TEMPORAL QUESTION ANSWERING SYSTEM: A SURVEY

Language Resources and …, 2005

Abstract. In this paper, we discuss the role that temporal information plays in natural language text, specifically in the context of question answering systems. We define a descriptive framework with which we can examine the temporally sensitive aspects of natural language queries. ...

2006

We pursue two strategies for oine data collection for a temporal question answering system that uses both quantitative methods and fuzzy methods to reason about time and events. The first strategy extracts event descriptions from the structured year entries in the online encyclopedia Wikipedia, yielding clean quantitative temporal information about a range of events. The second strategy mines the web

Lecture Notes in Computer Science, 2006

A temporal question answering system must be able to deduce which qualitative temporal relation holds between two events, a reasoning task that is complicated by the fact that historical events tend to have a gradual beginning and ending. In this paper, we introduce an algebra of temporal relations that is well-suited to represent the qualitative temporal information we have at our disposal. We provide a practical algorithm for deducing new temporal knowledge, and show how this can be used to answer questions that require several pieces of qualitative and quantitative temporal information to be combined. Finally, we propose a heuristic technique to cope with inconsistencies that may arise when integrating qualitative and quantitative information.

2009

This paper presents a multilayered architecture that enhances the capabilities of current QA systems and allows different types of complex questions or queries to be processed. The answers to these questions need to be gathered from factual information scattered throughout different documents. Specifically, we designed a specialized layer to process the different types of temporal questions. Complex temporal questions are first decomposed into simple questions, according to the temporal relations expressed in the original question. In the same way, the answers to the resulting simple questions are recomposed, fulfilling the temporal restrictions of the original complex question. A novel aspect of this approach resides in the decomposition which uses a minimal quantity of resources, with the final aim of obtaining a portable platform that is easily extensible to other languages. In this paper we also present a methodology for evaluation of the decomposition of the questions as well as the ability of the implemented temporal layer to perform at a multilingual level. The temporal layer was first performed for English, then evaluated and compared with: a) a general purpose QA system (F-measure 65.47% for QA plus English temporal layer vs. 38.01% for the general QA system), and b) a well-known QA system. Much better results were obtained for temporal questions with the multilayered system. This system was therefore extended to Spanish and very good results were again obtained in the evaluation (F-measure 40.36% for QA plus Spanish temporal layer vs. 22.94% for the general QA system).

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.

Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), 2015

QA TempEval shifts the goal of previous TempEvals away from an intrinsic evaluation methodology toward a more extrinsic goal of question answering. This evaluation requires systems to capture temporal information relevant to perform an end-user task, as opposed to corpus-based evaluation where all temporal information is equally important. Evaluation results show that the best automated TimeML annotations reach over 30% recall on questions with 'yes' answer and about 50% on easier questions with 'no' answers. Features that helped achieve better results are event coreference and a time expression reasoner.

Current Issues in Linguistic Theory, 2004

The growing interest in practical NLP applications such as text summarization and question-answering places increasing demands on the processing of temporal information in natural languages. To support this, several new capabilities have emerged. These include the ability to tag events and time expressions, to temporally anchor and order events, and to build models of the temporal structure of discourse. This paper describes some of the techniques and the further challenges that arise.