The Question Answering Systems : A Survey

Question answering is an important technique in this new era of information. Question answering system comes under the branch of natural language processing. It works with full potential of information extraction and information retrieval in the area of text mining. Automatically answering repeated question will help us to answer repeated questions exactly ones and it will help to save wastage of resource. This method is very well suited for web based QA systems.This paper mainly aims different technique in answering repeated questions.

Information Sciences, 2011

This article provides a comprehensive and comparative overview of question answering technology. It presents the question answering task from an information retrieval perspective and emphasises the importance of retrieval models, i.e., representations of queries and information documents, and retrieval functions which are used for estimating the relevance between a query and an answer candidate. The survey suggests a general question answering architecture that steadily increases the complexity of the representation level of questions and information objects. On the one hand, natural language queries are reduced to keyword-based searches, on the other hand, knowledge bases are queried with structured or logical queries obtained from the natural language questions, and answers are obtained through reasoning. We discuss different levels of processing yielding bag-of-words-based and more complex representations integrating part-of-speech tags, classification of the expected answer type, semantic roles, discourse analysis, translation into a SQL-like language and logical representations.

Learning and Analytics in Intelligent Systems, 2019

Question Answering systems (QASs) is a system that provide answers to the question or query asked by the user in the natural language. It retrieves small portion of text from the collection of document which contains the answer of the user’s question. Therefore to retrieve such an accurate and precise answer from the collection of document, Information Retrieval (IR) Techniques are required and to process or understand the user’s question posed in the natural language (NLP) Natural Language Techniques are used.In this survey paper we will see what exactly a Question Answering System is, previous work done on such Question Answering system and we will also compare research against each other with respect to the different approaches that were followed and components that were used. At the end, the survey gives a clear comparison between the different QASs and idea of the our proposed QAS model.

Question Answering System could automatically provide an answer to a question posed by human in natural languages. This system consists of question analysis, document processing, and answer extraction module. Question Analysis module has task to translate query into a form that can be processed by document processing module. Document processing is a technique for identifying candidate documents, containing answer relevant to the user query. Furthermore, answer extraction module receives the set of passages from document processing module, then determine the best answers to user. Challenge to optimize Question Answering framework is to increase the performance of all modules in the framework. The performance of all modules that has not been optimized has led to the less accurate answer from question answering systems. Based on this issues, the objective of this study is to review the current state of question analysis, document processing, and answer extraction techniques. Result from this study reveals the potential research issues, namely morphology analysis, question classification, and term weighting algorithm for question classification

2015

Question answering system aims to develop techniques that can go beyond the retrieval of relevant documents in order to return exact answers to natural language questions posed by the user. Answering natural language questions requires more complex processing of text than employed by current information retrieval systems. A number of question answering systems have been developed which are capable of carrying out the processing required to achieve high levels of accuracy. Existing Question Answering system are unable to handle variety of questions and reasoning based question. Also, because of the lack of the data sources, the QA system fails to answer the question. In this paper, a comparative analysis of several Question Answering Systems is presented.

International Journal of Computer Applications, 2013

It is examined that the power of ontology based for open and closed domain question and its answering systems in this paper. In the order of obtaining an optimal database for this system, it has studied the method for linking the different phrases of different web links. The tagged corpus is built from an Internet in the bootstrapping process by providing some of the handcrafted examples of each question and their types. And then the patterns are automatically extracted from the returned documents and formatted answers are provided according to the entities and keywords provided. The precision of each entity has been calculated, and the each question type's average. This ontology is then applied to find the answers of new questions which are about to ask.

2008

Question answering (QA) aims at retrieving precise information from a large collection of documents. Most of the Question Answering systems composed of three main modules: question processing, document processing and answer processing. Question processing module plays an important role in QA systems to reformulate questions. Moreover answer processing module is an emerging topic in QA systems, where these systems are often required to rank and validate candidate answers. These techniques aiming at finding short and precise answers are often based on the semantic relations and co-occurrence keywords. This paper discussed about a new model for question answering which improved two main modules, question processing and answer processing which both affect on the evaluation of the system operations. There are two important components which are the bases of the question processing. First component is question classification that specifies types of question and answer. Second one is reform...

International Journal of Computer Applications, 2012

Question Answering (QA) is a specific type of information retrieval. Given a set of documents, a Question Answering system attempts to find out the correct answer to the question pose in natural language. Question answering is multidisciplinary. It involves information technology, artificial intelligence, natural language processing, knowledge and database management and cognitive science. From the technological perspective, question answering uses natural or statistical language processing, information retrieval, and knowledge representation and reasoning as potential building blocks. It involves text classification, information extraction and summarization technologies. In general, question answering system (QAS) has three components such as question classification, information retrieval, and answer extraction. These components play a essential role in QAS. Question classification play primary role in QA system to categorize the question based upon on the type of its entity. Information retrieval method is get of identify success by extracting out applicable answer post by their intelligent question answering system. Finally, answer extraction module is rising topics in the QAS where these systems are often requiring ranking and validating a candidate's answer. Most of the Question Answering systems consists of three main modules: question processing, document processing and answer processing. Question processing module plays an important part in QA systems. If this module doesn't work correctly, it will make problems for other sections. Moreover answer processing module is an emerging topic in Question Answering, in which these systems are often required to rank and validate candidate answers. These techniques aiming at discovering the short and precise answers are often based on the semantic classification. QA systems give the ability to answer questions posed in natural language by extracting, from a repository of documents, fragments of documents that contain material relevant to the answer.

2014 37th International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), 2014

With the development of ICT the need for automated question-answering systems is becoming increasingly important. Question-answering systems are still under development and experimentation. This paper is an overview of the research area that deals with question-answering systems; it explains the concept of question-answering systems and points out the problems that occur during their development. It also refers to a complex assessment techniques that are necessary when designing such systems. The system described is a real system and so are the test results.

2011

The article presents the experiments carried out as part of the participation in the main task of QA4MRE@CLEF 2011. We have submitted total five unique runs in the main task: two runs from systems based on Answer Validation (AV) machine reading techniques, one run from systems based on Question Answering (QA) techniques while the last two runs are hybrid systems where the decision is taken based on the outputs from the AV and QA based systems. In the AV system, we first combine the question and each answer option to form the Hypothesis (H). Stop words are removed from each H and query words are identified to retrieve the most relevant sentences from the associated document using Lucene. Relevant sentences are retrieved from the associated document based on the TF-IDF of the matching query words along with n-gram overlap of the sentence with the H. Each retrieved sentence defines the Text T. Each T-H pair is assigned a ranking score in the AV system that works on textual entailment principle. The answer option for which the T-H pair gets the maximum score is selected as the possible answer. The two unique runs differ in the way in which the relevant sentences are retrieved from the associated document. The second system is based on Question Answering (QA) technique. Each question along with each answer option generates the possible answer patterns. Each sentence in the associated document is assigned an inference score with respect to each answer pattern. The sentence that receives the highest inference score corresponding to the answer patterns is identified as the relevant sentence in the document and the corresponding answer option is selected as the answer to the given question.