one for a specific task. This paper aims to bridge this research gap by conducting a systematic literature review, including a detailed analysis and comparing existing approaches. This comparison is crucial to determine if the latest Large language Model-based solutions could surpass existing methods in effectiveness and ease of use. The systematic literature review enables users to select the most suitable method based on their data and end goals. Moreover, this work proposes the NL2MTL (https://***/marisol-barrientos/nl2mtl, DLA: 22.04.2024) method to bridge some of the gaps identified in the literature analysis, i.e., establishing a comparable assessment method, under-representation of legal texts, poor output context management, and the necessity to automate the formalization of requirements, considering both quantitative and qualitative aspects of time. Addressing the latter aspect, we select Metric Temporal Logic (MTL) as formalization and provide the associated prompts and an evaluation of the NL2MTL output. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Administrative data processing

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MoWE-Audio: Multitask AudioLLMs with Mixture of Weak Encoders

MoWE-Audio: Multitask AudioLLMs with Mixture of Weak Encoder...

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

作者： Wenyu Zhang Shuo Sun Bin Wang Xunlong Zou Zhuohan Liu Yingxu He Geyu Lin Nancy F. Chen Ai Ti Aw Institute for Infocomm Research (I2R) Agency for Science Technology and Research (A*STAR) Singapore Centre for Frontier AI Research (CFAR) Agency for Science Technology and Research (A*STAR) Singapore

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

The rapid advancements in large language models (LLMs) have significantly enhanced natural language processing capabilities, facilitating the development of AudioLLMs that process and understand speech and audio inputs alongside text. Existing AudioLLMs typically combine a pre-trained audio encoder with a pre-trained LLM, which are subsequently finetuned on specific audio tasks. However, the pre-trained audio encoder has constrained capacity to capture features for new tasks and datasets. To address this, we propose to incorporate mixtures of ‘weak’ encoders (MoWE) into the AudioLLM framework. MoWE supplements a base encoder with a pool of relatively lightweight encoders, selectively activated based on the audio input to enhance feature extraction without significantly increasing model size. Our empirical results demonstrate that MoWE effectively improves multi-task performance, broadening the applicability of AudioLLMs to more diverse audio tasks.

关键词： Large language models Speech enhancement Signal processing Multitasking Feature extraction natural language processing Acoustics

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Evaluating Robustness of LLMs on Crisis-Related Microblogs across Events, Information Types, and Linguistic Features 34

Evaluating Robustness of LLMs on Crisis-Related Microblogs a...

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34th ACM Web conference, WWW 2025

作者： Imran, Muhammad Ziaullah, Abdul Wahab Chen, Kai Ofli, Ferda Hamad Bin Khalifa University Qatar Computing Research Institute Doha Qatar OpenAI San Francisco CA United States

ISBN: (纸本)9798400712746

The widespread use of microblogging platforms like X (formerly Twitter) during disasters provides real-time information to governments and response authorities. However, the data from these platforms is often noisy, requiring automated methods to filter relevant information. Traditionally, supervised machine learning models have been used, but they lack generalizability. In contrast, Large language Models (LLMs) show better capabilities in understanding and processing natural language out of the box. This paper provides a detailed analysis of the performance of six well-known LLMs in processing disaster-related social media data from a large-set of real-world events. Our findings indicate that while LLMs, particularly GPT-4o and GPT-4, offer better generalizability across different disasters and information types, most LLMs face challenges in processing flood-related data, show minimal improvement despite the provision of examples (i.e., shots), and struggle to identify critical information categories like urgent requests and needs. Additionally, we examine how various linguistic features affect model performance and highlight LLMs’ vulnerabilities against certain features like typos. Lastly, we provide benchmarking results for all events across both zero- and few-shot settings and observe that proprietary models outperform open-source ones in all tasks. © 2025 Copyright held by the owner/author(s).

