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共 27241 条记录，以下是311-320 订阅

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Joint Entity and Relation Extraction with Self-prior Attention for Dual Set Prediction Networks 3rd

Joint Entity and Relation Extraction with Self-prior Attenti...

引用

3rd international conference on Machine Learning, Cloud Computing and Intelligent Mining, MLCCIM 2024

作者： Wang, Jie Zhang, Juan Shanghai University of Engineering Science Shanghai China

ISBN: (纸本)9789819616930

Joint extraction of entity and relation represents a critical component in natural language processing, exerting a direct influence on the quality of ensuing knowledge graph construction. Although existing methodologies for the joint extraction of entities and relations have demonstrated promising results, exemplified by the parallel triplet decoding method which generates final triplets via ensemble prediction, these approaches often neglect the interactions between entities and relationships. To address this issue, we introduce a dual set prediction network, enhanced with a self-prior attention mechanism (SADSPN).In order to bolster the interactions between entities and relationships, our approach utilizes parallel decoding of triplets via dual set prediction networks, incorporating a self-prior attention mechanism within both networks to intensify the model's emphasis on pivotal information. The initial set prediction network decodes the subject-object information contained within triplets, whereas the second network models the embedded triplets, using the subject-object information to decipher the types of relationships between them. Utilizing a pretrained BERT encoder, our model attains F1 scores of 93.1% and 93.7% on the extensively employed NYT and WebNLG datasets. Additionally, the model exhibits exceptional performance in analyzing overlapping triplets. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： knowledge graph

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Using language Models for assessment of users’ satisfaction with their partner in Persian 1

Using Language Models for assessment of users’ satisfaction...

引用

1st Workshop on language Models for Low-Resource languages, LoResLM 2025 - co-located at the 31st international conference on Computational Linguistics, COLING 2025

作者： Habibzadeh, Zahra Asadpour, Masoud School of Electrical Computer Engineering College of Engineering University of Tehran Iran

ISBN: (纸本)9798891762152

Sentiment analysis, the process of gauging user attitudes and emotions through their textual data, including social media posts and other forms of communication, is a valuable tool for informed decision-making. In other words, by determining whether a statement conveys positivity, negativity, or neutrality, sentiment analysis offers insights into public sentiment regarding a product, individual, event, or other significant topics. This research focuses on the effectiveness of sentiment analysis techniques, using Machine Learning (ML) and natural language processing (NLP) especially pre-trained language models for Persian, in assessing users’ satisfaction with their partner, using data collected from X (formerly Twitter). Our motivation stems from traditional in-person surveys, which periodically analyze societal challenges in Iran. The limitations of these surveys led us to explore Artificial Intelligence (AI) as an alternative solution for addressing contemporary social issues. We collected Persian tweets and utilized data annotation techniques to label them according to our research question, forming the dataset. Our goal also was to provide a benchmark of Persian tweets on this specific topic. To evaluate our dataset, we employed several classification methods, including classical ML models, Deep Neural Networks, and pre-trained language models for Persian. Following a comprehensive evaluation, our results show that BERTweet-FA (one of the pre-trained language models for Persian) emerged as the best performer among the classifiers for assessing users’ satisfaction. This point indicates the ability of language models to understand conversational Persian text and perform sentiment analysis, even in a low-resource language like Persian. © 2025 Association for Computational Linguistics.

关键词： Tweets

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Towards Complex Ontology Alignment Using Large language Models 6th

Towards Complex Ontology Alignment Using Large Language Mode...

引用

6th international conference on knowledge Graphs and Semantic Web, KGSWC 2024

作者： Amini, Reihaneh Norouzi, Sanaz Saki Hitzler, Pascal Amini, Reza Kansas State University ManhattanKS United States Wright State University DaytonOH United States

ISBN: (纸本)9783031812200

Ontology alignment, a critical process in the Semantic Web for detecting relationships between different ontologies, has traditionally focused on identifying so-called "simple" 1-to-1 relationships through class labels and properties comparison. The more practically useful exploration of more complex alignments remains a hard problem to automate, and as such is largely underexplored, i.e. in application practice it is usually done manually by ontology and domain experts. Recently, the surge in natural language processing (NLP) capabilities, driven by advancements in Large language Models (LLMs), presents new opportunities for enhancing ontology engineering practices, including ontology alignment tasks. This paper investigates the application of LLM technologies to tackle the complex ontology alignment challenge. Leveraging a prompt-based approach and integrating rich ontology content – so-called modules – our work constitutes a significant advance towards automating the complex alignment task. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Modeling languages

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Synthetic pseudo-text aided radar interference recognition: A few-shot learning approach 16

Synthetic pseudo-text aided radar interference recognition: ...

