QUESTION ANSWERING SYSTEM WITH NATURAL LANGUAGE INTERFACE TO DATABASE (original) (raw)
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International Journal of Computer Applications
Search engines have played a very important role in helping the users to search the necessary information from the huge information. By displaying the list of links to documents.The Question-Answering systems are gaining popularity. Because The main benefit of such QA systems is that the user can ask the query (question) in natural language and he /she get a precise and appropriate answer instead of just displaying a list of links to documents. The main advantage of the proposed Question answering system, which is not restricted to a specific domain. This approach is related to a natural language interface to the database (NLIDB), which takes a natural language query as input and giving the appropriate answer from the manually created knowledge base(structured database). There are two main steps of implementation of the proposed question answering system. The first step is to use a classifier to identify appropriate tables and columns in a structured database for an incoming question, and the second step is to perform the free text retrieval to lookup answer. The system uses named entity normalization, part-of-speech tagging, and a statistical classifier trained on data from the TREC QA task.
A Hindi Question Answering System using Machine Learning approach
2016 International Conference on Computational Techniques in Information and Communication Technologies (ICCTICT), 2016
As an upshot of Natural Language Interface to Database (NLIDB), Question Answering System is relatively an Information Retrieval system which is suppose to reflect the user with the correct or closest results to the query being asked to the system in natural language. Information Retrieval and Information Extraction plays vital role in accomplishing the task of interaction between the user and the system. The paper discusses various ways and techniques of interaction between the user and the system along with different approaches. Machine Learning is one of the approaches which is preferred for the Question Answering System. Out of Supervised and unsupervised learning, supervised learning is taken priority here by going through numerous other techniques.
Implementation approaches for various categories of question answering system
2013 IEEE CONFERENCE ON INFORMATION AND COMMUNICATION TECHNOLOGIES, 2013
Search engines can return ranked documents as a result for any query from which the user struggle to navigate and search the correct answer. This process wastes user's navigation time and due to this the need for automated question answering systems becomes more urgent. We need such a system which is capable of replying the exact and concise answer to the question posed in natural language. The best way to address this problem is use of Question answering systems (QAS). The basic aim of QAS is to provide short and correct answer to the user saving his/her navigation time. The concept of Natural Language Processing plays an important role in developing any QAS. This paper provides an implementation approaches for various categories of QAS such as Closed Domain based QAS, Open Domain based QAS, WEBBASED QAS, Information Retrieval or Information Extraction (IR/IE) based QAS, and Rule based QAS which will be helpful for new directions of research in this area.
Question Answering System for Election Database Using NLP
In this paper, Question answering (QA) system for Election information using NLP has been described. The main goal is to extract election information in Telugu Language. User type input in Telugu, system will generate exact answers in Telugu so that this system is useful to technical as well as non-technical users. Same question can be framed in several ways but the meaning and answer is same in Telugu. This proposed system will give the answers smartly even though user frames queries in different manner. The election QA system is helpful to the pupil in different ways. This system needs an interface between user and database i.e., Telugu Language Interface to Database. It can be called as Natural Language interface to DB (NLIDB). By generating SQL Queries, answer can be collected from single Table or multiple tables. The accurate answers will be very useful and time saving. In this system we use pattern matching technique to extract the answer from database and produces answer in Telugu to the user.
A Review Paper: Question and Answering System
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.
Question Answering System, Approaches and Techniques: A Review
As technology developed the use of internet has tremendously increased because of the availability of huge amount of data. Question answering is a specialized area in the field of information retrieval Text Processing. Question Answering system has many application based on source of answering like extracting information from document, language learning, online examination etc.
Question Answering System Using Ontology in Marathi Language
International Journal of Artificial Intelligence & Applications
Humans are always in a quest to extract information related to some topic or entity. Question answering system helps user to find the precise answer of the question articulated in natural language. Question answering system provides explicit, concise and accurate answer to user questions rather than providing set of relevant documents or web pages as answers as most of the information retrieval system does. The paper proposes question answering system for Marathi natural language by using concept of ontology as a formal representation of knowledge base for extracting answers. Ontology is used to express domain specific knowledge about semantic relations and restrictions in the given domains. The ontologies are developed with the help of domain experts and the query is analyzed both syntactically and semantically. The results obtained here are accurate enough to satisfy the query raised by the user. The level of accuracy is enhanced since the query is analyzed semantically.
NLP Algorithm Based Question and Answering System
2015
Question and Answering (QA) systems are referred as virtual assistants and are envisioned to be the next generation call center. However the accuracy of such QA systems is not as desirable and needs significant enhancement. Understanding the intent of the query is a significant contributor to an efficient system which has not been often analyzed. The current study intends to develop a QA system which can understand the query intent by using NLP based classification along with a novel scoring mechanism to extract the related information. Initially an insurance domain based simple frequently asked QA system was developed, which was then operationally enhanced into a system that can answer any type of insurance related query. The system was tested with 200 different queries and the output was cross validated by 3 experts from insurance field.
An Intelligent Database System using Natural Language Processing
Enormous amount of data are being processed and exchanged in our daily life, and database, which is used to organize data has been an active research topic for a long time. Database plays a major role in many computer systems and there is always a demand from technical and nontechnical people to ease the process of accessing data on database. Using Natural Language to directly interact with a database is a nice and user friendly solution. In order to achieve this type of communication between the computer (In particular, database) and human we have to make the computer understand what the human asks, and then, be able to respond with the right answer that was expected to be extracted from the database. In this paper we present an intelligent system for converting Natural Language queries into equivalent database Structured Query Language (SQL). Our system also allows processing complex Natural Language queries. We call this Intelligent Agent based Natural Language Interface to Database (INLIDB). The query results from the INLIDB is presented in an attractive succinctly viewable format. We have obtained encouraging results from INLIDB.
Research and Reviews in Question Answering System
Procedia Technology, 2013
Question Answering (QA) Systems is an automated approach to retrieve correct responses to the questions asked by human in natural language. The fundamental thought behind QA system is to assist man-machine interaction. In this paper, we propose taxonomy for characterizing Question Answer (QA) systems, briefly survey major QA systems described in literature and provide a qualitative analysis of them. Finally, a comparison between these approaches based on certain features of QA system found critical in our study has been done, in order to bring an insight to research scope in this direction.