Automatic Question Answering System Research Articles

Smart reception: An artificial intelligence driven bangla language based receptionist system employing speech, speaker, and face recognition for automating reception services

In recent times, service robots (SR) have become widely accepted in a variety of fields as an alternative to traditional reception methods. Artificial Intelligence (AI) driven systems are seen as efficient labor alternatives, resulting in several SR models already being developed, primarily designed for English-speaking environments. Despite a large Bangla-speaking population, the exploration and implementation of Bangla language support within AI reception systems remain unexplored due to limited resources, posing significant challenges for deployment. This study presents a novel AI-enabled receptionist framework tailored to Bangla-speaking environments, addressing the limitation of Bangla language resources for developing automated reception systems. Leveraging advanced AI technologies including Face Recognition, Speaker Recognition, Automatic Speech Recognition (ASR), Text-to-Speech Synthesis, and Question Answering System, our integrated system demonstrates promise to automate Bangla reception systems. Our suite of models yields positive performance, with a face recognition accuracy of 99.38%, a speaker recognition system with 5.83 Equal Error Rate (ERR), ASR model achieving a Word Error Rate (WER) of 9.005%, TTS model scoring a Mean Opinion Score (MOS) of 4.10, and a question-answering system with a validation loss of 0.03%. Real-world evaluation among 1664 users in a university setting achieved over 75% user satisfaction, with 88% expressing interest in real-life implementation, showcasing usability across different domains. Limitations such as limited training data, scalability, and environmental sensitivity persist, underscoring the need for further development. Nevertheless, our framework demonstrates potential for real-life implementation, fostering human-robot interaction in Bangla-speaking contexts and paving the way for future innovations in AI-driven Bangla receptionist systems.

Creating and validating the Fine-Grained Question Subjectivity Dataset (FQSD): A new benchmark for enhanced automatic subjective question answering systems.

In the domain of question subjectivity classification, there exists a need for detailed datasets that can foster advancements in Automatic Subjective Question Answering (ASQA) systems. Addressing the prevailing research gaps, this paper introduces the Fine-Grained Question Subjectivity Dataset (FQSD), which comprises 10,000 questions. The dataset distinguishes between subjective and objective questions and offers additional categorizations such as Subjective-types (Target, Attitude, Reason, Yes/No, None) and Comparison-form (Single, Comparative). Annotation reliability was confirmed via robust evaluation techniques, yielding a Fleiss's Kappa score of 0.76 and Pearson correlation values up to 0.80 among three annotators. We benchmarked FQSD against existing datasets such as (Yu, Zha, and Chua 2012), SubjQA (Bjerva 2020), and ConvEx-DS (Hernandez-Bocanegra 2021). Our dataset excelled in scale, linguistic diversity, and syntactic complexity, establishing a new standard for future research. We employed visual methodologies to provide a nuanced understanding of the dataset and its classes. Utilizing transformer-based models like BERT, XLNET, and RoBERTa for validation, RoBERTa achieved an outstanding F1-score of 97%, confirming the dataset's efficacy for the advanced subjectivity classification task. Furthermore, we utilized Local Interpretable Model-agnostic Explanations (LIME) to elucidate model decision-making, ensuring transparent and reliable model predictions in subjectivity classification tasks.

Research on the Construction Method of Knowledge Base of Automatic Question Answering System for Labor Law

At present, with the rapid development of China's economy, labor relations in society are becoming more and more tense. Therefore, it is urgent and necessary to develop an automatic question and answer system for labor law to solve problems in legislation, law enforcement, justice, rights protection, etc., and the construction of labor law knowledge base is the core system of the automatic question and answer system of labor law. With the development and training of large AI models, the automatic question answering system is becoming more and more mature, and this paper will use FastGPT to build a local knowledge base and build a knowledge base of the automatic question answering system for labor law.

A Review on Conversational Question Answering (CQA)

The internet is constantly changing how people communicate with one another, manage their daily lives, and share the information. As a result of digitization, automated Question Answering Systems are becoming more important in extracting useful information from the knowledge sources. In general, Question Answering (QA)mechanisms are intended to answer a specific question only once (so-called single-turn).However, to satisfy a user's information needs, the Conversational Question Answering (CQA) system must grasp the given context and engage in multi-turn QA. While single-turn QA leads the majority of current research, multi-turn QA has rapidly gained prominence due to the availability of large-scale multi-turn QA datasets and the development of “pre-trained language models”. In this research paper, we have presented a comprehensive review on the Conversational Question Answer (CQA) field which includes need of CQA followed by categorization of QA system, list of available datasets and various evaluation metrics.

