The question answer system based on natural language understanding

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.

Expert Knowledge-Based Apparel Recommendation Question and Answer System

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.

Query Expansion Based on Semantics and Statistics in Chinese Question Answering System

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.

Development of a Best Answer Recommendation Model in a Community Question Answering (CQA) System

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.

Designing an automated FAQ answering system for farmers based on hybrid strategies

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.

PAL-BERT:An Improved Question Answering Model

In the field of natural language processing(NLP),there have been various pre-training language models in recent years,with question answering systems gaining significant attention.However,as algorithms,data,and computing power advance,the issue of increasingly larger models and a growing number of parameters has surfaced.Consequently,model training has become more costly and less efficient.To enhance the efficiency and accuracy of the training process while reducing themodel volume,this paper proposes a first-order pruningmodel PAL-BERT based on the ALBERT model according to the characteristics of question-answering(QA)system and language model.Firstly,a first-order network pruning method based on the ALBERT model is designed,and the PAL-BERT model is formed.Then,the parameter optimization strategy of the PAL-BERT model is formulated,and the Mish function was used as an activation function instead of ReLU to improve the performance.Finally,after comparison experiments with traditional deep learning models TextCNN and BiLSTM,it is confirmed that PALBERT is a pruning model compression method that can significantly reduce training time and optimize training efficiency.Compared with traditional models,PAL-BERT significantly improves the NLP task’s performance.

DPAL-BERT:A Faster and Lighter Question Answering Model

Recent advancements in natural language processing have given rise to numerous pre-training language models in question-answering systems.However,with the constant evolution of algorithms,data,and computing power,the increasing size and complexity of these models have led to increased training costs and reduced efficiency.This study aims to minimize the inference time of such models while maintaining computational performance.It also proposes a novel Distillation model for PAL-BERT(DPAL-BERT),specifically,employs knowledge distillation,using the PAL-BERT model as the teacher model to train two student models:DPAL-BERT-Bi and DPAL-BERTC.This research enhances the dataset through techniques such as masking,replacement,and n-gram sampling to optimize knowledge transfer.The experimental results showed that the distilled models greatly outperform models trained from scratch.In addition,although the distilled models exhibit a slight decrease in performance compared to PAL-BERT,they significantly reduce inference time to just 0.25%of the original.This demonstrates the effectiveness of the proposed approach in balancing model performance and efficiency.

Operational requirements analysis method based on question answering of WEKG

The weapon and equipment operational requirement analysis(WEORA) is a necessary condition to win a future war,among which the acquisition of knowledge about weapons and equipment is a great challenge. The main challenge is that the existing weapons and equipment data fails to carry out structured knowledge representation, and knowledge navigation based on natural language cannot efficiently support the WEORA. To solve above problem, this research proposes a method based on question answering(QA) of weapons and equipment knowledge graph(WEKG) to construct and navigate the knowledge related to weapons and equipment in the WEORA. This method firstly constructs the WEKG, and builds a neutral network-based QA system over the WEKG by means of semantic parsing for knowledge navigation. Finally, the method is evaluated and a chatbot on the QA system is developed for the WEORA. Our proposed method has good performance in the accuracy and efficiency of searching target knowledge, and can well assist the WEORA.

MKEAH:Multimodal knowledge extraction and accumulation based on hyperplane embedding for knowledge-based visual question answering

Background External knowledge representations play an essential role in knowledge-based visual question and answering to better understand complex scenarios in the open world.Recent entity-relationship embedding approaches are deficient in representing some complex relations,resulting in a lack of topic-related knowledge and redundancy in topic-irrelevant information.Methods To this end,we propose MKEAH:Multimodal Knowledge Extraction and Accumulation on Hyperplanes.To ensure that the lengths of the feature vectors projected onto the hyperplane compare equally and to filter out sufficient topic-irrelevant information,two losses are proposed to learn the triplet representations from the complementary views:range loss and orthogonal loss.To interpret the capability of extracting topic-related knowledge,we present the Topic Similarity(TS)between topic and entity-relations.Results Experimental results demonstrate the effectiveness of hyperplane embedding for knowledge representation in knowledge-based visual question answering.Our model outperformed state-of-the-art methods by 2.12%and 3.24%on two challenging knowledge-request datasets:OK-VQA and KRVQA,respectively.Conclusions The obvious advantages of our model in TS show that using hyperplane embedding to represent multimodal knowledge can improve its ability to extract topic-related knowledge.

