Developing a Semantic Question Answering System for E-learning Environments using Linguistic Resources

The Question answering (QA) system plays a basic role in the acquisition of information and the e-learning environment is considered to be the field that is most in need of the question-answering system to help learners ask questions in natural language and get answers in short periods of time. The main problem in this context is how to understand the questions without any doubts in meaning and how to provide the most relevant answers to the questions. In this study, a question-answering system for specific courses has been developed to support the learning environment. The research outcomes indicate that the proposed method helps to solve the problem of ambiguities in meaning through the integration of natural language processing tools and semantic resources that can help to overcome several problems related to the natural language structure. This method also helps improve the capability to understand students’ needs and, consequently, to retrieve the most suitable answers

Online answers dealing with the internment of Japanese-Americans during World War II

The internment of Americans of Japanese descent during World War II lies at the heart of ongoing discussions in American social studies. We analyzed inputs of members of the Yahoo! Answers Q&A online community following students’ questions dealing with differential treatment of Japanese, and German and Italian American citizens during World War II, and whether the internment of Japanese Americans was justified. The questions were submitted to the community by students struggling with their coursework. The majority of responses to first question justified the differential treatment, citing national security and presenting Japanese-Americans as a threat. The dominant position in the case of the second question negates internment legitimacy, and views it as a gross violation of justice and as a racially motivated act. These stances, likely to make their way into submitted assignments by students, necessitate the familiarization of teachers with such discussions as they take place within Q&A communities

Question Answering System : A Review On Question Analysis, Document Processing, And Answer Extraction Techniques

Question Answering System could automatically provide an answer to a question posed by human in natural languages. This system consists of question analysis, document processing, and answer extraction module. Question Analysis module has task to translate query into a form that can be processed by document processing module. Document processing is a technique for identifying candidate documents, containing answer relevant to the user query. Furthermore, answer extraction module receives the set of passages from document processing module, then determine the best answers to user. Challenge to optimize Question Answering framework is to increase the performance of all modules in the framework. The performance of all modules that has not been optimized has led to the less accurate answer from question answering systems. Based on this issues, the objective of this study is to review the current state of question analysis, document processing, and answer extraction techniques. Result from this study reveals the potential research issues, namely morphology analysis, question classification, and term weighting algorithm for question classification

PowerAqua: Open Question Answering on the Semantic Web

With the rapid growth of semantic information in the Web, the processes of searching and querying these very large amounts of heterogeneous content have become increasingly challenging. This research tackles the problem of supporting users in querying and exploring information across multiple and heterogeneous Semantic Web (SW) sources. A review of literature on ontology-based Question Answering reveals the limitations of existing technology. Our approach is based on providing a natural language Question Answering interface for the SW, PowerAqua. The realization of PowerAqua represents a considerable advance with respect to other systems, which restrict their scope to an ontology-specific or homogeneous fraction of the publicly available SW content. To our knowledge, PowerAqua is the only system that is able to take advantage of the semantic data available on the Web to interpret and answer user queries posed in natural language. In particular, PowerAqua is uniquely able to answer queries by combining and aggregating information, which can be distributed across heterogeneous semantic resources. Here, we provide a complete overview of our work on PowerAqua, including: the research challenges it addresses; its architecture; the techniques we have realised to map queries to semantic data, to integrate partial answers drawn from different semantic resources and to rank alternative answers; and the evaluation studies we have performed, to assess the performance of PowerAqua. We believe our experiences can be extrapolated to a variety of end-user applications that wish to open up to large scale and heterogeneous structured datasets, to be able to exploit effectively what possibly is the greatest wealth of data in the history of Artificial Intelligence

Educational Question Answering based on Social Media Content

Abstract. We analyze the requirements for an educational Question Answering (QA) system operating on social media content. As a result, we identify a set of advanced natural language processing (NLP) technologies to address the challenges in educational QA. We conducted an inter-annotator agreement study on subjective question classification in the Yahoo!Answers social Q&A site and propose a simple, but effective approach to automatically identify subjective questions. We also developed a two-stage QA architecture for answering learners ’ questions. In the first step, we aim at re-using human answers to already answered questions by employing question paraphrase identification [1]. In the second step, we apply information retrieval techniques to perform answer retrieval from social media content. We show that elaborate techniques for question preprocessing are crucial

Educational Question Answering based on Social Media Content

International audienc