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16th international conference on knowledge engineering and Ontology Development, KEOD 2024 as part of 16th international Joint conference on knowledge Discovery, knowledge engineering and knowledge Management, IC3K 2024

作者： Kenneweg, Svenja Deigmöller, Jörg Cimiano, Philipp Eggert, Julian Bielefeld University Germany Honda Research Institute Europe Germany

ISBN: (纸本)9789897587160

The ability to reason about events and their temporal relations is a key aspect in natural language Understanding. In this paper, we investigate the ability of Large language Models to resolve temporal references with respect to longer event sets. Given that events rarely occur in isolation, it is crucial to determine the extent to which Large language Models can reason about longer sets of events. Towards this goal, we introduce a novel synthetic benchmark dataset comprising of 2,200 questions to test the abilities of LLMs to reason about events using a Question Answering task as proxy. We compare the performance of 4 state of the art LLMs on the benchmark, analyzing their performance in dependence of the length of the event set considered as well as of the explicitness of the temporal reference. Our results show that, while the benchmarked LLMs can answer questions over event sets with a handful of events and explicit temporal references successfully, performance clearly deteriorates with larger event set length and when temporal references get less explicit. The Benchmark is available at https://***/***/bamer. © 2024 by SCITEPRESS – Science and Technology Publications, Lda.

关键词： Spatio-temporal data

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An Overview of the ADReSS-M Signal processing Grand Challenge on Multilingual Alzheimer's Dementia Recognition Through Spontaneous Speech

IEEE OPEN JOURNAL OF SIGNAL PROCESSING

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IEEE OPEN JOURNAL OF SIGNAL processing 2024年 5卷 738-749页

作者： Luz, Saturnino Haider, Fasih Fromm, Davida Lazarou, Ioulietta Kompatsiaris, Ioannis Macwhinney, Brian Univ Edinburgh Usher Inst Edinburgh Med Sch Edinburgh EH9 3JW Scotland Univ Edinburgh Sch Engn Edinburgh EH9 3JW Scotland Carnegie Mellon Univ Dept Psychol Pittsburgh PA 15213 USA CERTH Informat Technol Inst Thessaloniki 57001 Thermi Thessalo Greece

The ADReSS-M Signal processing Grand Challenge was held at the 2023 IEEE international conference on Acoustics, Speech and Signal processing, ICASSP 2023. The challenge targeted difficult automatic prediction problems of great societal and medical relevance, namely, the detection of Alzheimer's Dementia (AD) and the estimation of cognitive test scoress. Participants were invited to create models for the assessment of cognitive function based on spontaneous speech data. Most of these models employed signal processing and machine learning methods. The ADReSS-M challenge was designed to assess the extent to which predictive models built based on speech in one language generalise to another language. The language data compiled and made available for ADReSS-M comprised English, for model training, and Greek, for model testing and validation. To the best of our knowledge no previous shared research task investigated acoustic features of the speech signal or linguistic characteristics in the context of multilingual AD detection. This paper describes the context of the ADReSS-M challenge, its data sets, its predictive tasks, the evaluation methodology we employed, our baseline models and results, and the top five submissions. The paper concludes with a summary discussion of the ADReSS-M results, and our critical assessment of the future outlook in this field.

关键词： Biomedical signal processing medical conditions Alzheimer's disease human disease biomarkers speech processing natural language processing multilingual Alzheimer's dementia detection

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Deep Learning Approach for Early Prediction of Depression on Social Network 3rd

Deep Learning Approach for Early Prediction of Depression on...

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3rd international conference on language processing and knowledge Management-LPKM

作者： Ben Amira, Manel Khoufi, Nabil Aloulou, Chafik Univ Sfax Miracl Lab ANLP Res Grp Sfax Tunisia

ISBN: (纸本)9783031850660;9783031850677

We used deep learning methods to create an innovative system for early detection of depression based on user comments on social networks. This revolutionary approach exploits large amounts of textual data available online to identify depression. The data set originates from the eRisk 2022 competition. Thanks to natural language and statistical modeling techniques, our system can analyze user comments and detect their depression. We applied deep learning methods, in particular the BiLSTM(Bidirectional Long Short-Term Memory) model. Using the approach, we obtained an F-score of 42.96%.

关键词： natural language processing Prediction of depression Deep learning BiLSTM

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RESEARCH ON TEXT SENTIMENT ANALYSIS IN natural language processing

RESEARCH ON TEXT SENTIMENT ANALYSIS IN NATURAL LANGUAGE PROC...