关键词： disaster response Large language models LLM benchmarking LLM evaluation social media

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Quantifying the Impact of Non-Code Text Block on Java Code Quality: Multi-Model Framework 3

Quantifying the Impact of Non-Code Text Block on Java Code Q...

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3rd International conference on Integrated Circuits and Communication Systems, ICICACS 2025

作者： Vvs, Sriharsha Nair, Pranav B. Akarsh, S.V. Narmada, Nakka Pati, Peeta Basa Amrita School of Computing Dept. of Computer Science & Engineering Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (纸本)9798331508456

Non-code elements in programming, such as comments, documentation, and whitespace, play a crucial role in improving code readability, maintainability, and collaboration. However, their inclusion introduces overhead affecting code processing tools such as compilers and interpreters and static analyzers. In essence, this study investigates the impact of non-code blocks on the performance metrics of time complexity, space complexity, and execution time. A regression model was used to analyze the proportion of non-code elements in Java code and their effect on the performance metrics. The impact is quantified with the aim of providing empirical insights into how the density of non-code affects performance. Such findings are intended to help Java developers make decisions regarding code structuring and documentation while balancing aspects of maintainability and efficiency. The model's performance is validated by achieving an R2 score of 0.67, which demonstrates an improvement over previous methods and highlights its potential for real-world educational applications. © 2025 IEEE.

关键词： Java programming language

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Revolutionizing Legal Intelligence: Advances in Neural Networks and language Models for Legal NLP 3

Revolutionizing Legal Intelligence: Advances in Neural Netwo...

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

作者： Noronha, Raoul Samuel Alenchery, Alex Stanley Deepa, S. Jayapriya, J. Vinay, M. Christ University Department of Computer Science Bengaluru India

ISBN: (纸本)9798331523893

As the legal field continues to generate vast amounts of complex text, from contracts to court rulings, machine learning and natural language processing (NLP) techniques have emerged as valuable tools to help analyze and organize this data. In this paper, a number of state-of-the-art models will be reviewed and evaluated, including transformer models like BERT, GPT, and T5, and neural network models such as LSTM and CNN-RNN hybrids. These were then tested for the legal tasks of document classification, text summarization, and entity recognition. Some of the metrics used for evaluation include Accuracy, F1-Score, ROUGE, and BLEU. Advanced models, in particular large language models (LLMs), outperform the traditional methods by a large margin since they capture the niceties of legal language and structure much more completely. Meanwhile, high-quality legal datasets remain scarce, legalese remains incomprehensible to most, and the models are still relatively unexplainable. In sum, these challenges clearly call for future research in terms of data augmentation, explainable AI techniques, and more robust training methods that would allow AI-powered tools to be integrated much more effectively within lawyers' workflows to support them in their decision-making processes. © 2025 IEEE.

关键词： BERT Contract Clause Extraction GPT Large language Models (LLMs) Legal Document Classification Legal NLP Legal Text Summarization Machine Learning Named Entity Recognition (NER) Neural Networks Transformer Models

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natural language processing for Sentiment Analysis in ESL Writing: Understanding Emotional Tone in Learner Essays 5

Natural Language Processing for Sentiment Analysis in ESL Wr...

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5th IEEE International conference on Advances in Electrical, Computing, Communication and Sustainable Technologies, ICAECT 2025

作者： Kumar, M. Vinoth Ansari, Subuhi Kashif Gupta, Deepak Kavitha, P. Kumar, G.M. Prem Bala, B. Kiran Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Technology Department of English Avadi Chennai India Jazan University College of Engineering & Computer Science Jazan Saudi Arabia Graphic Era Hill University Department of Mechanical Engineering Uttarakhand Dehradun India Graphic Era Deemed to be University Department of Mechanical Engineering Uttarakhand Dehradun India Department of English Erode India Ddgd Vaishnav College University of Madras Pg Department of English Tamil Nadu Chennai India K. Ramakrishnan College of Engineering Department of Ai & Ds Trichy India

