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International Conference on Web Research (ICWR)

作者： Parinaz Soltanzadeh 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

Relation extraction in textual data is an important task in natural language processing with diverse applications in QA systems, knowledge graph construction and text analysis. The research conducted in this area is categorized into rule-based methods, machine learning methods and deep learning methods. However, to the best of our knowledge, none of the existing models have considered context-awareness in relation extraction. In this paper, we propose a method called ACORD, which first attempts to identify the context using ANOVA measure and second utilizes identified context to determine the relationship between two entities in a text through an ensemble learning model. The evaluation results demonstrate that ACORD achieves a precision of 88% in determining the correct relationship. Moreover, the non-numerical interpretation of the results allows for the identification of the most discriminative terms for each class.

关键词： Deep learning Context-aware services Text analysis Knowledge graphs Context awareness natural language processing Ensemble learning Analysis of variance Context modeling

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Multi-view Unsupervised Representation Learning via Integration of Fuzzy Rules and graph-based Adaptive Regularization

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IEEE Transactions on Fuzzy Systems 2025年

作者： Zhu, Yi Li, Dong Xi, Chao Pedrycz, Witold Macau University of Science and Technology School of Computer Science and Engineering China East China University of Technology School of Mechanical and Electronic Engineering Nanchang330013 China Gliwice Department of Measurement and Control Systems Gliwice44-100 Poland University of Alberta Department of Electrical and Computer Engineering EdmontonABT6G 2R3 Canada Istinye University Research Center of Performance and Productivity Analysis Istanbul34010 Turkey

With the rapid advancement of data acquisition technologies, multiview data have been widely applied in fields such as social networks, computer vision, and natural language processing. Multiview data typically contain information arising from different views or sensors, offering more perspectives for observation. The multiview nature also brings challenges, such as high dimensionality, noise, heterogeneity, and redundancy. Particularly, in scenarios with limited labeled data, traditional single-view learning methods often struggle to handle these complex issues. To address this, this article proposes an unsupervised multiview learning framework that integrates Takagi–Sugeno–Kang fuzzy systems and graph-based adaptive regularization (MvTSK-GAR) to handle the heterogeneity and redundancy in multiview data effectively. Specifically, this article first captures the uncertainty in multiview data through fuzzy rules and models the structural relationships between the data using graph-based adaptive regularization. The framework does not rely on a large amount of labeled data. Instead, it integrates complementary information coming from different views to automatically mine latent patterns, thus generating more accurate and stable data representations. Experimental results demonstrate that the proposed framework performs well in various real-world applications, particularly excelling in high-dimensional data processing and noise reduction. Good performance in multiple publicly available datasets validates the effectiveness of our approach. Thecode is available at https://***/r/MvTSK-GAR-E3FB/***. © 1993-2012 IEEE.

关键词： Unsupervised learning

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Speaker identification combining role knowledge graph correction and contextual block attention

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Expert Systems with Applications 2025年 284卷

作者： Tao, Ye Wang, Fei Cai, Yanchang Li, Wei College of Information Science and Technology Qingdao University of Science and Technology Shandong Qingdao26606 China China Telecom Research Institute No. 3 Penglaiyuan South Street Future Science City Beijing Changping China

