With the development of Web 2.0 technologies, social question and answering has become an important venue for individuals to seek for information that are important to their everyday lives. While prior literatures studying social question answering have suggested the possibility of routing questions to potential answerers for assistance, still little is known about how effective these question ...

This paper is concerned with the problem of question search. In question search, given a question as query, we are to return questions semantically equivalent or close to the queried question. In this paper, we propose to conduct question search by identifying question topic and question focus. More specifically, we first summarize questions in a data structure consisting of question topic and ...

Question terminology is a set of terms which appear in keywords, idioms and fixed expressions commonly observed in questions. This paper investigates ways to automatically extract question terminology from a corpus of questions and represent them for the purpose of classifying by question type. Our key interest is to see whether or not semantic features can enhance the representation of strongl...

We present a multi-sentence question segmentation strategy for community question answering services to alleviate the complexity of long sentences. We develop a complete scheme and make a solution to complexquestion segmentation, including a question detector to extract question sentences, a question compression process to remove duplicate information, and a graph model to segment multi-sentenc...

With the blooming of online social media applications, Community Question Answering (CQA) services have become one of the most important online resources for information and knowledge seekers. A large number of high quality question and answer pairs have been accumulated, which allow users to not only share their knowledge with others, but also interact with each other. Accordingly, volumes of ...

The German question answering (QA) system IRSAW (formerly: InSicht) participated in QA@CLEF for the fifth time. IRSAW was introduced in 2007 by integrating the deep answer producer InSicht, several shallow answer producers, and a logical validator. InSicht builds on a deep QA approach: it transforms documents to semantic representations using a parser, draws inferences on semantic representatio...

We study the problem of joint question answering (QA) and question generation (QG) in this paper. Our intuition is that QA and QG have intrinsic connections and these two tasks could improve each other. On one side, the QAmodel judges whether the generated question of a QG model is relevant to the answer. On the other side, the QG model provides the probability of generating a question given th...

the main purpose of this research is the exploration functions of question in the understanding process and features of teacher and student as a questioner. for this purpose is used conceptual analysis and deductive method. so according to study question has a key role in the understanding and has some tasks in its steps: enable pre-understanding and prejudices, challenging text in the detailed...

In this paper, we investigate how an accurate question classifier contributes to a question answering system. We first present a Maximum Entropy (ME) based question classifier which makes use of head word features and their WordNet hypernyms. We show that our question classifier can achieve the state of the art performance in the standard UIUC question dataset. We then investigate quantitativel...

We address the challenge of automatically generating questions from reading materials for educational practice and assessment. Our approach is to overgenerate questions, then rank them. We use manually written rules to perform a sequence of general purpose syntactic transformations (e.g., subject-auxiliary inversion) to turn declarative sentences into questions. These questions are then ranked ...