ISBN: (纸本)9798350356236

In recent years, the significance of sentiment analysis in educational contexts has gained considerable attention, particularly in understanding the emotional expressions of English as a Second language (ESL) learners. Accurately capturing these sentiments is essential for providing targeted support and feedback to enhance student writing skills. This study presents an advanced natural language processing (NLP) framework for sentiment analysis in ESL writing, specifically focusing on understanding emotional tones in learner essays. Utilizing a dataset sourced from Kaggle, which categorizes tweets based on emoticons, the methodology incorporates rigorous data preprocessing techniques, including text cleaning, tokenization, and error correction to enhance data quality. A comprehensive feature extraction process captures linguistic, syntactic, and sentiment-related features using dependency parsing and word embeddings, such as Word2Vec and BERT. The proposed model, implemented in Python, demonstrates a remarkable accuracy of 98.5%, significantly surpassing existing methods like Recurrent Neural Networks (RNN) and Convolutional Neural Networks (CNN), which achieved accuracies of 95.12% and 96.97%, respectively. This enhanced performance highlights the model's ability to effectively capture nuanced emotional expressions in ESL writing. The validation of the model's predictions against human evaluations ensures the reliability and accuracy of the findings. This research not only contributes to the field of sentiment analysis but also informs educational practices, offering targeted interventions to support ESL learners in improving their emotional expression and writing skills. Ultimately, the proposed NLP framework serves as a robust tool for enhancing understanding and assessment of emotional tone in educational contexts. © 2025 IEEE.

关键词： Emotion Recognition

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BertTab: Table Learning with Feature Descriptions and Context 7th

BertTab: Table Learning with Feature Descriptions and Contex...

引用

7th Chinese conference on Pattern Recognition and Computer Vision

作者： Xie, Meng An, Haibing Han, Songjun Mao, Jiangtao Jiang, Ying Wang, Jiajia Xinjiang Univ Urumqi Peoples R China

ISBN: (纸本)9789819787944;9789819787951

Tabular data is widely used as a common form of relational data in many industries where machine learning is applied, especially in finance, healthcare, and industry. However, traditional methods often do not fully consider the information embedded in the samples in the tables, including the meaning of the features themselves (column descriptions) as well as the contextual information. In this paper, we propose the BertTab model, which transforms a table sample into a sentence describing that sample by using an utterance template into which the category features of the table sample are populated. The sentences describing the sample are then converted into powerful contextual embeddings using the pre-trained Bert model. Finally, the context-embedded features are fused with the original features to obtain richer and more complete features of the sample, thus achieving higher performance. We evaluated the model on three datasets. Compared to the benchmark model, BertTab improves the AUC-ROC, AUC-PR, and accuracy by an average of 2.10, 4.43, and 0.48% on the three datasets, respectively. The ablation experiments demonstrate the positive effect of introducing category feature column descriptions and considering sample category feature contexts with the fusion of raw features on model effectiveness improvement.

关键词： Tabular data Contextual information Feature extraction Feature fusion natural language processing

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Caskow: Context-Aware Stance Detection Using External Knowledge-Augmented LLM

Caskow: Context-Aware Stance Detection Using External Knowle...

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International conference on Web Research (ICWR)

作者： Maryam Shafiei Hossein Rahmani Amirhossein Derakhshan Milad Allahgholi School of Computer Engineering Iran University of Science and Technology Tehran Iran

ISBN: (数字)9798331508913

ISBN: (纸本)9798331508920

Stance detection in texts is an important task in natural language processing with diverse applications in political, economic and marketing. The research conducted in this area is categorized into traditional methods, deep learning methods and language model-based methods. However, to the best of our knowledge, none of these methods have effectively addressed context-aware stance detection. In this paper, we propose a method called CASKOW, which first collects context for a given text from Wikipedia as an external resource using information retrieval techniques and then employs the context alongside a large language model (LLM) to detect the stance. Experimental results show that CASKOW achieves a 7% improvement in F1-measure when the label “none” is included and a 10 % improvement when the label “none” is excluded, compared to models that do not utilize contextual information. Additionally, non-numerical interpretation of results reveals key insights, allowing for the identification of errors and challenges in stance detection datasets.

