Multimedia Answering and Retrieval System based on CQA with Media Query Generation

The question answering system which has recently received an attention from the various information retrieval systems, machine learning, information extraction and the natural language processing the goal of the QAS is to retrieve the answer to the question than full documents. This question answering system which works on the various modules related only to the question processing, the document processing, and the answer processing. This QAS which doesn’t work properly with the main module which is questioning processing this system fails to categorize properly the questions. So to overcome the QAS the Community question answering (CQA) has gained popularity. As compare to QAS and automated QA sites the CQA sites are more effective. In this drawback available for community question answering system is that it only provides the textual answer. Here in this paper, we propose a scheme that enhances the textual answer with the multimedia data. The outline of Community question answering which mainly consists of three components: the selection of answer medium, the query generation for multimedia search and the selection and presentation of multimedia data. This approach automatically defines which type of media information should be added for the textual answer. Then it automatically collects the data from the web to supplement the answer.by handling an available dataset of QA pairs and adding them to a pool, in this, our approach is to allow a new multimedia question answering (MMQA) approach so as the users can find the answer in multimedia matching the questions pair those in the pool. Therefore, the users can approach MMQA from Web information will answer the questions in different media formats (text, video, and image) as particularly selected by the users

Event-based Access to Historical Italian War Memoirs

The progressive digitization of historical archives provides new, often domain specific, textual resources that report on facts and events which have happened in the past; among these, memoirs are a very common type of primary source. In this paper, we present an approach for extracting information from Italian historical war memoirs and turning it into structured knowledge. This is based on the semantic notions of events, participants and roles. We evaluate quantitatively each of the key-steps of our approach and provide a graph-based representation of the extracted knowledge, which allows to move between a Close and a Distant Reading of the collection.Comment: 23 pages, 6 figure

An Approach to Knowledge Discovery by Data Harvesting

In mining technology the text mining plays a vital role in today’s life. Text mining is cluster data like user needs and classify the data.But its having some challenges like Information is in unstructured textual form, Not readily accessible to be used by computers, Dealing with huge collections of documents.One can express personal experiences and opinions on almost anything, at review sites, forums, discussion groups, blogs ..., (called the user generated content.)They contain valuable information in this processing cost is indeed. However In text and opinion mining problems we not solved So this paper address the problem of knowledge discovery for question and answers. Here we are presented knowledge discovery with Markov techniques   and comparable techniques, these are present rigorous information about the mining. My results shows potent and emotive information for asking questions

Passage Retrieval Using Answer Type Profiles in Question Answering

PACLIC 23 / City University of Hong Kong / 3-5 December 200

Structured Use of External Knowledge for Event-based Open Domain Question Answering

SIGIR Forum (ACM Special Interest Group on Information Retrieval)SPEC. ISS.33-40FASR

Avaliação de agentes de conversação : a influência de elementos multimédia

Tese de mestrado. Multimédia. Faculdade de Engenharia. Universidade do Porto. 201

MULTIMEDIA QUESTION ANSWERING AND CONTENT-BASED PRODUCT ANNOTATION AND SEARCH

Ph.DDOCTOR OF PHILOSOPH

combining multimodal external resources for event-based news video retrieval and question answering

Ph.DDOCTOR OF PHILOSOPH