引用

16th international conference on Signal processing Systems, ICSPS 2024

作者： Chen, Kunjie Jin, Benzhou Xiao, Jianghao Liu, Jingyi College of Electronic and Information Engineering Nanjing University of Aeronautics and Astronautics Nanjing China China Telecom Corporation Limited Jiangsu Branch Nanjing China

ISBN: (纸本)9781510689251

In practical scenarios of radar interference recognition, it is challenging to acquire sufficient instances of certain interference patterns, yielding a limited dataset for training recognition models. This scarcity of training data severely degrades the recognition accuracy. To address this few-shot recognition dilemma, this paper proposes a novel few-shot interference recognition methods based on synthetic pseudo text dataset. Large language Models (LLM) are adopted to generate varied text descriptions as supporting data. After splitting and filtering, the text data is paired with the time-frequency (TF) graph of interference, forming an image-to-text dataset. We select the Contrastive language-Image Pretraining (CLIP) model as the base model and further refine it using the synthesized text-image pairs. This fine-tuning process enables the model to recognize radar interference types by calculating the similarity between the signal’s TF graph and the descriptive text prompts for each potential class. Simulation experiments demonstrate that our proposed method achieves a recognition accuracy of 93.32% with a only 5 shots supporting set, outperforming ProtoNet and MatchingNet by 4.15% and 5.94%, respectively. © 2025 SPIE.

关键词： Radar interference

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Deep Learning Approach to Identify and Classify Arabic Verbal Multi-word Expressions 3rd

Deep Learning Approach to Identify and Classify Arabic Verba...

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

作者： Bsissa, Meniara Keskes, Iskandar Mohamed, Najet Hadj Univ Sfax Fac Econ & Management Sfax FSEGS MIRACL Lab Sfax Tunisia Univ Tours LIFAT Tours France

ISBN: (纸本)9783031850660;9783031850677

This paper focuses on the identification and classification of Arabic Verbal Multi-Word Expressions (VMWE), which are combinations of words with a unitary meaning and containing at least one verb. We describe our contribution to annotating the Arabic-annotated-conll17 corpus with VMWEs, following the annotation guide of the PARSEME framework, and using the ChatGPT model to enrich the corpus with sentences that contain more linguistic phenomena. We propose a method for the identification and classification of Arabic VMWEs based on machine learning (Random Forest, Decision trees and Support Vector Machine) and deep learning (Conv1D+BiLSTM) models. The results show that our Random Forest model outperformed others in identification, while the Decision Tree model excelled in classification. Additionally, our deep learning model exhibited significant performance improvement after data enrichment with ChatGPT. These findings underscore the effectiveness of our approach in addressing this complex problem.

关键词： Multi-word expression Arabic language VMWE identification VMWE classification deep learning

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Leveraging AI to Ensure Authenticity in Student Assignments: A knowledge-Based Validation and Evaluation Framework 5

Leveraging AI to Ensure Authenticity in Student Assignments:...

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5th international conference on Advanced Research in Computing, ICARC 2025

作者： Hondarangala, Chinthaka Wickramaarachchi, Dilani University of Kelaniya Department of Industrial Management Sri Lanka

ISBN: (纸本)9798331530983

This research explores the development of an AI-driven assessment system designed to evaluate student knowledge while minimizing cheating in educational settings. The system operates in two distinct phases. In Phase 1, AI-generated, contextually relevant questions based on student-submitted assignments assess whether the student has genuinely engaged with the content or used external tools, such as generative AI, without proper understanding. In Phase 2, the system delves deeper into conceptual understanding by aligning submissions with course-specific materials, evaluating relevance, depth, and originality. A fine-tuned large language model (LLM), trained on domain-specific data, enables precise evaluation against academic standards. Results indicate that students demonstrated marked improvements in understanding and performance, validating the effectiveness of the two-phase model. This model encouraged deeper engagement and comprehension, increasing students' knowledge of the material. By addressing the shortcomings of traditional assessment methods, this research introduces a model that reduces the likelihood of students blindly submitting assignments without truly understanding the content. © 2025 IEEE.

关键词： natural language processing systems

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Customer-Based Opinion Analysis Using Clustering and Classification Techniques 5th

Customer-Based Opinion Analysis Using Clustering and Classif...

引用

5th international conference on Data Science, Machine Learning and Applications

作者： Ahad, Abdul Riyazuddin Sadiq, Jaffar Raju, Basava Lakshmi, Rama Anurag Univ Dept AI Hyderabad India Anurag Univ Dept IT Hyderabad India Sreenidhi Inst Sci & Tech Dept CSE DS Hyderabad India SRKR Engn Coll Dept CSD Bhimavaram India

ISBN: (纸本)9789819780334;9789819780310;9789819780303

This article accentuates the opinion examination in view of the order of flipkart client surveys. Feeling Examination known as Feeling Man-made consciousness or Assessment Mining. Computerized reasoning (artificial intelligence) alludes to the efficient recognizable proof, extraction, measurement, and investigation of emotional states and abstract information utilizing regular language handling and text examination. An application challenge in message mining and computational phonetics research is opinion examination of item audits. Here, the relationship between Flipkart item audits and the rating of the items given by the clients is to be considered. Different machine learning methods are utilized. In the first place, the surveys can be changed into vector portrayal utilizing various strategies, i.e., Sack of-words, and TF-IDF. Then, train the system by applying the Strategic Relapse, XGBoost, and Decision Trees. From that point forward, assess the models utilizing F1-Score. Thus, opinion examination is unmistakably applied to audits, review reactions, web and virtual entertainment, and medical care assets for purposes going from showcasing to client assistance to clinical medication.

