During recent years, question answering (QA) has grown from simple passage retrieval and information extraction to very complex approaches that incorporate deep question and document analysis, reasoning, planning, and sophisticated uses of knowledge resources. Most existing QA systems combine rule-based, knowledge-based and statistical components, and are highly optimized for a particular style...

We have developed a method for answering single answer questions automatically using a collection of documents or the Internet as a source of data for the production of the answer. Examples of such questions are ‘What is the melting point of tin?’, and ‘Who wrote the novel Moby Dick?’. The approach we have adopted to the problem uses the Mikrokosmos ontology to represent knowledge about questio...

Question answering aims to develop techniques that can go beyond the retrieval of relevant documents in order to return exact answers to natural language questions, such as “How tall is the Eiffel Tower?”, “Which cities have a subway system?”, and “Who is Alberto Tomba?”. Answering natural language questions requires more complex processing of text than employed by current information retrieval...

This paper describes the Webclopedia Question Answering system, in which methods to automatically learn patterns and parameterizations are combined with hand-crafted rules and concept ontologies. The source for answers is a collection of 1 million newspaper texts, distributed by NIST. In general, two kinds of knowledge are used by Webclopedia to answer questions: knowledge about language and kn...

In this paper, we have proposed a rule based Automated QuestionAnswering system which aims at delivering concise information that contains answers to user questions. The context would be the domain specific systems. This technique is the solution to the problem of unlimited or irrelevant data which is bombarded on the user as a result of his query on any search engine. Given a question, our sys...

This study is devoted to the problem of question analysis for a Polish question answering system. The goal of the question analysis is to determine its general structure, type of an expected answer and create a search query for finding relevant documents in a textual knowledge base. The paper contains an overview of available solutions of these problems, description of their implementation and ...

The first step of processing a question in Question Answering(QA) Systems is to carry out a detailed analysis of the question for the purpose of determining what it is asking for and how to perfectly approach answering it. Our Question analysis uses several techniques to analyze any question given in natural language: a Stanford POS Tagger & parser for Arabic language, a named entity recognizer...

English. Question Answering (QA) is an important aspect of Natural Language Processing. It comprises building a system that automatically answers questions sought in natural language. Frequently Asked Questions (FAQs) are a set of listed questions and answers concerning a specific topic, which are most likely to be enquired by a user. This paper deals with developing an open domain QA system fo...

With the increasing heterogeneity and specialization of medical texts, automated question answering is becoming more and more challenging. In this context, answering a given medical question by retrieving similar questions that are already answered by human experts seems to be a promising solution. In this paper, we propose a new approach for the detection of similar questions based on Recogniz...

This paper describes machine learning based parsing and question classification for question answering. We demonstrate that for this type of application, parse trees have to be semantically richer and structurally more oriented towards semantics than what most treebanks offer. We empirically show how question parsing dramatically improves when augmenting a semantically enriched Penn treebank tr...