Answering Binary Causal Questions Using Role-Oriented Concept Embedding

Answering binary causal questions is a challenging task, and it requires rich background knowledge to answer such questions. Extracting useful causal features from the background knowledge base and applying them effectively in a model is a crucial step to answering binary causal questions. The state-of-the-art approaches apply deep learning techniques to answer binary causal questions. In these approaches, candidate concepts are often embedded into vectors to model causal relationships among them. However, a concept may play the role of a cause in one question, but it could be an effect in another question. This aspect has not been extensively explored in existing approaches. Role-oriented causal concept embeddings are proposed in this paper to model causality between concepts. We also propose leveraging semantic concept similarity to extract causal information from concepts. Finally, we develop a deep learning framework to answer binary causal questions. Our approach yields accuracy that is comparable to or better than the benchmark approaches. <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Impact Statement</u> —Understanding causality is crucial for automatic question-answering systems, which are useful in extracting and distributing human knowledge. An automatic question-answering system with causal knowledge can be used to check whether there is causal relationship between two concepts. Existing approaches to answer binary causal questions often answer such questions with close to 55% accuracy due to the limited usage of causal and contextual features. The deep learning framework we propose in this paper uses a role-oriented concept embedding to address such issues. Our approach achieves better accuracy by up to 3.6%, compared to the state-of-the-art benchmark approaches. The proposed approach can be used in a variety of fields, including prescriptive analysis, event prediction, and any other area where entity relationships are essential. It could also be used to improve the retrieval of causality-related inquiries in web search engines.

Research on Methods and Applications Related to Question-and-Answer Dialogue Systems

In the face of more and more network data information, search engines have gradually become the main retrieval method to obtain relevant information knowledge. However, in today's increasingly explosive development of information on the Internet, by contrast, traditional search engines have problems such as semantic understanding and complicated answers. Therefore, question answering systems are more important. The automatic question answering system generally adopts natural language processing related technologies. When users ask questions, the system automatically judges and gives answers. It involves computer linguistics, machine learning, artificial intelligence and other popular technology research. According to different classification criteria, the automatic question answering system is roughly divided into open field automatic question answering system and stereotyped automatic question answering system.. This thesis investigates methods and applications related to question-and-answer dialogue systems. On the methodological side, we introduce commonly used datasets and the principles and techniques of text, speech and visual question and answer systems, and analyse in detail the excellent example ChatGPT. In terms of applications, we present the application of Q&amp;A dialogue systems in search engines, smart campuses. There is some reference value.

Discovering and Ranking Relevant Comment for Chinese Automatic Question-Answering System

Intelligent customer service system is timely, efficient, and accurate, which is more and more popular in grid electric power companies, and the amount of customer consultation is increasing day by day. It is infeasible for human customer service to answer these questions on time, so an automatic question-answering system is of great help to the grid electric power company. The customer queries from the grid electric power company customer service is very different from open-domain questions: the problems questioned by customer tend to be for a specific device or system within the enterprise operation problem. Most grid electric companies provide customers with a communication platform where customers can get guidance on using equipment and the business process. The comments from communication platforms are valuable resources for answering customer questions. In our work, we use three neural network models which excavate potential answers to customer queries from comments. One of the key challenges, however, is the difficulty of matching customer questions with comments. To solve this problem, we propose a method based on deep learning to find the comments related to customer questions to generate more accurate and reliable answers. Experiments can prove that our method performed well in the customer service of grid electric power company.

Students' adaptive deep learning path and teaching strategy of contemporary ceramic art under the background of Internet.

