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

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Improving Zero-Shot Image Captioning Efficiency with Metropolis-Hastings 7th

Improving Zero-Shot Image Captioning Efficiency with Metropo...

引用

7th Chinese conference on Pattern Recognition and Computer Vision

作者： Dul, Dehu Wu, Yujia Shanghai Polytech Univ Sch Comp & Informat Engn Inst Artificial Intelligence Shanghai 201209 Peoples R China Sanda Univ Sch Informat Sci & Technol Shanghai 201209 Peoples R China

ISBN: (纸本)9789819785100;9789819785117

Image captioning, a crucial aspect of natural language processing, aims to convert visual content into textual representations through technological advancements. Zero-shot learning, a technique that has gained widespread attention in recent research, performs tasks without relying on domain-specific training datasets. However, current zero-shot image captioning methods mainly depend on non-autoregressive language models, which often suffer from operational inefficiencies, resulting in prolonged captioning times and limiting their practical applications. To address this limitation, this study introduces an efficient zero-shot image captioning method, MHIC, leveraging the Metropolis-Hastings sampling algorithm. MHIC significantly improves computational speed while maintaining caption quality. Specifically, we optimize the caption generation process using the Metropolis-Hastings algorithm, effectively reducing the number of iterations required for word generation while preserving caption quality by minimizing the number of generated words. This approach markedly enhances the speed of image captioning. To validate the effectiveness of MHIC, we conducted rigorous experiments on two publicly available datasets. The results demonstrate that our proposed algorithm achieves an average speedup of 2 times compared to state-of-the-art methods, highlighting its significant potential in improving computational efficiency.

关键词： Image Captioning Zero-Shot Pre-trained language Models Metropolis-Hastings

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Detecting Suicidal Ideation on Social Media Using Large language Models with Zero-Shot Prompting 11

Detecting Suicidal Ideation on Social Media Using Large Lang...

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11th International conference on Information and Communication Technologies for Ageing Well and e-Health, ICT4AWE 2025

作者： Nikmehr, Golnaz Bilbao-Jayo, Aritz Henriksson, Aron Almeida, Aitor Deustotech - University of Deusto Bilbao Spain Stockholm University Stockholm Sweden

ISBN: (纸本)9789897587436

Detecting suicidal ideation in social media posts using natural language processing (NLP) and Machine Learning has become an essential approach for early intervention and providing support to at-risk individuals. The role of data is critical in this process, as the accuracy of NLP models largely depends on the quality and quantity of labeled data available for training. Traditional methods, such as keyword-based approaches and models reliant on manually annotated datasets, face limitations due to the complex and time-consuming nature of data labeling. This shortage of high-quality labeled data creates a significant bottleneck, limiting model fine-tuning. With the recent emergence of Large language Models (LLMs) in various NLP applications, we utilize their strengths to classify posts expressing suicidal ideation. Specifically, we apply zero-shot prompting with LLMs, enabling effective classification even in data-scarce environments without needing extensive fine-tuning, thus reducing the dependence on large annotated datasets. Our findings suggest that zero-shot LLMs can match or exceed the performance of traditional approaches like fine-tuned RoBERTa in identifying suicidal ideation. Although no single LLM outperforms consistently across all tasks, their adaptability and effectiveness underscore their potential to detect suicidal thoughts without requiring manually labeled data. Copyright © 2025 by SCITEPRESS - Science and Technology Publications, Lda.

关键词： natural language processing systems

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Automated Query-Product Relevance Labeling using Large language Models for E-commerce Search 24

Automated Query-Product Relevance Labeling using Large Langu...