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2024 international conference on Electrical engineering and Intelligent Control, EEIC 2024

作者： Jiao, Shuoyang Department of Electronic Information Engineering Chengdu Jincheng University Sichuan Chengdu611731 China

ISBN: (纸本)9781837242672

Text sentiment analysis in natural language processing is an important means of understanding and interpreting human emotions in texts. It plays an important role in various applications, such as customer feedback analysis and social media monitoring. The topic of this article is research on text sentiment analysis in natural language processing. Four text sentiment analysis methods are reviewed: the method based on sentiment dictionary, the method based on traditional machine learning, the method based on deep learning, and the method based on hybrid method. By citing the literature on four methods, the paper analyzing the definition, advantages, disadvantages, and performance of each method in detail. The research results show that text sentiment analysis is mainly the method based on deep learning and the hybrid method. The conclusion highlights the need for continuing research to address the limitation of existing methods and improve the accuracy and applicability of sentiment analysis in different scenarios. © The Institution of engineering & Technology 2024.

关键词： Adversarial machine learning

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ProDepDet: Out-of-domain knowledge Transfer of Pre-trained Large language Models for Depression Detection in Text-Based Multi-Party Conversations

ProDepDet: Out-of-domain Knowledge Transfer of Pre-trained L...

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international Joint conference on Neural Networks (IJCNN)

作者： Priyadarshana, Y. H. P. P. Liang, Zilu Piumarta, Ian Kyoto Univ Adv Sci Kyoto Japan

ISBN: (纸本)9798350359329;9798350359312

Parameter-efficient, soft, and prompt- based tuning methods have received increasing attention in various downstream tasks due to the high cost of traditional fine-tuning methods in pre-trained language models (PLM). Prompt tuning (PT) is one such effective mechanism which has achieved remarkable performance in transferring the acquired knowledge of a PLM to perform an unseen task within the same domain using task-specific prompts and informative instructions. Even though most prior work considers in-domain knowledge transfer using PT, much work remains to be done for PT-based out-of-domain knowledge transfer. In this study we propose ProDepDet, a novel framework specifically designed to use a PLM's knowledge about structure and semantic modelling in multi-party conversations to perform the unseen, out-ofdomain task of depression detection. To our knowledge, this study is the first attempt to adapt the acquired knowledge of a PLM for out-of-domain task modelling using PT-based crosstask transferability. Experiments on few-shot and full data settings across multiple benchmark datasets demonstrate the superiority of our PT framework in two downstream tasks including depressed utterance classification and depressed speaker identification.

关键词： prompt task transferability natural language processing deep learning discourse analysis mental health

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Investigating Text and Image Features for the Detection of Cyberbulling 24

Investigating Text and Image Features for the Detection of C...

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

作者： Heine, Julia Schaaff, Kristina Schlippe, Tim IT and Engineering IU International University of Applied Sciences Erfurt Germany

ISBN: (纸本)9798400717383

A very serious issue throughout social media platforms and cultural groups on the internet is the phenomenon of cyberbullying, an emerging form of victimization brought about through the digital age [1]. In this paper, we investigate traditional and new features to detect cyberbullying in text messages which contain images. Our best text-based cyberbullying detection system achieves an accuracy of 66.7% on a balanced subset of the MMSH150K dataset [2]. The system uses a combination of text vector features, semantic features, list lookup features, emoji features, error-based features, and AI feedback features. Our best image-based cyberbullying detection system, which leverages image text features, obtains an accuracy of 52.5%. This shows that text vector features and image vector features, which are mostly used for this task in related work, do not necessarily lead to the best results and our new features contribute a part to improving the performance. © 2024 Copyright held by the owner/author(s).

关键词： natural language processing systems

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Exploring the natural language Generation: Current Trends and Research Challenges

Exploring the Natural Language Generation: Current Trends an...

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2024 international conference on engineering and Computing, ICECT 2024

作者： Azhar, Usama Nazir, Ambreen Shifa Tameer-e-Millat University Department of Computing Islamabad Pakistan

ISBN: (纸本)9798350349719

natural language Generation (NLG) acts as a bridge between input data and human communication. NLG systems are meant to generate human-understandable output making it a useful technique mainly in report generation, automatic replies to queries like Chatbots, and other domains that involve natural language processing (NLP). NLG continues to evolve;embracing the latest trends like neural networks, large language models, personalized features, cognitive architectures, and emotional intelligence, but it still faces some challenges with its implementations and operations for cohesive integration. Therefore, this survey presents a brief introduction to some of the latest trends and challenges faced by NLG which are playing a dynamic role in shaping the evolution of NLG. © 2024 IEEE.