基于知识图谱和大语言模型的口述历史资源的问答应用研究

档案馆和图书馆等人文机构逐渐形成了丰富多样的有序化整理后的口述历史档案集合。引入问答系统,通过互动方式可展示档案单元内容的知识推理能力。本研究融合知识图谱和大语言模型,充分发挥知识图谱的准确性、内容透明度等优势,降低大语言模型带来应答幻觉、建设成本高等问题,尝试构造面对口述历史档案资源的问答系统。文章详细阐述了系统设计思路与构建过程,以及核心部件的关键技术要点等,并以李政道图书馆馆藏的CUSPEA主题的口述历史为研究对象,进行问答应用实践。实践验证了问答系统的可行性,能实现口述历史档案资源的知识融汇与知识挖掘,能有效辅助人文学者和历史爱好者理解与洞悉口述历史本质。

基于检索增强生成(RAG)技术的医学教学辅助智能问答系统的构建探索

在医学教育领域,人工智能技术的应用前景广阔,但其在特定知识领域的准确性和可靠性尚须提高,这限制了其在医学教学辅助智能问答系统中的应用普及。为了解决这一问题,本研究尝试探索一种结合检索增强生成(retrieval augmented generation,RAG)技术和临床医学专业教科书知识库的方法,以提高智能问答系统的准确性和可靠性,并减少人工智能幻觉的产生。结果显示,该系统能够为医学生提供丰富、准确且可靠的医学知识资源;在准确性和可靠性方面也显著优于仅依赖大语言模型的智能平台;能为学生提供智能化的学习支持。这表明,通过整合先进的人工智能技术和专业的医学知识库,可以有效提升医学教育的质量和效率。

ANSWER2000在小流域土壤侵蚀过程模拟中的应用研究

ANSWERS2 0 0 0是一个用于流域土壤侵蚀过程模拟的分散型物理模型 ,将此模型运用于三峡库区小流域侵蚀产沙、地表径流以及不同土地利用类型水沙分布状况的模拟中。通过两个不同小流域模拟结果的对比 ,采用误差百分比、线性回归以及 Nash- Sutcliffe效率 3种方法 ,分析和评价了模型的模拟效果。结果表明 ,模型在应用于我国三峡库区小流域土壤侵蚀模拟时 ,其模拟结果与实测结果具有较高的吻合度 ,模拟结果基本可信。但是 ,对于一些陡坡林地等特殊地类 ,模型的模拟误差较大 ,其模拟精度还有待于进一步提高。

煤矿安全知识问答系统的答案生成模型研究

随着国家和煤矿行业对煤矿应急管理要求的逐步提高,对煤矿安全知识的学习也提出了更高的要求,因此建立一种煤矿安全知识智能问答模型。有效学习煤矿安全知识,对于确保煤矿企业工作人员的人身安全和预防煤矿安全事故的发生至关重要。首先,基于RoBERTa-wwm算法自动生成问答对数据,获取并分析煤矿安全知识原始文本数据,定义问题类型并标注问答对;然后,结合RoBERTa-wwm与UniLM,采用点互信息与邻接熵发现新词扩充领域词典,提出问答对自动生成算法,同时构建煤矿安全培训知识问答对数据集,解决煤矿安全知识系统问答对数据集问题;最后,引入问题相似度机制,针对无法回答问题和无关问题提出答案生成策略,构建基于问题相似度机制的答案生成模型,使其只关注可回答问题,从而提升模型的推理能力。实验结果表明,所提出的煤矿安全知识问答系统答案生成模型可有效识别无法回答和无关的问题,能够为煤矿企业工作人员提供知识支持,最大程度地提升煤矿企业工作人员安全培训学习效果。

