Development Of Question Answering System Research Articles

Automatic question answering for multiple stakeholders, the epidemic question answering dataset

One of the effects of COVID-19 pandemic is a rapidly growing and changing stream of publications to inform clinicians, researchers, policy makers, and patients about the health, socio-economic, and cultural consequences of the pandemic. Managing this information stream manually is not feasible. Automatic Question Answering can quickly bring the most salient points to the user’s attention. Leveraging a collection of scientific articles, government websites, relevant news articles, curated social media posts, and questions asked by researchers, clinicians, and the general public, we developed a dataset to explore automatic Question Answering for multiple stakeholders. Analysis of questions asked by various stakeholders shows that while information needs of experts and the public may overlap, satisfactory answers to these questions often originate from different information sources or benefit from different approaches to answer generation. We believe that this dataset has the potential to support the development of question answering systems not only for epidemic questions, but for other domains with varying expertise such as legal or finance.

Development of Question Answering System for Public Relation Division in Student Admission

Politeknik Statistika STIS (Polstat STIS) holds the new students' admission (PMB) every year which aims to gather, test, and, select all of its applicants who want to continue their study at STIS. STIS establish a committee during this event named Public Relation (PR) Division. PR Division to be intermediaries between STIS and the applicants. One of many PR Division tasks is to reply to all the questions from applicants about administration, procedure, or other things about PMB and STIS. PR Division is facing some problems that can hinder its performance to do the tasks. How do we address the problem is the reason that this research begins in the first place. The goal of this research is to build and establish a web-based system that is capable to solve all the problems the current system has. The system is divided into two main functions, the first one is FAQ management by PR Division members. The other function is a chatbot that automatically answers the question by using the TF-IDF algorithm. The conclusion on all testing and evaluation is the system that being build is already fulfilled all its requirements also the system is feasible to be used.

Question Answering Systems: A Systematic Literature Review

Question answering systems (QAS) are developed to answer questions presented in natural language by extracting the answer. The development of QAS is aimed at making the Web more suited to human use by eliminating the need to sift through a lot of search results manually to determine the correct answer to a question. Accordingly, the aim of this study was to provide an overview of the current state of QAS research. It also aimed at highlighting the key limitations and gaps in the existing body of knowledge relating to QAS. Furthermore, it intended to identify the most effective methods utilized in the design of QAS. The systematic review of literature research method was selected as the most appropriate methodology for studying the research topic. This method differs from the conventional literature review as it is more comprehensive and objective. Based on the findings, QAS is a highly active area of research, with scholars taking diverse approaches in the development of their systems. Some of the limitations observed in these studies encompass the focused nature of current QAS, weaknesses associated with models that are used as building blocks for QAS, the need for standard datasets and question formats hence limiting the applicability of the QAS in practical settings, and the failure of researchers to examine their QAS solutions comprehensively. The most effective methods for designing QAS include focusing on syntax and context, utilizing word encoding and knowledge systems, leveraging deep learning, and using elements such as machine learning and artificial intelligence. Going forward, modular designs ought to be encouraged to foster collaboration in the creation of QAS.

Pseudo-labeling with transformers for improving Question Answering systems

Advances in neural networks contributed to the fast development of Natural Language Processing systems. As a result, Question Answering systems have evolved and can classify and answer questions in an intuitive yet communicative way. However, the lack of large volumes of labeled data prevents large-scale training and development of Question Answering systems, confirming the need for further research. This paper aims to handle this real-world problem of lack of labeled datasets by applying a pseudo-labeling technique relying on a neural network transformer model DistilBERT. In order to evaluate our contribution, we examined the performance of a text classification transformer model that was fine-tuned on the data subject to prior pseudo-labeling. Research has shown the usefulness of the applied pseudo-labeling technique on a neural network text classification transformer model DistilBERT. The results of our analysis indicated that the model with additional pseudo-labeled data achieved the best results among other compared neural network architectures. Based on that result, Question Answering systems may be directly improved by enriching their training steps with additional data acquired cost-effectively.