Character dialogue play a crucial role in novels, serving as a key element for understanding both the plot and character relationships. With advancements in artificial intelligence and natural language processing, dialogue speaker recognition has seen significant progress. In this paper, we integrate a character role knowledge graph with a self-training mechanism to perform the speaker recognition on Chinese novel quotations using large-scale novel corpora. Quotes in novels can generally be classified as explicit or implicit. Implicit quotes require identifying speakers from extensive contextual information, which poses challenges for existing models in handling long and detail-rich contexts. In addition, existing end-to-end speaker recognition methods are ineffective because they do not fully consider the relationship between context and quotes. In this paper, we first propose a Narrative Unit-based Context Selection (NUCS) algorithm for determining the context to which quotes belong. Secondly, a speaker recognition method based on Role Knowledge graph Correction (RKGC) and Contextual Block Attention (CBA) is proposed. The proposed role knowledge graph correction algorithm improves speaker attribution by deeply analyzing role entities, their relationships, and relevant trigger words within quotations. It then refines candidate speaker probabilities obtained from the previous module. Additionally, the algorithm captures character relationships within the context using role mappings from existing novels. The CBA method introduces a block attention mechanism to capture character relationships within the context. It effectively computes the probability of each character being the speaker and determines the start and end indices of the most probable speaker segments. This allows the model to focus more precisely on speaker-related content, leading to more accurate speaker predictions. Experimental results demonstrate that our approach achieves competitive performance: o

关键词： Speech enhancement

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A Flexible Knowledge graph Error Detection Framework Combined with Semantic Information 12th

A Flexible Knowledge Graph Error Detection Framework Combine...

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12th CCF Conference on BigData, BigData 2024

作者： Zhao, Yangwu Liu, Yang Ao, Xiang He, Qing Henan Institute of Advanced Technology Zhengzhou University Zhengzhou China Beijing China Key Lab of Intelligent Information Processing Institute of Computing Technology CAS Beijing China

ISBN: (纸本)9789819610235

Knowledge graphs (KGs) are extensively utilized in numerous applications, including question-answering systems and recommender systems. However, knowledge graphs are often constructed through web crawling or crowdsourcing, leading to errors in the data. The task of knowledge graph error detection aims to identify inaccurate triplets in KGs and has received substantial attention in recent years. However, the majority of current error detection methods overlook the semantic information of the triplets, which can be vital for accurate error detection. In this paper, we introduce a Flexible Knowledge graph Error Detection Framework that integrates Semantic Information (FKED), which combines both structural and semantic information to detect errors within the knowledge graph. FKED first extracts the structural information of KGs using a graph embedding model. Next, FKED employs a pre-trained language model (PLM) to extract semantic information from the triplets. Then the structural and semantic information are combined to detect errors within the knowledge graph. FKED can be flexibly added to other structure-based error detection models, enhancing their capabilities in downstream tasks of knowledge graphs. We assess FKED using two benchmark datasets: FB15k-237 and WN18RR. Experimental results indicate that our method is both superior and effective, substantially improving the performance of the base models. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Knowledge graph

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基于深度学习模型的文化景观基因命名实体识别

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地球信息科学学报 2025年第1期27卷 207-225页

作者：林洁如胡最衡阳师范学院地理与旅游学院衡阳421002 传统村镇文化数字化保护与创意利用技术国家地方联合工程实验室衡阳421002

【目的】针对当前传统聚落研究缺乏结合自然语言处理的原理与方法从海量文本数据中提取文化景观基因信息的相关探索,本文提出景观基因命名实体(Traditional Settlement Landscape Genes Named Entity,TSLGNE)的概念,并以邵阳市48个传统... 详细信息

【目的】针对当前传统聚落研究缺乏结合自然语言处理的原理与方法从海量文本数据中提取文化景观基因信息的相关探索,本文提出景观基因命名实体(Traditional Settlement Landscape Genes Named Entity,TSLGNE)的概念,并以邵阳市48个传统村落为案例,采用BERT-BiLSTM-CRF深度学习模型开展TSLGNE的识别研究。【方法】首先,结合地理实体特征与文化景观基因理论及其分类体系,提出TSLGNE的概念、分类体系与知识表达方法。其次,基于TSLGNE的分类体系与扩展BIOES标注方法,对研究案例源文本数据进行语料标注,构建了相应的语料库。随后,基于BERT-BiLSTM-CRF深度学习模型识别并提取语料数据中的TSLGNE信息。最后,通过Neo4j图数据库对获取到的TSLGNE知识进行组织与储存,并基于此对区域传统聚落及其TSLGNE进行空间特征分析。【结果】本文模型能够有效识别文本数据中的建筑、环境、文化等12类景观基因实体信息,总体精确率、召回率、F1值相较对比模型均有提升。与BiLSTM-CRF、BERT-CRF模型相比,本模型的F1值分别提高11%与1%。特别地,本模型对于语料数据质量差且语义复杂的实体的识别效果有极大提升,如文化基因C3类实体识别的F1较对比模型分别提高了31%与5%。【结论】此方法能够有效地对区域复杂的传统聚落文化基因特征及其关系进行空间分析处理,对今后结合GIS和数据挖掘方法分析传统聚落的重要文化特征及传统聚落知识服务等具有较好的借鉴意义。