ANSWERS模型及其应用

ANSWERS模型主要是针对欧洲平原地区研发的分散型物理模型。介绍了模型的研发历史、结构、输入和输出信息以及模型的应用。ANSWERS主要适用于缓坡地形区的径流模拟、侵蚀模拟和农业污染物运移模拟。如何根据中国的实际合理确定模型参数,使模型在我国复杂地形区应用,尚有许多问题需要研究。

土壤侵蚀建模中ANSWERS及地理信息系统ARC/INFO^R的应用研究

研究了土壤侵蚀模型ＡＮＳＷＥＲＳ和地理信息系统（ＧＩＳ）ＡＲＣ／ＩＮＦＯ之间的连结。采用ＡＲＣ／ＩＮＦＯ建立数据库和ＡＮＳＷＥＲＳ进行实际操作，加强了该模型在制定水保措施中的应用。同时，研究出的ＡＲＣＡＮＳ模型，使ＡＲＣ／ＩＮＦＯ和ＡＮＳＷＥＲＳ之间的连结更为容易、有效。最后，对四川紫色丘陵区的一个小流域实施了模拟，以展示连结情况和一些值得注意的问题。

借鉴Google Answers构建高校图书馆咨询专家队伍

分析Google Answers,借鉴它的问答模式、用人政策等,从而构想依托高校专家教授的人力资源来建立高校图书馆的咨询专家队伍。

Question classification in question answering based on real-world web data sets

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.

面向私有问答系统的检索增强式大模型稳定输出方法

基于大模型的问答系统受大模型语义不一致性问题的影响,会出现“输出结果不稳定”的现象,从而制约着问答系统的安全性、鲁棒性和可信度,严重影响了用户体验。针对上述问题,提出一种面向私有问答系统的检索增强式大模型稳定输出方法。该方法通过优化提示词,让大模型首先输出num_k个用户查询的同义查询,然后输出答案;目的是在大模型输出答案时,可以参考已经输出的num_k个同义查询,从而使大模型的输出结果更加稳定。针对开源大模型因指令理解能力弱而出现的“同义查询生成数目不稳定、输出格式无法解析”等问题,提出通过数据蒸馏的方式,利用闭源大模型自动构建了一个开放域上的检索增强式指令数据集,在该指令集上对开源大模型进行微调。构建了一个私有问答场景下的评估集以验证该方法的有效性。在上述评估集上的实验结果表明,该方法在一致性指标和效果指标上,均显著优于基线方法。与基线方法相比,该方法的一致性指标ROUGE-1、ROUGE-2、ROUGE-L和BLEU分别提升了18.9、30.1、24.5和30.6个百分点,效果指标正确率提升了17.4个百分点。

Triple Multimodal Cyclic Fusion and Self-Adaptive Balancing for Video Q&A Systems

Performance of Video Question and Answer(VQA)systems relies on capturing key information of both visual images and natural language in the context to generate relevant questions’answers.However,traditional linear combinations of multimodal features focus only on shallow feature interactions,fall far short of the need of deep feature fusion.Attention mechanisms were used to perform deep fusion,but most of them can only process weight assignment of single-modal information,leading to attention imbalance for different modalities.To address above problems,we propose a novel VQA model based on Triple Multimodal feature Cyclic Fusion(TMCF)and Self-AdaptiveMultimodal Balancing Mechanism(SAMB).Our model is designed to enhance complex feature interactions among multimodal features with cross-modal information balancing.In addition,TMCF and SAMB can be used as an extensible plug-in for exploring new feature combinations in the visual image domain.Extensive experiments were conducted on MSVDQA and MSRVTT-QA datasets.The results confirm the advantages of our approach in handling multimodal tasks.Besides,we also provide analyses for ablation studies to verify the effectiveness of each proposed component.