关键词： Economics Deep learning Large language models Biological system modeling Encyclopedias Context awareness natural language processing Internet Online services Context modeling

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RoBGuard: Enhancing LLMs to Assess Risk of Bias in Clinical Trial Documents 31

RoBGuard: Enhancing LLMs to Assess Risk of Bias in Clinical ...

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31st International conference on Computational Linguistics, COLING 2025

作者： Ji, Changkai Zhao, Bowen Wang, Zhuoyao Wang, Yingwen Zhang, Yuejie Cheng, Ying Feng, Rui Zhang, Xiaobo School of Computer Science Shanghai Key Laboratory of Intelligent Information Processing Fudan University Shanghai200433 China National Children's Medical Center Children's Hospital of Fudan University Shanghai China

ISBN: (纸本)9798891761964

Randomized Controlled Trials (RCTs) are rigorous clinical studies crucial for reliable decision-making, but their credibility can be compromised by bias. The Cochrane Risk of Bias tool (RoB 2) assesses this risk, yet manual assessments are time-consuming and labor-intensive. Previous approaches have employed Large language Models (LLMs) to automate this process. However, they typically focus on manually crafted prompts and a restricted set of simple questions, limiting their accuracy and generalizability. Inspired by the human bias assessment process, we propose RoBGuard, a novel framework for enhancing LLMs to assess the risk of bias in RCTs. Specifically, RoBGuard integrates medical knowledge-enhanced question reformulation, multimodal document parsing, and multi-expert collaboration to ensure both completeness and accuracy. Additionally, to address the lack of suitable datasets, we introduce two new datasets: RoB-Item and RoB-Domain. Experimental results demonstrate RoBGuard's effectiveness on the RoB-Item dataset, outperforming existing methods. © 2025 Association for Computational Linguistics.

关键词： Computational linguistics

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Can LLMs Clarify? Investigation and Enhancement of Large language Models on Argument Claim Optimization 31

Can LLMs Clarify? Investigation and Enhancement of Large Lan...

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31st International conference on Computational Linguistics, COLING 2025

作者： Wang, Yiran He, Ben Chen, Xuanang Sun, Le School of Computer Science and Technology University of Chinese Academy of Sciences Beijing China Chinese Information Processing Laboratory Institute of Software Chinese Academy of Sciences Beijing China

ISBN: (纸本)9798891761964

In argumentation, the claim is the foundational proposition that underpins the argument, serving as the central pillar upon which the argument is constructed. It guides the subsequent presentation of evidence, reasoning, and analysis, thereby facilitating the audience's understanding of the core issue. Therefore, ensuring that the claim is precise and unambiguous is crucial for constructing a coherent and persuasive argument. While Large language Models (LLMs) have demonstrated proficiency in text rewriting tasks such as style transfer and query rewriting, their application to claim optimization remains unexplored. Unlike other rewriting tasks, claim clarification requires the model to rewrite ambiguous or unclear segments of the claim, enhance the content by adding omitted key details, and eliminate redundant or verbose elements. Addressing this gap, this paper evaluates the performance of LLMs on the claim clarification task across various settings. While popular rewriting evaluation methods such as BLEU and ROUGE rely on exact word matching, this paper introduces a novel semantic evaluation approach based on a sliding window mechanism. Three distinct LLMs, including LLama2, Mistral, and Qwen2, are assessed for their ability to clarify arguments through zero-shot or few-shot prompting, and supervised fine-tuning (SFT). Additionally, we propose a reinforcement learning-based clarification approach that optimally balances content preservation with claim clarity, thereby augmenting the performance of LLMs on the claim clarification task. © 2025 Association for Computational Linguistics.

关键词： Semantics

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