Automatic Question Answer System(QAS)is a kind of high-powered software system based on Internet.Its key technology is the interrelated technology based on natural language understanding,including the construction of ...Automatic Question Answer System(QAS)is a kind of high-powered software system based on Internet.Its key technology is the interrelated technology based on natural language understanding,including the construction of knowledge base and corpus,the Word Segmentation and POS Tagging of text,the Grammatical Analysis and Semantic Analysis of sentences etc.This thesis dissertated mainly the denotation of knowledge-information based on semantic network in QAS,the stochastic syntax-parse model named LSF of knowledge-information in QAS,the structure and constitution of QAS.And the LSF model's parameters were exercised,which proved that they were feasible.At the same time,through "the limited-domain QAS" which was exploited for banks by us,these technologies were proved effective and propagable.展开更多
Aiming at the lack of professional knowledge to guide apparel recommendation,an apparel recommendation method based on image design expert knowledge has been proposed.Then,apparel recommendation knowledge graphs have ...Aiming at the lack of professional knowledge to guide apparel recommendation,an apparel recommendation method based on image design expert knowledge has been proposed.Then,apparel recommendation knowledge graphs have been created and a apparel recommendation question and answer(Q&A)system has been designed and implemented.The question templates in the apparel recommendation domain were defined,the task of recognizing the named entities of question sentences was completed by the Bi-directional encoder representations from transformer-Bi-directional long short-term memory-conditional random field(BERT-BiLSTM-CRF)model,and the question template with the highest matching degree to the user’s question was obtained by using term frequency-inverse document frequency(TF-IDF)algorithm.The corresponding cypher graph database query statement was generated to retrieve the knowledge graph for answers,and iFLYTEK’s voice application programming interface(API)was called to implement the Q&A.The experimental results have shown that the Q&A system has a high accuracy rate and application value in the field of apparel recommendations.展开更多
In Chinese question answering system, because there is more semantic relation in questions than that in query words, the precision can be improved by expanding query while using natural language questions to retrieve ...In Chinese question answering system, because there is more semantic relation in questions than that in query words, the precision can be improved by expanding query while using natural language questions to retrieve documents. This paper proposes a new approach to query expansion based on semantics and statistics Firstly automatic relevance feedback method is used to generate a candidate expansion word set. Then the expanded query words are selected from the set based on the semantic similarity and seman- tic relevancy between the candidate words and the original words. Experiments show the new approach is effective for Web retrieval and out-performs the conventional expansion approaches.展开更多
Question answering systems offer a friendly interface for human beings to interact with massive online information. It is time consuming for users to retrieve useful medical information with search engines among massi...Question answering systems offer a friendly interface for human beings to interact with massive online information. It is time consuming for users to retrieve useful medical information with search engines among massive online websites. An effort is made to build a Chinese Question Answering System in Medical Domain(CQASMD) to provide useful medical information for users. A large medical knowledge base with more than 300 thousand medical terms and their descriptions is firstly constructed to store the structured medical knowledge data, and classified with the FastText model. Furthermore, a Word2Vec model is adopted to capture the semantic meanings of words, and the questions and answers are processed with sentence embedding to capture semantic context information. Users' questions are firstly classified and processed into a sentence vector and a matching algorithm is adopted to match the most similar question. After querying the constructed medical knowledge base, the corresponding answers to previous questions are responded to users. The architecture and flowchart of CQASMD is proposed, which will play an important role in self disease diagnosis and treatment.展开更多
