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.

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.

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.

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.

Question Bank Construction and Test Paper Auto-generation System Implementation Based on Delphi

Nowadays, the computer is increasingly popular, and college examination is developing in the direction of traditional examination means to automation and intelligence ones gradually, all these make it inevitable to construct question bank for courses, and to generate test paper using computers. This paper uses the Delphi technique, to make improvements to existing components, combining with VBA programming, and use of SQL Server to implement the question bank management and test paper auto-generation system, which could generate test paper in Word Document. A large number of tests show that the software is running stably and system features are functioning correctly on Windows 2000/XP/2003 platform with Office XP/2003 environment.

Special Issue:Questions&Data for Better Science and Innovation Call for submissions

Editors Yang Wang,Xi'an Jiaotong University Dongbo Shi,Shanghai Jiaotong University Ye Sun,University College London Zhesi Shen,National Science Library,CAS Topic of the Special Issue What are the top questions towards better science and innovation and the required data to answer these questions?

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Question Point的合作虚拟咨询服务

介绍了Question Point合作虚拟咨询服务产生的背景、功能、系统工作流程以及我国开展合作虚拟参考咨询的情况。

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.

Question Point——基于网络参考咨询系统的探讨

简要概述了Question Point的由来,讨论了其功能和在图书馆中的运作,同时提出了合作化数字参考咨询实施过程中的一些问题及未来的发展趋势。

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。

Design of Questions in the Teaching of English Reading

Questions which were conventionally designed to check reading comprehension can also be used to enhance understanding. Different types of questions can be designed to achieve different purposes in reading. While designing questions, teachers should take into consideration such points as the language used, manner of presentation and types of questions etc. Moreover, once questions have been designed, it’s essential for teachers to think about the techniques for the use of these questions. If appro- priately used, questions contribute greatly to students’ understanding of what they’ re reading by helping to explore the meaning that language conveys, in addition to developing proper reading skills. Therefore, teachers should be able to teach reading with well-designed questions so that the ultimate goal of understanding the text is likely to be achieved.

英语Tag Question用法探究

TagQuestion(附加疑问句 ,也叫反意疑问句 )是英语中常用的疑问句式 ,主要用来提出情况和看法 ,并征求对方意见。文章就如何选配英语TagQuestion及其特殊用法做一简单探究。

A Study on Teacher's Questioning Strategies in the UK

Classroom questioning is one of the main means for classroom interaction which plays a very important role in classroom teaching. Therefore, based on the observation of four different level English classes in the UK and interview of English teachers, this thesis investigates the types, functions and answer-seeking strategies used by EFL teachers.

Questioning Styles in Vocational College English Classrooms

This study investigates the effects of TBLT reform in Higher Vocational Colleges from the perspective of questioning styles.It employs three methods to collect data:classroom observation,semi-structured interviews and focus group discussion with eight English teachers and their 384 non-English major students from three Higher Vocational Colleges in Guangdong.The results indicated that the teachers assigned students different tasks to perform in class.They seemed to be adopting the TBLT approach,but their English classes were not totally different from the teacher-centered grammar-focused lessons,the student-centered or communicative lessons.

Gender Differences In Using Questions——An Investigation of the Questions Used by Mr. Rochester and Jane in Jane Eyre

In literature,differences in the description of female and male characters have been noticeable for a long time.In this study,the novel Jane Eyre is uses as a material to investigate whether there are in fact any significant differences in the questions Mr.Rochester and Jane use and how the questions function to portray these two main characters.

Teachers’Questioning Strategies in Reading Class—the Perspective of Adaptation

Teachers’questioning is one of the crucial means for classroom interaction.This study attempts to explore teachers’questioning strategies in reading class from the perspective of Adaptation Theory.It is found that the effective questioning can be realized in teachers’adaptation to their roles,the features of students and the features of reading courses.