A Survey of Text Question Answering Techniques

Abstract

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.

Towards improving the performance of Vector Space Model for Chinese Frequently Asked Question Answering

“…Si =axSui +(I-a)xSbi (4) Where, Si is the combined similarity score between the query vector Q and the ith document D; in the QA database. S U i is the corresponding score based on unigram model.…”

“…QA is a research area which involves information retrieval, information extraction and natural language processing [2,4,5]. The search documents can be in the form of database, knowledge base, local text documents or World Wide Web.…”

The Selective Labels Problem

“…Machine learning models have been very effective at automating various perception and recognition tasks such as character recognition, sentiment detection, question answering, game playing, and image classification [13, 14, 27, 31]. In all these examples, when given the same set of input data and enough labeled training data, machines tend to outperform humans.…”

Question Focus Recognition in Question Answering Systems

“…Many researchers have opted for solving the individual problems involved in such systems separately. While some of these problems are considered to be solved, the majority of them are still open to further research [1,2]. The main goal of question classification is to precisely assign labels to questions based on expected answer category [3].…”