With the rapid expansion of Internet technology, this research aims to explore the teaching strategies of ceramic art for contemporary students. Based on deep learning (DL), an automatic question answering (QA) system is established, new teaching strategies are analyzed, and the Internet is combined with the automatic QA system to help students solve problems encountered in the process of learning. Firstly, the related theories of DL and personalized learning are analyzed. Among DL-related theories, Back Propagation Neural Network (BPNN), Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU) are compared to implement a single model and a mixed model. Secondly, the collected student questions are selected and processed, and experimental parameters in different models are set for comparative experiments. Experiments reveal that the average accuracy and Mean Reciprocal Rank (MRR) of traditional retrieval methods can only reach about 0.5. In the basic neural network, the average accuracy of LSTM and GRU structural models is about 0.81, which can achieve better results. Finally, the accuracy of the hybrid model can reach about 0.82, and the accuracy and MRR of the Bidirectional Gated Recurrent Unit Network-Attention (BiGRU-Attention) model are 0.87 and 0.89, respectively, achieving the best results. The established DL model meets the requirements of the online automatic QA system, improves the teaching system, and helps students better understand and solve problems in the ceramic art courses.

Application of Knowledge Map Based on BiLSTM-CRF Algorithm Model in Ideological and Political Education Question Answering System

Ideological and political education plays an important role in university education and is an important way to realize the function of educating people. It is of great significance to establish a perfect automatic question answering system for ideological and political education. Traditional automatic question answering methods usually rely on predicates and other prior information to achieve knowledge base question answering, which requires a lot of manpower and poor generalization ability. In order to solve this problem, this paper designs a question answering system for ideological and political education based on BiLSTM-CRF algorithm model (BiLSTM: Bidirectional Long Short-Term Memory and CRF: Conditional Random Fields). For the knowledge base question answering method with weak-dependent information, this paper combines BERT (Bidirectional Encoder Representation from Transformers) and BiLSTM-CRF network to extract the named entity in the questions and locate the triplet information related to the entity in the knowledge base. Through the answer matching network, the similarity score is marked for the answers in the triplet set, and the threshold selection strategy is used to select the answers that meet the requirements. And according to the similarity score from high to paper, it is presented to the user. The experimental results show that the method weakens the dependence on prior information, reduces manual intervention and ensures the quality of question answering, and completes the validity verification of the question answering system of ideological and political education.

Lioness Adapted GWO-Based Deep Belief Network Enabled with Multiple Features for a Novel Question Answering System

Recently, the researches on Question Answering (QA) systems attract progressive attention with the enlargement of data and the advances on machine learning. Selection of answers from QA system is a significant task for enhancing the automatic QA systems. However, the major complexity relies in the designing of contextual factors and semantic matching. Motivation: Question Answering is a specialized form of Information Retrieval which seeks knowledge. We are not only interested in getting the relevant pages but we are interested in getting specific answer to queries. Question Answering is in itself intersection of Natural Language Processing, Information Retrieval, Machine Learning, Knowledge Representation, Logic and Inference and Semantic Search. Contribution: Feature extraction plays a major role for accurate classification, where the learned features get extracted for enhancing the capability of sequence learning. Optimized Deep Belief network model is adopted for the precise question answering system, which could handle both objective and subjective questions. A new hybrid optimization algorithm known as Lioness Adapted GWO (LA-GWO) algorithm is introduced, which mainly concentrates on high reliability and convergence rate. This paper intends to formulate a novel QA system, and the process starts with word embedding. From the embedded results, some of the features get extracted, and subsequently, the classification is carried out using the hybrid optimization enabled Deep Belief Network (DBN). Specifically, the hidden neurons in DBN will be optimally tuned using a new Lioness Adapted GWO (LA-GWO) algorithm, which is the hybridization of both Lion Algorithm (LA) and Grey Wolf optimization (GWO) models. Finally, the performance of proposed work is compared over other conventional methods with respect to accuracy, sensitivity, specificity, and precision, respectively.

Word Level Similarity Auto-Evaluation for an Online Question Answering System

Over the last decade, the educational system has undergone numerous changes, the most significant of which is the shift in learning and examination methods. Educational institutions are gradually going into online instruction and education, which is altering students' and parents' perceptions of learning. The scenario of Covid-19 has seriously raised demand for online subjective examination and evaluation process to be automated. Paraphrasing is the challenge while designing and evaluation an fully automated Question Answering System. This paper has shown the word level similarity for evaluation one word answer of fill in the blank types of question taken in real time scenario.

