Абстрактный

Beyond Text QA Multimedia diverse relevance ranking based Answer Generation by Extracting Web

R.Manju

Community QA (cQA) provides only textual answers, which are not informative enough for many questions. To overcome these problems previous studies proposed three steps: First, information seekers are able to post their specific questions on any topic and obtain answers provided by other participants. Second, in comparison with automated QA systems. Third, over times, a tremendous number of QA pairs have been accumulated in their repositories. But the major problem is the lack of diversity of the generated media data .It estimates the relevance scores of images with respect to the query term based on both the visual information of images and the semantic information of associated tags. Then, we estimate the semantic similarities of social images based on their tags. Based on the relevance scores and the similarities, the ranking list is generated by a greedy ordering algorithm which optimizes average diverse precision, a novel measure that is extended from the conventional average precision