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8th International conference on natural language processing and Information Retrieval, NLPIR 2024

作者： Sachdev, Jayant Rosario, Sean D. Phatak, Abhijeet Wen, He Kirti, Swati Tripathy, Chittaranjan Walmart Global Tech Sunnyvale United States

ISBN: (纸本)9798400717383

Accurate query-product relevance labeling is indispensable to generate ground truth dataset for search ranking in e-commerce. Traditional approaches for annotating query-product pairs rely on human-based labeling services, which is expensive, time-consuming and prone to errors. In this work, we explore the application of Large language Models (LLMs) to automate query-product relevance labeling for large-scale e-commerce search. We use several publicly available and proprietary LLMs for this task, and conducted experiments on two open-source datasets and an in-house e-commerce search dataset. Using prompt engineering techniques such as Chain-of-Thought (CoT) prompting, In-context Learning (ICL), and Retrieval Augmented Generation (RAG) with Maximum Marginal Relevance (MMR), we show that LLM's performance has the potential to approach human-level accuracy on this task in a fraction of the time and cost required by human-labelers, thereby suggesting that our approach is more efficient than the conventional methods. We have generated query-product relevance labels using LLMs at scale, and are using them for evaluating improvements to our search algorithms. Our work demonstrates the potential of LLMs to improve query-product relevance thus enhancing e-commerce search user experience. More importantly, this scalable alternative to human-annotation has significant implications for information retrieval domains including search and recommendation systems, where relevance scoring is crucial for optimizing the ranking of products and content to improve customer engagement and other conversion metrics. © 2024 Copyright held by the owner/author(s).

关键词： Marketplaces

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A Prompt Learning Framework with Large language Model Augmentation for Few-shot Multi-label Intent Detection

A Prompt Learning Framework with Large Language Model Augmen...

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

作者： Zhuang, Ning Wei, Xiao Li, Junlei Wang, Xiaobao Wang, Chenyang Wang, Longbiao Dang, Jianwu Tianjin Key Laboratory of Cognitive Computing and Application College of Intelligence and Computing Tianjin University Tianjin China Shenzhen China Co. Ltd. Tianjin China Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China

ISBN: (纸本)9798350368741

Intent detection (ID) is essential in spoken language understanding, especially in multi-label settings where intent labels are interdependent and diverse. Existing methods like SE-MLP and QA-FT struggle in few-shot settings, due to limited data availability and efficiency concerns. To address this, we introduce a Prompt Learning framework with large language Model Augmentation (PLMA) for few-shot multi-label ID. In this study, we make three contributions. First, PLMA integrates large language models (LLMs) with small language models (SLMs), using LLMs to enhance SLMs, with prompt learning as the core framework. Second, it leverages LLMs to improve the model's understanding of both queries and labels by intent spans extraction and answer space expansion. Third, PLMA refines the QA-FT. A novel QA format template is designed to allow single-step training and inference for each input, improving efficiency. Experiments on NLU++ show that PLMA significantly outperforms other baselines in both in-domain and cross-domain settings, demonstrating its effectiveness in combining the strengths of both small and large models. © 2025 IEEE.

关键词： large language model multi-label intent detection prompt learning spoken language understanding

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Performance Evaluation of Reddit Comments Using Machine Learning and natural language processing methods in Sentiment Analysis 30th

Performance Evaluation of Reddit Comments Using Machine Lear...

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30th International conference on Computational and Experimental Engineering and Sciences

作者： Zhang, Xiaoxia Qi, Xiuyuan Teng, Zixin Shanghaitech Univ Shanghai Peoples R China

ISBN: (纸本)9783031774911;9783031774898;9783031774881

Sentiment analysis, an increasingly vital field in both academia and industry, plays a pivotal role in machine learning applications, particularly on social media platforms like Reddit. However, the efficacy of sentiment analysis models is hindered by the lack of expansive and fine-grained emotion datasets. To address this gap, our study leverages the GoEmotions dataset, comprising a diverse range of emotions, to evaluate sentiment analysismethods across a substantial corpus of 58,000 comments. Distinguished from prior studies by the Google team, which limited their analysis to only two models, our research expands the scope by evaluating a diverse array of models. We investigate the performance of traditional classifiers such as Naive Bayes and Support Vector Machines (SVM), as well as state-of-the-art transformer-based models including BERT, RoBERTa, and GPT. Furthermore, our evaluation criteria extend beyond accuracy to encompass nuanced assessments, including hierarchical classification based on varying levels of granularity in emotion categorization. Additionally, considerations such as computational efficiency are incorporated to provide a comprehensive evaluation framework. Our findings reveal that the RoBERTa model consistently outperforms the baseline models, demonstrating superior accuracy in fine-grained sentiment classification tasks. This underscores the substantial potential and significance of the RoBERTa model in advancing sentiment analysis capabilities.