关键词：传统聚落文化景观基因自然语言处理命名实体识别 BERT预训练语言模型 Neo4j图数据库知识图谱

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Optimizing Symptom and Disease Representation with Structural and Textual Information Integration

Optimizing Symptom and Disease Representation with Structura...

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Multi-Agent Systems for Collaborative Intelligence (ICMSCI), International Conference on

作者： M. Ramya Meenakshi B K Kanagalakshmi M Department Of CSE St. Joseph's Institute of Technology Chennai-119

ISBN: (数字)9798331509828

ISBN: (纸本)9798331509835

Medical websites and healthcare records now contain an enormous variety of health-related data from online and offline medical check-ins and consultations. However, because there are few symptom-disease connections, it is difficult to leverage user-generated material to help patients get around online and receive medical attention offline. The casual and lighthearted ways that symptoms are expressed in the data are what lead to the severe sparsity. To improve therapeutic interventions and diagnostic systems, accurate portrayal of diseases and symptoms is essential. Medical websites and healthcare records now contain an enormous variety of health-related data from online and offline medical check-ins and consultations. However, because there are few symptom-disease connections, it is difficult to leverage content created by users to help patients get around online and receive healthcare offline. The casual and light hearted ways that symptoms are expressed in the data are what lead to the severe sparsity. To improve therapies and diagnostic systems, accurate portrayal of diseases and symptoms is essential. Our method involves a multi-phase approach: first, extracting and encoding structured information from EHRs using advanced techniques such as graph-based neural networks; second, applying natural language processing (NLP) techniques to capture and encode relevant information from clinical texts; and finally, fusing these two modalities to create an enriched representation of medical entities. We evaluate our approach on a dataset of medical records and clinical notes, demonstrating improvements in predictive accuracy and interpretability compared to traditional single-modality methods. The results highlight the effectiveness of combining structural and textual data in advancing the state-of-the-art in medical representation learning. This fusion approach not only enhances the precision of symptom and disease modeling but also provides deeper insights into complex m

关键词： Accuracy Smart healthcare Medical services Prediction algorithms Throughput natural language processing Sensors Data mining Medical diagnostic imaging Diseases

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Intelligent Hiring Software- Job Recommendation System and Profile Builder

Intelligent Hiring Software- Job Recommendation System and P...

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Advances in Computer Science, Electrical, Electronics, and Communication Technologies (CE2CT), International Conference on

作者： Shubham Kumar Nikhil Rajput Varsha Mishra Gunjan Aggarwal CSE (AI & ML) Sharda University Greater Noida India CSE Sharda University Greater Noida India

ISBN: (数字)9798331518578

ISBN: (纸本)9798331518585

In today's world, where there are multiple career options, still there is a difficulty in finding the securing and a good job that perfectly matches an individual's skill due to the absence of a very personalized recommendation systems. Additionally, the lack of proper guidance in making resumes, it is very much harder to find the best fit opportunity and get shortlisted for a company's hiring process. To alleviate those times consuming task, we develop and implement a job recommendation system for online job searching. This paper proposes a new method for overcoming these challenges by the development of Intelligent Hiring Software- job recommendation system and profile builder. By using the Naive Bayes our platform categorizes users according to their stated skills and profiles in their resumes and Hybrid approach of graph- based model enables job the recommendations. natural language processing used for betterment of the resumes or profile buildup. Our Platform uses the NLP for profile improvement through the suggestions based on the previous profile and job description. Your career profile can be greatly improved using NLP.

