检索结果-南通市图书馆

第35届中国过程控制会议

作者： Ronghui Liu Hao Ren Wei Cui Chunhua Yang Weihua Gui Xiaojun Liang Peng Tang2Jie Han School of Future Technology South China University of Technology Industrial Intelligence Basic Studio Peng Cheng Laboratory

The metallurgical industry, characterized as a typical process industry, exhibits features such as frequent changes in raw material compositions, severe fluctuations in operational conditions, and complex physicochemical reaction *** attributes result in the knowledge within the field being extensive and intricate, making centralized management and effective application challenging. Addressing these challenges, this paper introduces a method that leverages the expertise of local knowledge bases and the powerful semantic understanding and text generation capabilities of Generative AI Large Models(GLM) to enhance metallurgical industry knowledge and facilitate question-answering(Q&A). The approach includes: initially cleaning and deduplicating raw data from industrial literature, production manuals, and briefings to establish a local knowledge base for the metallurgical industry. Subsequently, the contents of the knowledge base are embedded using Word2vec technology to create a local vector library of knowledge. FAISS(Facebook AI Similarity Search) technology is then utilized to search for knowledge vectors similar to specific industrial problems. Finally, selected question vectors and knowledge base vectors are integrated into input prompts for the GLM, which then produces accurate and coherent answers. Experimental results indicate that this method significantly improves the accuracy of question-answering in the metallurgical industry.

关键词： Generative AI Knowledge Enhancement Large Language Models Metallurgical Industry question-answering

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question-answering Pair Matching Based on question Classification and Ensemble Sentence Embedding

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Computer Systems Science & Engineering 2023年第9期46卷 3471-3489页

作者： Jae-Seok Jang Hyuk-Yoon Kwon Department of Computer Science and Engineering Seoul National University of Science and TechnologySeoul01811Korea Department of Industrial Engineering/Graduate School of Data Science Seoul National University of Science and TechnologySeoul01811Korea

question-answering(QA)models find answers to a given *** necessity of automatically finding answers is increasing because it is very important and challenging from the large-scale QA data *** this paper,we deal with the QA pair matching approach in QA models,which finds the most relevant question and its recommended answer for a given *** studies for the approach performed on the entire dataset or datasets within a category that the question writer manually *** contrast,we aim to automatically find the category to which the question belongs by employing the text classification model and to find the answer corresponding to the question within the *** to the text classification model,we can effectively reduce the search space for finding the answers to a given ***,the proposed model improves the accuracy of the QA matching model and significantly reduces the model inference ***,to improve the performance of finding similar sentences in each category,we present an ensemble embedding model for sentences,improving the performance compared to the individual embedding *** real-world QA data sets,we evaluate the performance of the proposed QA matching *** a result,the accuracy of our final ensemble embedding model based on the text classification model is 81.18%,which outperforms the existing models by 9.81%∼14.16%***,in terms of the model inference speed,our model is faster than the existing models by 2.61∼5.07 times due to the effective reduction of search spaces by the text classification model.

关键词： question-answering text classification model data augmentation text embedding

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Semantic Computation in a Chinese question—answering System

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Journal of Computer Science & Technology 2002年第6期17卷 933-939页

作者：李素建张健黄雄白硕刘群 Institute of Computational Linguistics Peking University Beijing 100871 P.R. China Software Department Institute of Computing Technology The Chinese Academy of Sciences Beijing 100080 P.R. China

This paper introduces a kind of semantic computation and presents how tocombine it into our Chinese question-answering (QA) system. Based on two kinds of languageresources, Hownet and Cilin, we present an approach to computing the similarity and relevancybetween words. Using these results, we can calculate the relevancy between two sentences andthen get the optimal answer for the query in the system. The calculation adopts quantitativemethods and can be incorporated into QA systems easily, avoiding some difficulties in conven-tional NLP (Natural Language Processing) problems. The experiments show that the resultsare satisfactory.

关键词： similarity relevancy Hownet question-answering natural language processing

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Chinese question-answering System

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Journal of Computer Science & Technology 2004年第4期19卷 479-488页

作者： Gai-TaiHuang Hsiu-HsenYao InstituteofStrategicDefenseManagement.＂NationalDefeT~se＂ManagementCollege NDUP.O.BOX90046-8Chung-HoTaipei DepartmentofComputerEngineering YuanZeUniversity135Yuan-TungRoadChungliTaoyuan

Traditional Chinese text retrieval systems return a ranked list of documentsin response to a user''s request. While a ranked list of documents may be an appropriate response forthe user, frequently it is not. Usually it would be better for the system to provide the answeritself instead of requiring the user to search for the answer in a set of documents. Since Chinesetext retrieval has just been developed lately, and due to various specific characteristics ofChinese language, the approaches to its retrieval are quite different from those studies andresearches proposed to deal with Western language. Thus, an architecture that augments existingsearch engines is developed to support Chinese natural language question answering. In this paper anew approach to building Chinese question-answering system is described, which is thegeneral-purpose, fully-automated Chinese quest ion-answering system available on the web. In theapproach, we attempt to represent Chinese text by its characteristics, and try to convert theChinese text into ERE (E: entity, R: relation) relation data lists, and then to answer the questionthrough ERE relation model. The system performs quite well giving the simplicity of the techniquesbeing utilized. Experimental results show that question-answering accuracy can be greatly improvedby analyzing more and more matching ERE relation data lists. Simple ERE relation data extractiontechniques work well in our system making it efficient to use with many backend retrieval engines.

关键词： ERE relation model conceptual schema question-answering informationretrieval

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