关键词： Recurrent neural networks

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Large language Model and Application for Railway Track Management Based on Domain Specialization 11th

Large Language Model and Application for Railway Track Manag...

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11th international conference on Traffic and Transportation Studies, ICTTS 2024

作者： Song, Yu Tang, Yuanjie Liu, Rengkui School of Traffic and Transportation Beijing Jiaotong University Beijing100044 China

ISBN: (纸本)9789819796434

This paper explores the method of domain specialization for Large language Models in the field of railway track management, as well as their potential and practical effectiveness in knowledge-based question answering scenarios. Given the complexity of the railway track management work scenarios and the challenges associated with existing data acquisition and processing, this paper has proposed a domain-specialized large language model tailored for railway track management. Additionally, by integrating layout analysis, OCR image recognition technologies, and the self-instruct method, the efficiency and accuracy of data processing have been enhanced, and the training dataset has been expanded. Moreover, the paper employs knowledge retrieval augmentation techniques to mitigate the issue of hallucinations produced by the model, thereby improving the model's accuracy, relevance, and completeness in knowledge question answering. Furthermore, the paper has established a dataset and evaluation metrics specifically for question answering in the railway track management field and assessed them using a general Large language Model. This research not only aids relevant personnel in their daily operations and enhances work efficiency but also provides valuable references for future applications of Large language Models in similar domains. © Beijing Paike Culture Commu. Co., Ltd. 2025.

关键词： Efficiency

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Data Redundancy Elimination and Noise processing via Large language Model Prompt engineering 10

Data Redundancy Elimination and Noise Processing via Large L...

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10th international conference on Big Data and Information Analytics

作者： Jin Guang Jing Chao Zong Tianqi Chen Siya Cui Chaoyuan Fan Jun Natl Univ Def Technol Coll Syst Engn Changsha 410073 Peoples R China Chinese Acad Sci Inst Intelligent Machines Hefei Inst Phys Sci Hefei 230031 Peoples R China

ISBN: (数字)9798350354621

ISBN: (纸本)9798350354638;9798350354621

With the rapid advancement of artificial intelligence (AI), intelligent systems have been widely applied across various practical domains. During their experimental processes, data collection often occurs in complex environments, leading to challenges in ensuring data quality and integrity. Two significant issues are data redundancy and data noise, which can degrade model performance and impact the generalization ability and prediction accuracy of AI models. Traditional methods for handling these issues are either rule-based, relying heavily on domain knowledge, or machine learning-based, which require large volumes of high-quality training data and significant computational resources. In this paper, we propose a novel approach leveraging Large language Models (LLMs) combined with the Sequential Chain optimization algorithm to address data redundancy elimination and noise processing. By designing automated prompt templates tailored to experimental data characteristics and utilizing LLMs' extensive knowledge and reasoning capabilities, our approach improves the effectiveness of preprocessing tasks. Additionally, the Sequential Chain technique enhances LLM processing performance, reducing the hallucination phenomenon and ensuring higher accuracy in data preprocessing. Our experimental results demonstrate that LLM-based methods can achieve results comparable to traditional techniques while offering greater scalability and adaptability. Future work could focus on developing more efficient LLMs with lower computational requirements and refining prompt engineering techniques to reduce the time investment needed, making these advanced methods more accessible and practical for a broader range of applications.

关键词： Large language Models data redundancy elimination SequentialChain technique

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Hate Speech Detection for the Power Domain 13th

Hate Speech Detection for the Power Domain

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13th international conference on natural language processing and Chinese Computing

作者： Huang, Qingbao Deng, Zehua Chen, Shizhen Chen, Yifei Shuang, Feng Guangxi Univ Sch Elect Engn Nanning Guangxi Peoples R China Guangxi Univ Sch Comp Elect & Informat Nanning Guangxi Peoples R China Guangxi Key Lab Intelligent Control & Maintenance Nanning Guangxi Peoples R China

ISBN: (纸本)9789819794393;9789819794409

The professional nature and confidentiality of the power domain hinder the public to accurately assess the authenticity of online electricity-related statements, fostering an environment conducive to the spread of electricity-related hate speech on social media. To address this challenge, we introduce a new hate speech detection task for the electric power domain. A dataset for electric power domain hate speech detection is constructed, consisting of 6000 electricity-related Weibo posts. We propose a prompt learning approach for hate speech detection in the electric power domain, which integrates power domain knowledge, such as work scenarios and terms. Subsequently, a prompt template is formulated to facilitate hate speech detection. Experimental results on the dataset indicate that the proposed prompt learning method surpasses the baseline model.

关键词： Hate Speech Detection Prompt Learning Pre-trained visual language model

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