In this work, a best answer recommendation model is proposed for a Question Answering (QA) system. A Community Question Answering System was subsequently developed based on the model. The system applies Brouwer Fixed ...In this work, a best answer recommendation model is proposed for a Question Answering (QA) system. A Community Question Answering System was subsequently developed based on the model. The system applies Brouwer Fixed Point Theorem to prove the existence of the desired voter scoring function and Normalized Google Distance (NGD) to show closeness between words before an answer is suggested to users. Answers are ranked according to their Fixed-Point Score (FPS) for each question. Thereafter, the highest scored answer is chosen as the FPS Best Answer (BA). For each question asked by user, the system applies NGD to check if similar or related questions with the best answer had been asked and stored in the database. When similar or related questions with the best answer are not found in the database, Brouwer Fixed point is used to calculate the best answer from the pool of answers on a question then the best answer is stored in the NGD data-table for recommendation purpose. The system was implemented using PHP scripting language, MySQL for database management, JQuery, and Apache. The system was evaluated using standard metrics: Reciprocal Rank, Mean Reciprocal Rank (MRR) and Discounted Cumulative Gain (DCG). The system eliminated longer waiting time faced by askers in a community question answering system. The developed system can be used for research and learning purposes.展开更多
Purpose: The purpose of this study is to develop an automated frequently asked question(FAQ) answering system for farmers. This paper presents an approach for calculating the similarity between Chinese sentences based...Purpose: The purpose of this study is to develop an automated frequently asked question(FAQ) answering system for farmers. This paper presents an approach for calculating the similarity between Chinese sentences based on hybrid strategies.Design/methodology/approach: We analyzed the factors influencing the successful matching between a user's question and a question-answer(QA) pair in the FAQ database. Our approach is based on a combination of multiple factors. Experiments were conducted to test the performance of our method.Findings: Experiments show that this proposed method has higher accuracy. Compared with similarity calculation based on TF-IDF,the sentence surface forms and the semantic relations,the proposed method based on hybrid strategies has a superior performance in precision,recall and F-measure value.Research limitations: The FAQ answering system is only capable of meeting users' demand for text retrieval at present. In the future,the system needs to be improved to meet users' demand for retrieving images and videos.Practical implications: This FAQ answering system will help farmers utilize agricultural information resources more efficiently.Originality/value: We design the algorithms for calculating similarity of Chinese sentences based on hybrid strategies,which integrate the question surface similarity,the question semantic similarity and the question-answer similarity based on latent semantic analysis(LSA) to find answers to a user's question.展开更多
Medical visual question answering(MedVQA)faces unique challenges due to the high precision required for images and the specialized nature of the questions.These challenges include insufficient feature extraction capab...Medical visual question answering(MedVQA)faces unique challenges due to the high precision required for images and the specialized nature of the questions.These challenges include insufficient feature extraction capabilities,a lack of textual priors,and incomplete information fusion and interaction.This paper proposes an enhanced bootstrapping language-image pre-training(BLIP)model for MedVQA based on multimodal feature augmentation and triple-path collaborative attention(FCA-BLIP)to address these issues.First,FCA-BLIP employs a unified bootstrap multimodal model architecture that integrates ResNet and bidirectional encoder representations from Transformer(BERT)models to enhance feature extraction capabilities.It enables a more precise analysis of the details in images and questions.Next,the pre-trained BLIP model is used to extract features from image-text sample pairs.The model can understand the semantic relationships and shared information between images and text.Finally,a novel attention structure is developed to fuse the multimodal feature vectors,thereby improving the alignment accuracy between modalities.Experimental results demonstrate that the proposed method performs well in clinical visual question-answering tasks.For the MedVQA task of staging diabetic macular edema in fundus imaging,the proposed method outperforms the existing major models in several performance metrics.展开更多