The Research of Convolutional Neural Network Based on Integrated Classification in Question Classification

As a new generation of search engine, automatic question answering system (QAS) is becoming more and more important and has become one of the hotspots of computer application research and natural language processing (NLP). However, as an indispensable part of the QAS, the role of question classification is an understood thing in the system. In view of this, to further make the performance of question classification much better, both the feature extraction and the classification model were explored. On the study of existing CNN research, an improved CNN model based on Bagging integrated classification (“W2V + B-CNN” for short) is proposed and applied to question classification. Firstly, we combine the characteristics of short texts, use the Word2Vec tool to map the features of the words to a certain dimension, and organize the question sentences into the form of a two-dimensional matrix similar to the image. Then, the trained word vectors are used as the input of the CNN for feature extraction. Finally, the Bagging integrated classification algorithm is used to replace the Softmax classification of the traditional CNN for classification. In other words, the good of W2V + B-CNN model is that it can make use of the advantages of CNN and Bagging integrated classification at the same time. Overall, the new model can not only use the powerful feature extraction capabilities of CNN to extract the potential features of natural language questions but also use the good data classification capabilities of the integrated classification algorithm for feature classification at the same time, which can help improve the accuracy of the W2V + B-CNN in the application of question classification. The comparative experiment results prove that the effect of the W2V + B-CNN is significantly better than that of the CNN and other classification algorithms in question classification.

A novel approach for automatic Bengali question answering system using semantic similarity analysis

Finding the semantically accurate answer is one of the key challenges in advanced searching. In contrast to keyword-based searching, the meaning of a question or query is important here and answers are ranked according to relevance. It is very natural that there is almost no common word between the question sentence and the answer sentence. In this paper, an approach is described to find out the semantically relevant answers in the Bengali dataset. In the first part of the algorithm, a set of statistical parameters like frequency, index, part-of-speech (POS) is matched between a question and the probable answers. In the second phase, entropy and similarity are calculated in different modules. Finally, a sense score is generated to rank the answers. The algorithm is tested on a repository containing a total of 275,000 sentences. This Bengali repository is a product of Technology Development for Indian Languages (TDIL) project sponsored by Govt. of India and provided by the Language Research Unit of Indian Statistical Institute, Kolkata. The shallow parser, developed by the LTRC group of IIIT Hyderabad is used for POS tagging. The actual answer is ranked as 1st in 82.3% cases. The actual answer is ranked within 1st to 5th in 90.0% cases. The accuracy of the system is coming as 97.32% and precision of the system is coming as 98.14% using confusion matrix. The challenges and pitfalls of the work are reported at last in this paper.

Medical speciality classification system based on binary particle swarms and ensemble of one vs. rest support vector machines.

Nowadays, artificial intelligence plays an integral role in medical and healthcare informatics. Developing an automatic question classification and answering system is essential for coping with constant advancements in science and technology. However, efficient online medical services are required to promote offline medical services. This article proposes a system that automatically classifies medical questions of patients into medical specialities and supports the Arabic language in the MENA region. Text classification is not trivial, especially when dealing with a highly morphologically complex language, the dialectical form of which is the dominant form on the Internet. This work utilizes 15,000 medical questions asked by the clients of Altibbi telemedicine company. The questions are classified into 15 medical specialities. As the number of medical questions received daily by the company has increased, a need has arisen for an automatic classification system that can save the medical personnel much time and effort. Therefore, this article presents an efficient medical speciality classification system based on swarm intelligence (SI) and an ensemble of support vector machines (SVMs). Particle swarm optimization (PSO) is an SI-based and stochastic metaheuristic algorithm that is adopted to search for the optimal number of features and tune the hyperparameters of the SVM classifiers, which are deployed as one-versus-rest for multi-class classification. In addition, PSO is integrated with various binarization techniques to boost its performance. The experimental results show that the proposed approach accomplished remarkable performance as it achieved an accuracy of 85% and a features reduction rate of 95.9%.

An Automatic Question Answering Method for Small-Scale Corpus

It is a growing trend for automatic question answering system to be prominent in the development process of society. There are many methods trying to address this problem, but with deficiencies—relatively developed methods based on template matching need a lot of manual work writing templates, and machine learning based methods need plenty of work collecting a large number of corpus, bring huge burden on small-scale scenery. Facing these problems, we propose an automatic question answering methods meeting the needs of small-scale corpus. This method consists of combining an improved text similarity calculation algorithm and an intention recognition method based on slot filling. We conduct experiments on the problem sets of related fields, and it shows a good performance of the proposed automatic question answering method. Our methods make small scene applications for dialog systems more practical.