关键词： natural language processing Opinion analysis Logistic regression K-mean clustering SVM classification

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Improving Arabic Fake News Detection Across Context-Aware Attention Deep Model Based on natural language processing 3rd

Improving Arabic Fake News Detection Across Context-Aware At...

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

作者： Khudair, Enas Tariq Lazzez, Onsa Hamdani, Tarek M. Sadiq, Ahmed T. Alimi, Adel M. Univ Sfax Natl Engn Sch Sfax ENIS REGIM Lab Res Grp Intelligent Machines BP 1173 Sfax 3038 Tunisia Ind 4 0 Res Lab Polytech Sfax IPSAS Ave 5 August Rue Said Aboubaker Sfax 3002 Tunisia Univ Monastir Higher Inst Comp Sci Mahdia ISIMa Dept Comp Sci Monastir Tunisia Univ Technol Iraq Dept Elect & Elect Engn Sci Baghdad Iraq Univ Johannesburg Fac Engn & Built Environm Johannesburg South Africa

ISBN: (纸本)9783031850660;9783031850677

With the enormous growth of social data in recent years, fake news detection has gained increasing research attention and has been widely explored in various languages. Arabic language nature imposes various challenges, such as the complicated morphological structure and the limited resources. Therefore, the current state-of-the-art methods for fake news detection remain to be enhanced, which inspired us to explore the application of the emerging deep-learning architecture to Arabic text classification. In this paper, we present an ensemble model that enhances the attention mechanism to detect fake news in Arabic sentences. An attention mechanism unit is incorporated to highlight the critical information from the contextual feature vectors. The context-related vectors generated by the attention mechanism layers are then concatenated and passed into a classifier to predict the final label. The experimental results show that the attention mechanism improves the model's performance while yielding 99.8% in accuracy.

关键词： Arabic language Social Network Fake News Arabic Fake News Detection

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Improve the Quality of Machine Translation in Low-Resource language Pairs 3rd

Improve the Quality of Machine Translation in Low-Resourc...

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3rd international conference on Advances in Information and Communication Technology, ICTA 2024

作者： Nguyen-Tien, Ha Nguyen-Ngoc, Phuc Nguyen-Van, Vinh Kim, Thai Dinh Hung Vuong University Phutho Viet Nam University of Engineering and Technology - VNU Hanoi Viet Nam International School Vietnam National University Hanoi100000 Viet Nam

ISBN: (纸本)9783031809422

Machine translation is a hard problem in natural language processing (NLP). Today, based on deep learning, the quality of machine translation systems is increasing strongly in rich-resource language pairs, but it is limited in low-resource language pairs, such as the Vietnamese-Laos language pair. In this work, we propose a method that depends on augmenting data and using a bilingual dictionary to improve the quality of machine translation for low-resource language pairs. Applying it to the Vietnamese-Laos language pair, it improved 14.0 BLEU scores for translating from Laos to Vietnamese and 3.5 BLEU scores for translating from Vietnamese to Laos. In addition, we built a good quality Vietnamese-Laos bilingual corpus that includes 182.208 sentence pairs to share with the community. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Machine translation

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SACR: Self-training with Saliency-Augmented Consistency Regularization for Few-Shot Learners

SACR: Self-training with Saliency-Augmented Consistency Regu...

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

作者： Feng, Yanyan Zhou, Yue Xue, Yun Li, Fenghuan Lin, Zehong School of Electronics and Information Engineering South China Normal University Foshan China Department of Artiffcial Intelligence School of Informatics Xiamen University Xiamen China School of Computer Science and Technology Guangdong University of Technology Guangzhou China

ISBN: (纸本)9798350368741

Pre-trained language models have made significant strides in natural language processing tasks, enabling flexible fine-tuning for downstream applications. However, in few-shot learning scenarios, pre-trained models face challenges related to overfitting due to limited training samples, which hinders their ability to capture data diversity and robustly handle input variations. To overcome these limitations, we propose the Saliency-Augmented Consistency Regularization (SACR) framework, a novel self-training strategy designed to improve few-shot learning performance and robustness. SACR consists of three key components: (1) Saliency-guided Data Perturbation, which uses saliency analysis to identify and perturb words that significantly influence model predictions, generating semantically consistent pseudo-samples;(2) Semantic-equivalent Sample Mining, which employs K-means clustering to select semantically similar samples and prevent semantic shift;and (3) Consistency Training, where regularization ensures consistency between the semantics and prediction distributions of the original and perturbed samples. We conducted extensive experiments on 15 public datasets to evaluate our approach. SACR significantly outperforms strong baseline models and demonstrates superior generalization capabilities compared to state-of-the-art methods, achieving an average 1.0% improvement in classification accuracy. © 2025 IEEE.

关键词： consistency regularization few-shot learning saliency self-training

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