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A Survey on Extracting knowledge Graphs by Employing natural language processing 10

A Survey on Extracting Knowledge Graphs by Employing Natural...

引用

10th international conference on Web Research, ICWR 2024

作者： Damirchi, Reza Saboohi, Hadi Amini, Amineh Islamic Azad University Karaj Branch Department of Computer Engineering Karaj Iran

ISBN: (纸本)9798350394986

We live in a world of information, and with the ever-increasing rate of content growth, we have no choice but to use machine-based solutions to manage, classify, and use it. However, the produced content is often unstructured, and for computer systems to be able to read and process it, there is no choice but to convert it to structured text. knowledge graph is one of the ways to represent data in a structured way, and its roots are found in the semantic web. There are different approaches to creating a knowledge graph, but given the significant advances in artificial intelligence, the use of natural language processing is one of the most accurate and automatic ways to do this. In this paper, an attempt has been made to review the latest methods proposed for creating a knowledge graph based on natural language processing. This first allows us to have a better understanding of this research area;in addition, the approaches used by researchers in this field are reviewed and the pros and cons of these approaches are identified. Also, the existing research areas for further work and even the initial ideas for new creative solutions are provided for researchers. © 2024 IEEE.

关键词： Semantic Web

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Research on large language model news recommendation technology based on mixed prompt 24

Research on large language model news recommendation technol...

引用

3rd international conference on Artificial Intelligence and Intelligent Information processing, AIIIP 2024

作者： Wei, Han Qu, Dan Xu, Minchen Peng, Sisi Guo, Zhigang Chen, Gang Information Engineering University Henan Zhengzhou China

ISBN: (纸本)9798400707308

With the emergence of massive news every day leading to information overload, it is difficult for people to select the content they are really interested in from numerous news articles. The language knowledge and strong generalization ability of the large model itself provide the possibility of intelligent news recommendation. This paper does not fine-tune the big language model, but uses the prompt engineering technology of the big language model to design a hybrid prompt engineering instruction combining prompt words, context learning and thought chain prompt to stimulate the recommendation performance of the big language model, and realizes the intelligent news recommendation of the big language model. © 2024 Copyright held by the owner/author(s).

关键词： engineering education

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Supervised Contrastive Learning for Short Text Classification in natural language processing 14

Supervised Contrastive Learning for Short Text Classificatio...

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14th international conference on Computer and knowledge engineering, ICCKE 2024

作者： Esmaeili, Mitra Vahdat-Nejad, Hamed Faculty of Electrical and Computer Engineering University of Birjand Birjand Iran

ISBN: (纸本)9798331511272

In recent years, the swift progress in information retrieval technologies has positioned text classification as a key area of research. Classifying short texts represents a major challenge within natural language processing. Given the growing prevalence of social media during critical events like hurricanes, accurately categorizing these texts is essential for facilitating relief operations. Tweets are concise and have an informal tone, which creates unique challenges for effective classification. Supervised contrastive learning has recently become popular as a strong machine learning method. It offers significant improvements over traditional approaches, especially in the area of natural language processing. This paper introduces a supervised contrastive learning methodology designed to enhance the accuracy of short-text classification while maintaining the model’s generalization capability. Our approach consistently surpasses existing state-of-the-art techniques, delivering better accuracy and stability across different ranges of text classification tasks. ©2024 IEEE.

关键词： Contrastive Learning

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AnxiBERT - Prompt engineering for Social Media Anxiety Detection 24

AnxiBERT - Prompt Engineering for Social Media Anxiety Detec...

引用

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

作者： Senanayake, Ashala Lakmini Liyanage, Sidath Faculty of Computing and Technology University of Kelaniya Colombo Sri Lanka

ISBN: (纸本)9798400717383

Social media anxiety detection refers to the approach of recognizing various signs of anxiety in a person based on their social media engagements. A person who experiences anxiety may express negative emotional responses like frustration, apprehension, and nervousness. Often, an individual's emotional tone in social media posts resonates with their feelings, providing valuable insights into their mental condition. The increase in socioeconomic adversities has resulted in a rapid growth of emotional disturbances in society. Identifying these emotional disturbances has become vital necessity for obtaining timely support and fostering better overall well-being by addressing the mental health component of the definition of health. Adapting pretrained language models (PLMs) for downstream tasks like digital mental health has therefore become a current trend even though there is no specific model for anxiety detection. There are many approaches for enhancing PLMs to determine downstream performance. In this research, we introduce a novel model, AnxiBERT, consists of soft prompt engineering logic, to adapt a PLMs knowledge for anxiety detection. To our knowledge, this is the first attempt to adapt a PLM's knowledge for anxiety detection using prompt engineering. Multiple evaluations show that our model demonstrates state-of-the-art performance compared to existing baselines. © 2024 Copyright held by the owner/author(s).

