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Contextual Analysis Based on Information Retrieval: Technical Analysis of Chinese and English Cross-language Q&A System 14th

Contextual Analysis Based on Information Retrieval: Technica...

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14th international conference on Frontier Computing, FC 2024

作者： Zhou, Jian School of Education and Foreign Languages Wuhan Donghu University Hubei Wuhan430212 China

ISBN: (纸本)9789819623907

Currently, the surge in Internet users across the globe has led to a surge in the linguistic diversity of online information resources. In this vast information environment, cross-lingual information retrieval has become a central theme in academic research. At the same time, automated Q&A technology is at the forefront of natural language processing, providing intelligent and progressive solutions for information retrieval. In the field of Q&A information retrieval, the operation of the system consists of three key stages: in-depth examination of the question, followed by comprehensive information exploration, and finally the refinement of key information through answers. The research focuses on the knowledge search of different language Q&A, aiming to remove language barriers for users and promote seamless multilingual communication. The system is designed to meet the challenges posed by the information explosion and is committed to providing users with efficient information extraction tools to improve search performance in complex multilingual environments. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Question answering

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Towards Human-Bot Collaborative Software Architecting with ChatGPT 23

Towards Human-Bot Collaborative Software Architecting with C...

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27th international conference on Evaluation and Assessment in Software engineering (EASE)

作者： Ahmad, Aakash Waseem, Muhammad Liang, Peng Fahmideh, Mahdi Aktar, Mst Shamima Mikkonen, Tommi Lancaster Univ Leipzig Sch Comp & Commun Leipzig Germany Univ Jyvaskyla Fac Informat Technol Jyvaskyla Finland Wuhan Univ Sch Comp Sci Wuhan Peoples R China Univ Southern Queensland Sch Business Darling Hts Qld Australia

ISBN: (纸本)9798400700446

Architecting software-intensive systems can be a complex process. It deals with the daunting tasks of unifying stakeholders' perspectives, designers' intellect, tool-based automation, pattern-driven reuse, and so on, to sketch a blueprint that guides software implementation and evaluation. Despite its benefits, architecture-centric software engineering (ACSE) suffers from a multitude of challenges. ACSE challenges could stem from a lack of standardized processes, socio-technical limitations, and scarcity of human expertise etc. that can impede the development of existing and emergent classes of software. Software Development Bots (DevBots) trained on large language models can help synergise architects' knowledge with artificially intelligent decision support to enable rapid architecting in a human-bot collaborative ACSE. An emerging solution to enable this collaboration is ChatGPT, a disruptive technology not primarily introduced for software engineering, but is capable of articulating and refining architectural artifacts based on natural language processing. We detail a case study that involves collaboration between a novice software architect and ChatGPT to architect a service-based software. Future research focuses on harnessing empirical evidence about architects' productivity and explores socio-technical aspects of architecting with ChatGPT to tackle challenges of ACSE.

关键词： Software Architecture ChatGPT Large language Models DevBots

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NeCo@ALQAC 2023: Legal Domain knowledge Acquisition for Low-Resource languages through Data Enrichment 15

NeCo@ALQAC 2023: Legal Domain Knowledge Acquisition for Low-...

引用

15th international conference on knowledge and Systems engineering, KSE 2023

作者： Nguyen, Hai-Long Nguyen, Dieu-Quynh Nguyen, Hoang-Trung Pham, Thu-Trang Nguyen, Huu-Dong Nguyen, Thach-Anh Vuong, Thi-Hai-Yen Nguyen, Ha-Thanh Vnu University of Engineering and Technology Hanoi Viet Nam National Institute of Informatics Tokyo Japan

ISBN: (纸本)9798350329742

In recent years, natural language processing has gained significant popularity in various sectors, including the legal domain. This paper presents NeCo Team's solutions to the Vietnamese text processing tasks provided in the Automated Legal Question Answering Competition 2023 (ALQAC 2023), focusing on legal domain knowledge acquisition for low-resource languages through data enrichment. Our methods for the legal document retrieval task employ a combination of similarity ranking and deep learning models, while for the second task, which requires extracting an answer from a relevant legal article in response to a question, we propose a range of adaptive techniques to handle different question types. Our approaches achieve outstanding results on both tasks of the competition, demonstrating the potential benefits and effectiveness of question answering systems in the legal field, particularly for low-resource languages, © 2023 IEEE.

关键词： knowledge acquisition

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Visual-language Collaborative Representation Network for Broad-Domain Few-Shot Image Classification 24

Visual-Language Collaborative Representation Network for Bro...