Automatic Question Answering System Based on Convolutional Neural Network and Its Application to Waste Collection System

As a typical cyber-physical-social system (CPSS), the waste collection system profoundly changes the current waste processing mode and greatly relieves the dilemma of waste disposal. However, the existing waste collection system does not provide the function that guides people to deliver the waste into the correct trash bin. In order to improve the efficiency of waste collection system, we propose an automatic question answering system based on convolutional neural network (CNN) to help people classify waste correctly. The construction process of automatic question answering system is divided into the following steps. We first construct a question answering dataset about waste classification, in which question answering pairs from the four waste categories (recyclable waste, harmful waste, dry waste, and wet waste) are included. After the dataset is constructed, we perform text preprocessing on the dataset, which includes denoising, Chinese word segmentation, and removing stop words. After text preprocessing, we use the Word2vec model as feature representation. Then, we construct a CNN and utilize the word embeddings as an input to train model. Finally, we deploy the trained model to the waste collection system, which can answer the question of waste classification that people ask. We also present a comparative analysis of the proposed method and traditional machine learning methods. The experiment shows that the proposed method has higher accuracy of waste classification than that of traditional machine learning methods.

Enhancing the Performance of Semantic Search in Bengali using Neural Net and other Classification Techniques

To know the information from the internet searching is one of the most important part for any user. In case of ‘Syntactic Search’ keyword based matching technique is used. Search accuracy is improved applying the filter like location, preference, user-history etc. However, it can happen that the user query or question and the best available answer or result in the internet domain has no terms in common or ignorable number of terms is common. In such case syntactic search cannot give the desired output. The role of ‘Semantic Search’ becomes prevalent in this scenario. The execution of semantic search faces challenge due to unavailability of resources like WordNet, Ontology, Annotation etc. An end to end algorithm is described to improve the accuracy of the semantic search in this work. Four classification techniques are used. They are ANN, Decision Tree, SVM and Naïve Bayes. Dataset is provided from the TDIL project of the Ministry of Electronics and IT, Govt. of India. The repository contains 86 categories of text having more than a million sentences. After getting the impressive result for the Bengali language test run was done for other Indian languages and a very good result is achieved. This research is extremely useful for the automatic question answering system, semantic similarity analysis, e-governance and m- governance.

Natural Language Query Based Question Answering System

Development of natural language query based automatic question answering system is in huge demand these days and is a rapidly growing field. It is considered to be the most powerful application for answering different user queries not only on limited domains but also in multi domain environments. In this work, a natural language query based intelligible question answering system is presented that extracts relevant answers from the documents and present the answer in a pre-defined format to the user. A comparative study of the presented model with the traditional techniques is also presented.

Semi-Automatic Corpus Expansion and Extraction of Uyghur-Named Entities and Relations Based on a Hybrid Method

Relation extraction is an important task with many applications in natural language processing, such as structured knowledge extraction, knowledge graph construction, and automatic question answering system construction. However, relatively little past work has focused on the construction of the corpus and extraction of Uyghur-named entity relations, resulting in a very limited availability of relation extraction research and a deficiency of annotated relation data. This issue is addressed in the present article by proposing a hybrid Uyghur-named entity relation extraction method that combines a conditional random field model for making suggestions regarding annotation based on extracted relations with a set of rules applied by human annotators to rapidly increase the size of the Uyghur corpus. We integrate our relation extraction method into an existing annotation tool, and, with the help of human correction, we implement Uyghur relation extraction and expand the existing corpus. The effectiveness of our proposed approach is demonstrated based on experimental results by using an existing Uyghur corpus, and our method achieves a maximum weighted average between precision and recall of 61.34%. The method we proposed achieves state-of-the-art results on entity and relation extraction tasks in Uyghur.

Research Progress of Automatic Question Answering System Based on Deep Learning

With the rapid development of deep learning, a large number of machine reading comprehension models based on deep learning have emerged. Firstly, the paper points out the shortcomings of traditional search engines and explains the advantages of automatic question answering systems compared with them. Secondly, it summarizes the development process of the deep learning-based machine reading comprehension model, and expounds the overall framework and operation principle of the model, as well as the advantages and application scope of the model. Finally, it points out where the development trend lies, and lays the foundation for follow-up researchers.