关键词： Sociology

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ParaFusion-Extended: Large Scale Paraphrase Dataset Integrating Lexico-Phrasal knowledge 30th

ParaFusion-Extended: Large Scale Paraphrase Dataset Integrat...

引用

30th international conference on Computational and Experimental engineering and Sciences

作者： Jayawardena, Lasal Yapa, Prasan Robert Gordon Univ Garthdee HouseGarthdee Rd Aberdeen AB10 7AQ Scotland Inst Informat Technol 57 Ramakrishna Rd Colombo 00600 Sri Lanka

ISBN: (纸本)9783031774911;9783031774898;9783031774881

Paraphrasing, the art of rephrasing text while retaining its original meaning, lies at the core of natural language understanding and generation. With the rise of demand for more domain-specialized models, high-quality data is more valued than ever;this includes paraphrasing. ParaFusion-Extend (PFE) is a large-scale dataset driven by Large language Models incorporating lexical and phrasal knowledge. The dataset is curated to contain high-quality diverse paraphrase pairs and also separate knowledge bases that could be used for research work and data augmentation models. We show that PFE offers around at least a 30% increase in syntactic and lexical diversity compared to the original data sources that are commonly used. We demonstrate the effectiveness of PFE on several downstream tasks such as few-shot learning and training on sentence embeddings. We utilize a gold-standard evaluation scheme, which is further strengthened by human evaluation that shows the potential of PFE in advancing paraphrase generation.

关键词： paraphrase generation natural language processing natural language generation knowledge representation large language models data augmentation sentence embeddings few-shot learning

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Deep Reasoning of Large Models Based on knowledge Graph 16

Deep Reasoning of Large Models Based on Knowledge Graph

引用

2024 16th international conference on Graphics and Image processing, ICGIP 2024

作者： Chenghao, Cao Du, Zhenlong Li, Xiaoli College of Computer and Information Engineering Nanjing Tech University China

ISBN: (数字)9781510688780

ISBN: (纸本)9781510688773

Although large language models (LLMs) have achieved significant success in various tasks, they often struggle with hallucination issues in scenarios requiring deep reasoning. Incorporating external knowledge into LLM reasoning can alleviate some of these issues. This paper proposes an efficient graph search method that integrates knowledge graphs. A knowledge graph is firstly constructed from the original document. This graph is then partitioned into multiple relevant communities using a community detection algorithm, and natural language summaries for each community are generated using an LLM. During the search phase, the LLM performs beam search on the knowledge graph, iteratively exploring multiple potential reasoning paths on the graph until it determines that it can answer the question based on the current reasoning path. Experiments demonstrate that our search method has lower computational costs and better generalizability, outperforming even large LLMs like GPT-4 in certain vertical domains. © 2025 SPIE.

关键词： knowledge graph

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Faithful Fake News Detection Based on natural language Explanation Generation 24

Faithful Fake News Detection Based on Natural Language Expla...

引用

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

作者： Yoshida, Ko Zhang, Jianwei Graduate School of Information Sciences Tohoku University Miyagi Sendai Japan Faculty of Science and Engineering Iwate University Iwate Morioka Japan

ISBN: (纸本)9798400717383

In this paper, we propose a fake news detection model that generates natural language explanations and leverages these explanations for prediction. Deep learning models, particularly Large language Models like BERT, are effective for fake news detection. However, interpreting and explaining predictions from these complex models remains challenging. Algorithms like LIME and SHAP provide numerical explanations, but these are often insufficient for users without technical knowledge. Our model generates text-based explanations, making them more user-friendly. Experimental results show our model improves binary classification accuracy and F1-score. Additionally, we demonstrate that the explanations contribute to classification, ensuring the model's faithfulness to the generated explanations. © 2024 Copyright held by the owner/author(s).