关键词： Sentiment Analysis Machine Learning natural language processing Reddit RoBERTa

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Graph Based Automatic Text Summarization for Categorization of BBC News Articles 2

Graph Based Automatic Text Summarization for Categorization ...

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2nd International conference on Computational Intelligence, Communication Technology and Networking, CICTN 2025

作者： Singh, Pratik Pandey, Shivani Das Lodhi, Abhinav Namdev, Sonal Mishra, Pradeep Kumar Pandey, Sanjay Kr. Department of Information Technology ABES Engineering College UP Ghaziabad India Department of CSE-DS MIET UP Meerut India Department of CSE-DS RKGIT UP Ghaziabad India Department of CSE MIT UP Meerut India Department of CSE United College of Engineering and Research UP Prayagraj India

ISBN: (纸本)9798331530389

Large number of users use world wide web, news on news channels, weather reports, blogs, online available research works, online social media platforms and government reports, magazines to extract the useful information but due to enormous size of textual content in Petabytes or Zettabytes it is unfeasible to manually summaries the big data and extract the required information. Thus, the use of computerized text summarization is becoming more widespread. Research on text summarization has been conducted extensively in the field of natural language processing (NLP). There are numerous methods for automatic text summarization. Using the BBC News Summary dataset, this research employs a popular technique called graph-based extractive text summarization. It might be difficult to assess a text summary's quality;BLEU measurements and ROGUE variables are employed in this process. © 2025 IEEE.

关键词： natural language processing systems

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Development and Implementation of Advanced Text Autocorrection via Probabilistic methods Using Levenshtein Distance and Jaccard Similarity 5

Development and Implementation of Advanced Text Autocorrecti...

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5th International conference on Emerging Systems and Intelligent Computing, ESIC 2025

作者： Saunshimath, Nirmala J Upadhyaya, Dipankar Raj Yadav, Abiskar Kumar Nitte Meenakshi Institute of Technology Department of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798331522100

The paper aims at creating an advanced autocorrection system specifically for the Nepali language. This system uses sophisticated natural language processing (NLP) techniques to automatically rectify typographical errors of Nepali texts. The autocorrection system suggests the most likely corrections for misspelt words using the Levenshtein Distance and Jaccard Similarity algorithms. This paper aims to improve the efficiency and user experience across a variety of text-based applications, such as social media, document editing, and messaging, by decreasing the amount of time needed for text editing and increasing text accuracy. The groundwork for future developments in NLP applications for the Nepali language is laid by this effort. © 2025 IEEE.

关键词： natural language processing systems

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Collision-less and Balanced Sampling for language-Queried Audio Source Separation

Collision-less and Balanced Sampling for Language-Queried Au...

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

作者： Nguyen, Binh Thien Takeuchi, Daiki Yasuda, Masahiro Niizumi, Daisuke Harada, Noboru NTT Corporation Tokyo Japan