The medical education of the Song dynasty constitutes a pivotal aspect within the broader framework of ancient Chinese medical education. The advent of the imperial examination system coincided with the emergence of a...The medical education of the Song dynasty constitutes a pivotal aspect within the broader framework of ancient Chinese medical education. The advent of the imperial examination system coincided with the emergence of a medical examination system, which served as the cornerstone for the subsequent evolution of medical education. According to historical records, the Song government established dedicated medical departments, along with comprehensive systems encompassing medical professors, students, and examinations. By examining extant medical historical documents, such as Tai Yi Ju Zhu Ke Cheng Wen Ge(《太医局诸科程文格》 Examination Answers and Standards of the Imperial Medical Bureau), researchers and readers can obtain a comprehensive understanding of the medical system that prevailed in the Song dynasty. While the intricate details of medical education during this era are not explicitly documented in historical records, modern researchers have the opportunity to uncover the entire view of medical education, particularly the medical examination system, through rigorous analysis of these extant historical medical documents. Such studies offer valuable insights into the developmental trajectory of the ancient Chinese medical examination system and provide crucial references for contemporary medical education. By conducting in-depth literature research and analysis of Tai Yi Ju Zhu Ke Cheng Wen Ge, this study endeavors to reconstruct the authentic scenario of medical examinations in the Song dynasty, as presented in the document, for the benefit of modern readers and researchers.展开更多
To improve question answering （QA） performance based on real-world web data sets,a new set of question classes and a general answer re-ranking model are defined.With pre-defined dictionary and grammatical analysis,t...To improve question answering （QA） performance based on real-world web data sets,a new set of question classes and a general answer re-ranking model are defined.With pre-defined dictionary and grammatical analysis,the question classifier draws both semantic and grammatical information into information retrieval and machine learning methods in the form of various training features,including the question word,the main verb of the question,the dependency structure,the position of the main auxiliary verb,the main noun of the question,the top hypernym of the main noun,etc.Then the QA query results are re-ranked by question class information.Experiments show that the questions in real-world web data sets can be accurately classified by the classifier,and the QA results after re-ranking can be obviously improved.It is proved that with both semantic and grammatical information,applications such as QA, built upon real-world web data sets, can be improved,thus showing better performance.展开更多
大模型时代,自动问答系统呈现出诸多新的特征。通过文献阅读和梳理,对自动问答系统特征和评测体系进行总结与归纳,从问答模型推理训练的训练数据、预训练框架、模型后处理、模型高效微调等阶段,对比大模型发展初期“追求数据和参数规模...大模型时代,自动问答系统呈现出诸多新的特征。通过文献阅读和梳理,对自动问答系统特征和评测体系进行总结与归纳,从问答模型推理训练的训练数据、预训练框架、模型后处理、模型高效微调等阶段,对比大模型发展初期“追求数据和参数规模”的训练方法和如今“注重数据和模型效率”之间的差异,系统分析基于大模型的自动问答系统新的特征。总结当前各种类型的自动问答大模型评测体系,并详细梳理自动化评价体系HELM(holistic evaluation of language model)在自动问答任务上的数据集、评价指标和量化计算方法。未来基于大模型的自动问答系统研究将会围绕多模态融合、高安全性、高可解释性、低资源消耗,以及结合大模型和自动化的综合评价体系这几个方面进一步拓展与深化。展开更多
基金Sponsored by the National Natural Science Foundation of China(Grant No.60305009)the Ph.D Degree Teacher Foundation of North China Electric Power University(Grant No.H0585).
文摘Automatic Question Answer System(QAS)is a kind of high-powered software system based on Internet.Its key technology is the interrelated technology based on natural language understanding,including the construction of knowledge base and corpus,the Word Segmentation and POS Tagging of text,the Grammatical Analysis and Semantic Analysis of sentences etc.This thesis dissertated mainly the denotation of knowledge-information based on semantic network in QAS,the stochastic syntax-parse model named LSF of knowledge-information in QAS,the structure and constitution of QAS.And the LSF model's parameters were exercised,which proved that they were feasible.At the same time,through "the limited-domain QAS" which was exploited for banks by us,these technologies were proved effective and propagable.
文摘Aiming at the lack of professional knowledge to guide apparel recommendation,an apparel recommendation method based on image design expert knowledge has been proposed.Then,apparel recommendation knowledge graphs have been created and a apparel recommendation question and answer(Q&A)system has been designed and implemented.The question templates in the apparel recommendation domain were defined,the task of recognizing the named entities of question sentences was completed by the Bi-directional encoder representations from transformer-Bi-directional long short-term memory-conditional random field(BERT-BiLSTM-CRF)model,and the question template with the highest matching degree to the user’s question was obtained by using term frequency-inverse document frequency(TF-IDF)algorithm.The corresponding cypher graph database query statement was generated to retrieve the knowledge graph for answers,and iFLYTEK’s voice application programming interface(API)was called to implement the Q&A.The experimental results have shown that the Q&A system has a high accuracy rate and application value in the field of apparel recommendations.