Классификация и принципы построения систем вопросно-ответного поиска

The analysis of the ambiguity of the natural language is based on the development of Question Answering Systems that can process a user-entered question in a natural language and give a meaningful answer. Unlike the classical keyword search task, in which the result is a list of documents, in the question-answer search task, the result is a short and concise answer generated by the system as a result of analysis of various data sources. The review article lists and considers the main approaches and principles for constructing question-answer systems: a meta-search system, an annotated text search system, an expert system, a search system in question and answer collections. One of the first approaches to Question Answering Systems can be called the BASEBALL system of the early 60s of the last century, since it became possible to ask questions to the system in a natural language, but the knowledge base was a regular structured database. Thus, it can be considered its natural input system. All early Question Answering Systems were faced with the problem of the lack of BigData — a large amount of digitized facts and rules. Really working expert systems were obtained only in a limited domain of knowledge. Therefore, for a qualitative discussion of Question Answering Systems, it is proposed to classify them in the following dimensions: types of supported questions, types of supported answers, source of information, technique for outputting a question or answer by source of information, limited domain of knowledge, quality assessment methods, direction — who asks the question: user or system. Direction is a dimension proposed for the classification of Question Answering Systems for the first time in this article. It defines the lead question-answer dialogue in a pair — a man-computer. A direct question-answer system implies that a person asks questions and the machine answers. An inverted system assumes that the computer is leading this dialogue. Today, one of the most developed and well-known direct Question Answering Systems is the question and answer system on the IBM Watson supercomputer. In the last decade, there has been an active development of educational technologies on the Internet (EduTech). Using the accumulated amount of data on successful or dead-end paths by the user on digitized course materials, it is possible to form an adaptive training course for each of them, which allows to maximize the student’s readiness coefficient. In practice, training is faced with a high threshold of entry by the author of the course, the teacher. He needs not only to restructure the structure of his classical courses, breaking up into smaller blocks, but also to add original questionnaires and elements of gamification of instruction to the pauses between blocks. Inverted Question Answering Systems with an open dialog domain of knowledge have a great prospect in solving the problem of generating original questionnaires, conducting a simple dialogue on an adaptive question graph and introducing gamification elements to improve the perception and assimilation of lecture material.

Qcorp: an annotated classification corpus of Chinese health questions

BackgroundHealth question-answering (QA) systems have become a typical application scenario of Artificial Intelligent (AI). An annotated question corpus is prerequisite for training machines to understand health information needs of users. Thus, we aimed to develop an annotated classification corpus of Chinese health questions (Qcorp) and make it openly accessible.MethodsWe developed a two-layered classification schema and corresponding annotation rules on basis of our previous work. Using the schema, we annotated 5000 questions that were randomly selected from 5 Chinese health websites within 6 broad sections. 8 annotators participated in the annotation task, and the inter-annotator agreement was evaluated to ensure the corpus quality. Furthermore, the distribution and relationship of the annotated tags were measured by descriptive statistics and social network map.ResultsThe questions were annotated using 7101 tags that covers 29 topic categories in the two-layered schema. In our released corpus, the distribution of questions on the top-layered categories was treatment of 64.22%, diagnosis of 37.14%, epidemiology of 14.96%, healthy lifestyle of 10.38%, and health provider choice of 4.54% respectively. Both the annotated health questions and annotation schema were openly accessible on the Qcorp website. Users can download the annotated Chinese questions in CSV, XML, and HTML format.ConclusionsWe developed a Chinese health question corpus including 5000 manually annotated questions. It is openly accessible and would contribute to the intelligent health QA system development.

Development and statistical validation of an ontology-based question answering system

Question answering systems in general use external knowledge sources for extracting answers. Domain specific question answering systems require pre-constructed knowledge sources like domain ontology. A major challenge in knowledge-based question answering system development is building a huge knowledge base with the objective and correct factual knowledge in the preferred domain. The process of collecting useful knowledge from various sources and maintaining in a knowledge repository is a useful process for providing required answer on demand with greater accuracy and efficiency. In this paper an experimental framework has been proposed for concept-relational ontology-based question answering process. The question answering framework proposed in this paper includes two subsystems: 1) a dynamic concept relational ontology construction module, is capable to extract new concepts from the web and incorporate the extracted knowledge into the concept relational ontology knowledge base; 2) the answer extraction module formulates the query string from the natural language question according to the expected answer and retrieves the information from the ontology for answer formation. Furthermore, a novel statistical validation framework for evaluating the prototype implementation of the proposed question answering system is introduced.

Named Entity Recognizer employing Multiclass Support Vector Machines for the Development of Question Answering Systems

Entity Recognition (NER) seeks to locate and classify atomic elements in text into predefined categories such as names of person, organization, location, Quantities, Percentage etc. Named entities tell us the roles of each meaning bearing word in a sentence and hence identification of these entities certainly helps us to extract the essence of the text which is very important in Question Answering(QA) , Information Extraction (IE) and Summarization. The system presented here is a Named Entity (NE) Classifier created using Multiclass Support Vector Machines based on linguistic grammar principles. Malayalam NER is a difficult task as each word of named entity has no specific feature such as Capitalization feature in English. NERs in other languages are not suitable for Malayalam language since its morphology, syntax and lexical semantics is different from them. Also there is no tagged corpus available for training. For testing this system, documents from well known Malayalam news papers and magazines containing passages from five different fields such as sports, health, politics, science and agriculture are selected. Experimental results show that the average precision recall and F-measure values are 89.12%, 89.15% and 89.13% respectively.

Quasinatural language in consultation systems

Following the development of question-answering systems, consultation systems have emerged as a useful integration between data-base management, knowledge representation, and natural-language interaction with the computer. The idea developed in this paper is to insert in a consultation system an input language which is formal but natural-oriented. That is a feasible input, which allows a natural communication with the computer without involving too many theoretical difficulties and practical disadvantages in terms of cost and execution time. The paper illustrates an implementation based on the classical facilities of the goal-oriented languages. Using microplanner as implementation language, we directly obtain the deep structure of the input quasinatural language expressed in microplanner, and this makes the deduction activity more reliable and direct. The problems related to knowledge representation and basic functions of the system are discussed; examples of its use are illustrated.