关键词： Engineering profession Terminology Filtering Collaborative filtering Resumes Measurement uncertainty Named entity recognition Bayes methods Interviews Recommender systems

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融合局部上下文的双图文档级关系抽取方法

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小型微型计算机系统 2025年第3期46卷 535-541页

作者：闻克妍纪婉婷宋宝燕辽宁大学信息学部沈阳110036

文档级关系抽取是一项复杂的自然语言处理任务,旨在识别出文档中存在的实体,并预测实体之间的关系.相较于句子级关系抽取任务,文档级关系抽取面临更大的挑战,因为它需要考虑整个文档的语义信息和句子间的逻辑关系.针对这一任务,提出了... 详细信息

文档级关系抽取是一项复杂的自然语言处理任务,旨在识别出文档中存在的实体,并预测实体之间的关系.相较于句子级关系抽取任务,文档级关系抽取面临更大的挑战,因为它需要考虑整个文档的语义信息和句子间的逻辑关系.针对这一任务,提出了一种融合局部上下文信息的双图推理方法(BRM)用于文档级关系抽取.该方法首先识别文档中的实体提及,并构造了一个提及级别的异构图来表示这些提及以及它们之间的关系.在获得提及级别的表示后,方法进一步构建了一个实体级别的推理图,通过聚合提及级别的信息来形成实体级别的表示,以判断实体之间的关系.该方法在文档级关系抽取公开数据集DocRED上进行了实验.实验结果表明,与现有的文档级关系抽取方法相比,该方法能够更准确地识别实体并预测它们之间的关系.

关键词：文档级关系抽取局部上下文双图推理数据集成

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Normalized entity graph for computing local coherence 9

Normalized entity graph for computing local coherence

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9th workshop on graph-based methods for natural language processing, Textgraphs 2014, in conjunction with the Conference on Empirical methods in natural language processing, EMNLP 2014

作者： Mesgar, Mohsen Strube, Michael Heidelberg Institute for Theoretical Studies gGmbH Schloss-Wolfsbrunnenweg 35 Heidelberg69118 Germany

ISBN: (纸本)9781937284961

Guinaudeau and Strube (2013) introduce a graph based model to compute local entity coherence. We propose a computationally efficient normalization method for these graphs and then evaluate it on three tasks: sentence ordering, summary coherence rating and readability assessment. In all tasks normalization improves the results. © 2014 Association for Computational Linguistics

关键词： graphic methods

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graph-based semi-supervised learning for natural language understanding 13

Graph-based semi-supervised learning for natural language un...

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13th workshop on graph-based methods for natural language processing, Textgraphs 2019, in conjunction with the 2019 Conference on Empirical methods in natural language processing and 9th International Joint Conference on natural language processing, EMNLP-IJCNLP 2019

作者： Qiu, Zimeng Cho, Eunah Ma, Xiaochun Campbell, William M. Electrical and Computer Engineering Department Carnegie Mellon University United States Amazon Alexa AI

ISBN: (纸本)9781950737864

Semi-supervised learning is an efficient method to augment training data automatically from unlabeled data. Development of many natural language understanding (NLU) applications has a challenge where unlabeled data is relatively abundant while labeled data is rather limited. In this work, we propose transductive graphbased semi-supervised learning models as well as their inductive variants for NLU. We evaluate the approach's applicability using publicly available NLU data and models. In order to find similar utterances and construct a graph, we use a paraphrase detection model. Results show that applying the inductive graph-based semi-supervised learning can improve the error rate of the NLU model by 5%. © 2019 EMNLP-IJCNLP 2019 - graph-based methods for natural language processing - proceedings of the 13th workshop. All rights reserved.

关键词： graphic methods

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