基金the Specialized Research Program Fundthe Doctoral Program of Higher Education of China (20050007023)the Natural Science Foundation of Shandong Province(Y2004G04)
文摘In Chinese question answering system, because there is more semantic relation in questions than that in query words, the precision can be improved by expanding query while using natural language questions to retrieve documents. This paper proposes a new approach to query expansion based on semantics and statistics Firstly automatic relevance feedback method is used to generate a candidate expansion word set. Then the expanded query words are selected from the set based on the semantic similarity and seman- tic relevancy between the candidate words and the original words. Experiments show the new approach is effective for Web retrieval and out-performs the conventional expansion approaches.
基金the National Natural Science Foundation of China(No.61303094)the Program of Science and Technology Commission of Shanghai Municipality(Nos.16511102400 and 16111107801)the Innovation Program of Shanghai Municipal Education Commission(No.14YZ024)
文摘Question answering systems offer a friendly interface for human beings to interact with massive online information. It is time consuming for users to retrieve useful medical information with search engines among massive online websites. An effort is made to build a Chinese Question Answering System in Medical Domain(CQASMD) to provide useful medical information for users. A large medical knowledge base with more than 300 thousand medical terms and their descriptions is firstly constructed to store the structured medical knowledge data, and classified with the FastText model. Furthermore, a Word2Vec model is adopted to capture the semantic meanings of words, and the questions and answers are processed with sentence embedding to capture semantic context information. Users' questions are firstly classified and processed into a sentence vector and a matching algorithm is adopted to match the most similar question. After querying the constructed medical knowledge base, the corresponding answers to previous questions are responded to users. The architecture and flowchart of CQASMD is proposed, which will play an important role in self disease diagnosis and treatment.
文摘In this work, a best answer recommendation model is proposed for a Question Answering (QA) system. A Community Question Answering System was subsequently developed based on the model. The system applies Brouwer Fixed Point Theorem to prove the existence of the desired voter scoring function and Normalized Google Distance (NGD) to show closeness between words before an answer is suggested to users. Answers are ranked according to their Fixed-Point Score (FPS) for each question. Thereafter, the highest scored answer is chosen as the FPS Best Answer (BA). For each question asked by user, the system applies NGD to check if similar or related questions with the best answer had been asked and stored in the database. When similar or related questions with the best answer are not found in the database, Brouwer Fixed point is used to calculate the best answer from the pool of answers on a question then the best answer is stored in the NGD data-table for recommendation purpose. The system was implemented using PHP scripting language, MySQL for database management, JQuery, and Apache. The system was evaluated using standard metrics: Reciprocal Rank, Mean Reciprocal Rank (MRR) and Discounted Cumulative Gain (DCG). The system eliminated longer waiting time faced by askers in a community question answering system. The developed system can be used for research and learning purposes.
基金jointly supported by the National Social Science Foundation of China(Grant Nos.:08ATQ003 and 10&ZD134)
文摘Purpose: The purpose of this study is to develop an automated frequently asked question(FAQ) answering system for farmers. This paper presents an approach for calculating the similarity between Chinese sentences based on hybrid strategies.Design/methodology/approach: We analyzed the factors influencing the successful matching between a user's question and a question-answer(QA) pair in the FAQ database. Our approach is based on a combination of multiple factors. Experiments were conducted to test the performance of our method.Findings: Experiments show that this proposed method has higher accuracy. Compared with similarity calculation based on TF-IDF,the sentence surface forms and the semantic relations,the proposed method based on hybrid strategies has a superior performance in precision,recall and F-measure value.Research limitations: The FAQ answering system is only capable of meeting users' demand for text retrieval at present. In the future,the system needs to be improved to meet users' demand for retrieving images and videos.Practical implications: This FAQ answering system will help farmers utilize agricultural information resources more efficiently.Originality/value: We design the algorithms for calculating similarity of Chinese sentences based on hybrid strategies,which integrate the question surface similarity,the question semantic similarity and the question-answer similarity based on latent semantic analysis(LSA) to find answers to a user's question.