关键词： Deep learning

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BigText-QA: Question Answering over a Large-Scale Hybrid knowledge Graph 13th

BigText-QA: Question Answering over a Large-Scale Hybrid Kno...

引用

13th international conference on Big Data Technologies and Applications (BDTA)

作者： Xu, Jingjing Biryukov, Maria Theobald, Martin Venugopal, Vinu Ellampallil Univ Luxembourg L-4365 Esch Sur Alzette Luxembourg Int Inst Informat Technol IIIT Bangalore Karnataka India

ISBN: (纸本)9783031522642;9783031522659

Answering complex questions over textual resources remains a challenge, particularly when dealing with nuanced relationships between multiple entities expressed within natural-language sentences. To this end, curated knowledge bases (KBs) like YAGO, DBpedia, Freebase, and Wikidata have been widely used and gained great acceptance for question-answering (QA) applications in the past decade. While these KBs offer a structured knowledge representation, they lack the contextual diversity found in natural-language sources. To address this limitation, BigText-QA introduces an integrated QA approach, which is able to answer questions based on a more redundant form of a knowledge graph (KG) that organizes both structured and unstructured (i.e., "hybrid") knowledge in a unified graphical representation. Thereby, BigText-QA is able to combine the best of both worlds-a canonical set of named entities, mapped to a structured background KB (such as YAGO or Wikidata), as well as an open set of textual clauses providing highly diversified relational paraphrases with rich context information. Our experimental results demonstrate that BigText-QA outperforms DrQA, a neural-network-based QA system, and achieves competitive results to QUEST, a graph-based unsupervised QA system.

关键词： Question Answering Large-Scale Graph Hybrid knowledge Graph natural language processing

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MApp-KG: Mobile App knowledge Graph for Document-Based Feature knowledge Generation 36th

MApp-KG: Mobile App Knowledge Graph for Document-Based Featu...

引用

36th international conference on Advanced Information Systems engineering (CAiSE)

作者： Motger, Quim Franch, Xavier Marco, Jordi Univ Politecn Cataluna Barcelona Spain

ISBN: (纸本)9783031609992;9783031610004

Mobile app repositories serve as large-scale crowdsourced information systems used for various document-based software engineering tasks, leveraging product descriptions, user reviews, and other natural language documents. Particularly, feature extraction (i.e., identifying functionalities or capabilities of a mobile app mentioned in these documents) is key for product recommendation, topic modelling, and feedback analysis. However, researchers often face domain-specific challenges in mining these repositories, including the integration of heterogeneous data sources, large-scale data collection, normalization and ground-truth generation for feature-oriented tasks. In this paper, we introduce MAppKG, a combination of software resources and data artefacts in the field of mobile app repositories aimed at supporting feature-oriented knowledge generation tasks. Our contribution provides a framework for automatically constructing a knowledge graph that models a domain-specific catalog of natural language documents related to mobile applications. We distribute MApp-KG through a public triplestore, enabling its immediate use for future research and replication of our findings.

关键词： Mobile Apps knowledge Graph User Reviews natural language Document Feature Extraction RDF

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Communication Assistance for Blind and Deaf using natural language processing

Communication Assistance for Blind and Deaf using Natural La...

引用

2024 international conference on knowledge engineering and Communication Systems, ICKECS 2024

作者： Sangeetha, V. Pani, Ishwariya Yasaswini, K. Nikita, B. Bhattacharjee, Arpita Ramaiah Institute of Technology Department of Cse Bangalore India

ISBN: (纸本)9798350359688

Having difficulties like being visually impaired, hard of listening to, dumb are a more amount of difficulty. Technology and innovation have motivated humans to grow to be depending on solace but there exists an underprivileged growth of those who are fighting for finding a creative manner that could make the procedure of conversation less difficult for them. The traditional ways of communication and knowing any information for disabled people is very time consuming as they had to find someone to ask for the information and wait for their response, and accurate answers are not received and language or gestures act as barriers. To overcome these problems, in this paper natural language processing(NLP) and Speech Recognition is used. NLP and Speech recognition eradicate language barriers between the disabled and the non-disabled person. The main objectives is to convert the spoken audio to text format, speech-to-text is used and to convert the typed text to audio format, text-to-speech is used. Speech to text and text to speech features in the website application are faster. This application gives a user friendly platform for the blind and deaf. © 2024 IEEE.

关键词： Speech recognition

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