引用

32nd ACM international conference on Multimedia, MM 2024

作者： Guo, Qianyu Ren, Jieji Wang, Haofen Wu, Tianxing Ge, Weifeng Zhang, Wenqiang Shanghai Key Lab of Intelligent Information Processing School of Computer Science Fudan University Shanghai China School of Mechanical Engineering Shanghai Jiao Tong University Shanghai China College of Design and Innovation Tongji University Shanghai China School of Computer Science and Engineering Southeast University Nanjing China School of Computer Science Fudan University Shanghai China Engineering Research Center of AI & Robotics Ministry of Education Academy for Engineering & Technology Fudan University Shanghai China

ISBN: (纸本)9798400706868

Visual-language models based on CLIP have shown remarkable abilities in general few-shot image classification. However, their performance drops in specialized fields such as healthcare or agriculture, because CLIP's pre-training does not cover all category data. Existing methods excessively depend on the multi-modal information representation and alignment capabilities acquired from CLIP pre-training, which hinders accurate generalization to unfamiliar domains. To address this issue, this paper introduces a novel visual-language collaborative representation network (MCRNet), aiming at acquiring a generalized capability for collaborative fusion and representation of multi-modal information. Specifically, MCRNet learns to generate relational matrices from an information fusion perspective to acquire aligned multi-modal features. This relationship generation strategy is category-agnostic, so it can be generalized to new domains. A class-adaptive fine-tuning inference technique is also introduced to help MCRNet efficiently learn alignment knowledge for new categories using limited data. Additionally, the paper establishes a new broad-domain few-shot image classification benchmark containing seven evaluation datasets from five domains. Comparative experiments demonstrate that MCRNet outperforms current state-of-the-art models, achieving an average improvement of 13.06% and 13.73% in the 1-shot and 5-shot settings, highlighting the superior performance and applicability of MCRNet across various domains. © 2024 ACM.

关键词： Visual languages

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knowledge Graph Entity Typing with Curriculum Contrastive Learning 31

Knowledge Graph Entity Typing with Curriculum Contrastive Le...

引用

31st international conference on Computational Linguistics, COLING 2025

作者： Wang, Hao Nuo, Minghua Jiang, Shan College of Computer Science Inner Mongolia University Inner Mongolia Hohhot010021 China National and Local Joint Engineering Research Center of Intelligent Information Processing Technology for Mongolian Inner Mongolia Hohhot010021 China Inner Mongolia Key Laboratory of Multilingual Artificial Intelligence Technology Inner Mongolia Hohhot010021 China

ISBN: (纸本)9798891761964

The knowledge Graph Entity Typing (KGET) task aims to predict missing type annotations for entities in knowledge graphs. Most recent studies only focus on the structural information from an entity's neighborhood or semantic information from textual representations of entities or relations. In this paper, inspired by curriculum learning and contrastive learning, we propose the CCLET model using the Curriculum Contrastive Learning strategy for KGET, which uses the Pre-trained language Model (PLM) and the graph model to fuse the entity related semantic and the structural information of the knowledge Graph (KG) respectively. Our CCLET model consists of two main parts. In the knowledge Fusion part, we design an Enhanced-MLP architecture to fuse the text of the entity's description, related triplet and tuples;In the Curriculum Contrastive Learning part, we define the difficulty of the training course by controlling the level of added noise, we aim to accurately learn with curriculum contrastive learning strategy from easy to difficult. Our extensive experiments demonstrate that the CCLET model outperforms recent state-of-the-art models, verifying its effectiveness in the KGET task. © 2025 Association for Computational Linguistics.

关键词： Contrastive Learning

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MediBot: Healthcare Assistant on Mental Health and Well Being 7

MediBot: Healthcare Assistant on Mental Health and Well Bein...

引用

7th IEEE international conference on Computational Systems and Information Technology for Sustainable Solutions, CSITSS 2023

作者： Kavyashree, N. Usha, J. Rv College of Engineering Department of Mca Bengaluru560059 India

ISBN: (纸本)9798350343144

This paper discusses the mental health issues of individuals and provides a personalized solution in the form of a medical chatbot to address the mental health and well-being to individuals seeking assistance for their mental health issues. This solution leverages the power of artificial intelligence and natural language processing techniques. The chatbot provides a user-friendly interface for users to interact and receive guidance on various mental health topics. The materials and methods employed in the development process include data collection from reputable sources, preprocessing of the collected data, platform selection, chatbot architecture design, integration of AI and NLP algorithms, personalization mechanisms, and the creation of a comprehensive knowledge base, testing and evaluation encompassing usability, functionality, accuracy, and user feedback assessments. The deployment and maintenance for continuous improvement, monitoring, logging, and user support. This medical chatbot holds promise for improving accessibility and efficiency in mental health care, potentially enhancing individuals' well-being and providing valuable resources in the field of mental health. © 2023 IEEE.

关键词： natural language processing systems

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Script2Graph: Auto-construct Screenplay Text World by Mining Contextual Information 3

Script2Graph: Auto-construct Screenplay Text World by Mining...