基金Supported by the Program for Liaoning Excellent Talents in University(No.LR15045)the Liaoning Provincial Science and Technology Department Applied Basic Research Plan(No.101300243).
文摘Medical visual question answering(MedVQA)faces unique challenges due to the high precision required for images and the specialized nature of the questions.These challenges include insufficient feature extraction capabilities,a lack of textual priors,and incomplete information fusion and interaction.This paper proposes an enhanced bootstrapping language-image pre-training(BLIP)model for MedVQA based on multimodal feature augmentation and triple-path collaborative attention(FCA-BLIP)to address these issues.First,FCA-BLIP employs a unified bootstrap multimodal model architecture that integrates ResNet and bidirectional encoder representations from Transformer(BERT)models to enhance feature extraction capabilities.It enables a more precise analysis of the details in images and questions.Next,the pre-trained BLIP model is used to extract features from image-text sample pairs.The model can understand the semantic relationships and shared information between images and text.Finally,a novel attention structure is developed to fuse the multimodal feature vectors,thereby improving the alignment accuracy between modalities.Experimental results demonstrate that the proposed method performs well in clinical visual question-answering tasks.For the MedVQA task of staging diabetic macular edema in fundus imaging,the proposed method outperforms the existing major models in several performance metrics.
文摘The medical education of the Song dynasty constitutes a pivotal aspect within the broader framework of ancient Chinese medical education. The advent of the imperial examination system coincided with the emergence of a medical examination system, which served as the cornerstone for the subsequent evolution of medical education. According to historical records, the Song government established dedicated medical departments, along with comprehensive systems encompassing medical professors, students, and examinations. By examining extant medical historical documents, such as Tai Yi Ju Zhu Ke Cheng Wen Ge(《太医局诸科程文格》 Examination Answers and Standards of the Imperial Medical Bureau), researchers and readers can obtain a comprehensive understanding of the medical system that prevailed in the Song dynasty. While the intricate details of medical education during this era are not explicitly documented in historical records, modern researchers have the opportunity to uncover the entire view of medical education, particularly the medical examination system, through rigorous analysis of these extant historical medical documents. Such studies offer valuable insights into the developmental trajectory of the ancient Chinese medical examination system and provide crucial references for contemporary medical education. By conducting in-depth literature research and analysis of Tai Yi Ju Zhu Ke Cheng Wen Ge, this study endeavors to reconstruct the authentic scenario of medical examinations in the Song dynasty, as presented in the document, for the benefit of modern readers and researchers.
基金Microsoft Research Asia Internet Services in Academic Research Fund(No.FY07-RES-OPP-116)the Science and Technology Development Program of Tianjin(No.06YFGZGX05900)
文摘To improve question answering （QA） performance based on real-world web data sets,a new set of question classes and a general answer re-ranking model are defined.With pre-defined dictionary and grammatical analysis,the question classifier draws both semantic and grammatical information into information retrieval and machine learning methods in the form of various training features,including the question word,the main verb of the question,the dependency structure,the position of the main auxiliary verb,the main noun of the question,the top hypernym of the main noun,etc.Then the QA query results are re-ranked by question class information.Experiments show that the questions in real-world web data sets can be accurately classified by the classifier,and the QA results after re-ranking can be obviously improved.It is proved that with both semantic and grammatical information,applications such as QA, built upon real-world web data sets, can be improved,thus showing better performance.
文摘大模型时代,自动问答系统呈现出诸多新的特征。通过文献阅读和梳理,对自动问答系统特征和评测体系进行总结与归纳,从问答模型推理训练的训练数据、预训练框架、模型后处理、模型高效微调等阶段,对比大模型发展初期“追求数据和参数规模”的训练方法和如今“注重数据和模型效率”之间的差异,系统分析基于大模型的自动问答系统新的特征。总结当前各种类型的自动问答大模型评测体系,并详细梳理自动化评价体系HELM(holistic evaluation of language model)在自动问答任务上的数据集、评价指标和量化计算方法。未来基于大模型的自动问答系统研究将会围绕多模态融合、高安全性、高可解释性、低资源消耗,以及结合大模型和自动化的综合评价体系这几个方面进一步拓展与深化。