ISBN: (纸本)9798350368741

language-queried audio source separation (LASS) is an emerging research field that has recently received increasing attention. This task aims to isolate individual sources from a mixture of signals using natural language descriptions, enabling applications in various areas such as automatic audio editing. While conventional methods focus on the system architecture, the important aspect of data processing has been overlooked. The data for training LASS are typically created by mixing various audio signals in the dataset to form a mixture. One signal is then used as the target, whereas the others are regarded as interference. However, sound events in the target signal could overlap with those in the interference signals, which may cause confusion that instructs the model to both retain and suppress the same sound events within a single training example. In addition, training LASS with large-scale datasets may suffer from the data imbalance problem, where some sound events appear too frequently while others are rare. In this paper, we address these problems by using data sampling techniques. Specifically, the interference signals are sampled so that their audio tags do not conflict with those of the target signal, where the tags are generated using an audio tagging model. To balance the data, we consider several balanced sampling approaches using tag or caption embedding. By leveraging their distribution information, we use either weighted or group sampling to boost the occurrence of underrepresented samples while reducing the presence of overrepresented ones. Experimental results show the superiority of the proposed method over state-of-the-art LASS systems in DCASE 2024 Challenge Task 9. Pretrained model is available at: https://***/tucothien/LASS-CLBS. © 2025 IEEE.

关键词： balanced sampling collision-less sampling contrastive language-audio pre-training language-queried audio source separation

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Aspect based Sentiment Analysis via Dependency Parsing and Structured Dependency Representation 1

Aspect based Sentiment Analysis via Dependency Parsing and S...

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1st International conference on Intelligent Systems and Computational Networks, ICISCN 2025

作者： Siddiqua, Ayesha Nagaraj, H.C. Nitte Meenakshi Institute of Technology Department of Ece Bengaluru India

ISBN: (纸本)9798331529246

Aspect-Based Sentiment Analysis (ABSA) is an essential activity in natural language processing (NLP), enabling a comprehensive assessment of sentiments associated with particular features of a product or service. This work underscores the significance of dependency parsing and representation strategies to improve the precision and detail of ABSA. The study used the Edmunds-Consumer automotive ratings and reviews dataset to discern essential automobile characteristics and evaluate the attitudes linked to them. Dependency relations, including AMOD (adjective modifier), ADVMOD (adverb modifier), XCOMP (open clausal complement), and NEG (negation), are incorporated into the analysis to elucidate phrase structures and contextual subtleties. This method greatly enhances the accuracy of sentiment categorization by utilizing detailed dependency structures. It offers profound insights into customer perspectives, facilitating the recognition of complex input concerning certain automotive characteristics. The suggested approach provides enterprises with a powerful instrument to optimize product offerings and improve customer satisfaction by generating actionable insights into consumer sentiment. Its capacity for detailed sentiment analysis renders it an invaluable resource for enterprises aiming to enhance their comprehension of client preferences and optimize engagement methods. Experiments utilizing synthetic and authentic datasets provide favorable outcomes in comparison to current sentiment analysis methodologies. © 2025 IEEE.

关键词： ADVMOD AMOD aspect extraction aspect-based sentiment analysis dependency parsing dependency representation edmunds-consumer car ratings natural language processing sentiment classification spacy

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Evolution and Optimization of language Model Architectures: From Foundations to Future Directions

Evolution and Optimization of Language Model Architectures: ...

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International conference on Computing and Machine Learning, CML 2024

作者： AlQenaei, Zainab M. Information System and Operations Management Department Sabah Al-Salem University City Sabah Al Salem Kuwait

ISBN: (纸本)9789819778386

In the continuously advancing domain of artificial intelligence, language model architectures have undergone a significant transformation, evolving from fundamental statistical methods to sophisticated neural network-based systems. This paper conducts a thorough survey of the chronological development of language models, emphasizing the pivotal shift brought about by the introduction of neural networks and the revolutionary advent of transformer architectures. By synthesizing existing literature, we aim to set the performance benchmarks that have served as milestones for language model evolution and optimization. The review compares the advances across different generations of models, identifying overarching themes, challenges, and the consequent trajectory of the field. Subsequent to a thorough comparative analysis, we elucidate persistent limitations and speculate on emergent trends, thus carving out potential future directions. The insights presented are predicated on an extensive compilation of empirical evaluations and theoretical propositions documented within the academic community, providing a comprehensive perspective to both the seasoned researcher and the inquiring novice. This synthesis not only contributes to the understanding of language model development but also serves as a beacon for future explorations in the realm of natural language understanding and generation. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Denial-of-service attack

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