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3rd IEEE international conference on Software engineering and Artificial Intelligence, SEAI 2023

作者： Wang, Depei Cheng, Lianglun Liu, Hongwei Li, Ruihao Wang, Huilin Liu, Hongrui Guangdong University of Technology School of Automation Guangzhou China Guangdong University of Technology School of Computer Science and Technology Guangzhou China Tianjin Foreign Studies University Tianjin China Guangdong University of Technology School of Physics and Optoelectronic Engineering Guangzhou China Hanshan Normal University Chaozhou China San Jose State University San Jose United States

ISBN: (纸本)9798350337952

With the rapid development of artificial intelligence and the continuous emergence of innovative technologies, its application in movie scenes has gained attention. Meanwhile, the application of natural language processing techniques in movie scripts has gradually attracted the attention of researchers, making text information extraction more intelligent and convenient. However, most of the previous works for unstructured script processing ignore the theoretical guidance of computational language and lack the framework of knowledge schema. Therefore, we propose Script2Graph method based on text world theory to solve unstructured text information extraction and knowledge organization, and realize the automatic construction of script text world. Our experiments demonstrate that the method is effective in extracting text world elements and using high-quality information in downstream applications. © 2023 IEEE.

关键词： Computational linguistics

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Continuous Pre-training of TAPAS Model with E-commerce Structured Data 4

Continuous Pre-training of TAPAS Model with E-commerce Struc...

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4th IEEE international conference on Information Technology, Big Data and Artificial Intelligence, ICIBA 2024

作者： Chen, Xiaoyu Du, Shuxin Qiu, Yi Huzhou University School of Engineering Zhejiang China Huzhou Key Laboratory of Intelligent Sensing and Optimal Control for Industrial Systems Zhejiang China

ISBN: (纸本)9798350363586

Currently, large models perform extremely well in natural language processing (NLP). Pre-trained large-scale language models (such as GPT-4, BERT, T5, etc.) have achieved significant performance improvements in a variety of NLP tasks. However, applying these models to specific domains still faces significant challenges, such as lack of domain-specific knowledge and insufficient adaptation to domain-specific structured data. In this article, we take the e-commerce field as an example, clean and convert various collected e-commerce structured data into specific formats, and conduct continuous pre-training of the TAPAS model. During the pre-training process, parameters are gradually adjusted through backpropagation to minimize the loss function to improve the accuracy of the model. By comparing various indicators with other models. Experimental results show that the TAPAS model after continuous pre-training shows better performance in processing structured data tasks in the e-commerce field, significantly improving its accuracy in tasks such as question answering and information extraction in this field. © 2024 IEEE.

关键词： Data accuracy

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TextGraphs-16 natural language Premise Selection Task: Zero-Shot Premise Selection with Prompting Generative language Models 16

TextGraphs-16 Natural Language Premise Selection Task: Zero-...

引用

16th Workshop on Graph-Based Methods for natural language processing, TextGraphs 2022, in conjunction with the 29th international conference on Computational Linguistics, COLING 2022

作者： Kovriguina, Liubov Teucher, Roman Wardenga, Robert Fraunhofer IAIS Dresden Germany InfAI Leipzig Germany

Automated theorem proving can benefit a lot from methods employed in natural language processing, knowledge graphs and information retrieval: this non-trivial task combines formal languages understanding, reasoning, similarity search. We tackle this task by enhancing semantic similarity ranking with prompt engineering, which has become a new paradigm in natural language understanding. None of our approaches requires additional training. Despite encouraging results reported by prompt engineering approaches for a range of NLP tasks, for the premise selection task vanilla re-ranking by prompting GPT-3 doesn’t outperform semantic similarity ranking with SBERT, but merging of the both rankings shows better results. © 2022 Proceedings - international conference on Computational Linguistics, COLING. All rights reserved.

关键词： Formal languages

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Leveraging Transfer Learning for Improved Medical Image Classification

Leveraging Transfer Learning for Improved Medical Image Clas...

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2024 international conference on Optimization Computing and Wireless Communication, ICOCWC 2024

作者： Wadhwa, Durgesh Kumar, Sunil Rengarajan, A. Sanskriti University Department of Chemistry Uttar Pradesh Mathura India College of Engineering Teetthanker Mahaveer University Uttar Pradesh Moradabad India Department of Computer Science and Information Technology Bangalore India

ISBN: (纸本)9798350383348

Transfer studying is a powerful approach for enhancing the performance of systems, and getting to know models in new domains. The primary concept is transferring existing information from a source area to a goal area with special classes and contexts. Switch-gaining knowledge is usually used in many one-of-a-kind fields consisting of pc vision and natural language processing. This paper focuses on the utility of transfer in getting to know in medical image category. We discover how to leverage current models and strategies from the pc imaginative and prescient domain for progressed accuracy and performance in scientific picture class. Mainly, we gift a complete evaluation of the literature awareness on recent techniques for utilizing transfer to know to classify diverse forms of scientific images, including computed tomography, radiography, and chest X-rays scans, and magnetic resonance imaging scans. We also talk about the capability of new guidelines for the utility of switch learning in medical photo classes. We speak about aspects that want special attention when running with clinical photos, including the want for special preprocessing steps and the significance of incorporating area understanding. Finally, we speak about the capability of switch-gaining knowledge of for improving prognosis accuracy and efficiency in medical imaging. © 2024 IEEE.

关键词： diagnosis incorporating potential comprehensive processing

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