Abstract:
A method and apparatus for extracting advertisement keywords in association with situations of scenes of video include: establishing a knowledge database including a classification hierarchy for classifying situations of scenes of video and an advertisement keyword list, segmenting a video script corresponding to a received video in units of scenes, and determining a situation corresponding to each scene with reference to the knowledge database, and extracting an advertisement keyword corresponding to the situation of a scene of the received video with reference to the knowledge database.
Abstract:
The subject disclosure is directed towards ranking participants in an online platform according to expertise. A competition-based metric is applied to question and answer threads in order to model each thread as a set of comparisons between various groups of the participants. After aggregating comparison results for the question and answer treads, one or more relative expertise scores may be estimated for each participant. Each relative expertise score may correspond to a specific category of questions.
Abstract:
Exemplary methods, computer-readable media, and systems are presented for learning to recommend questions and other user-generated submissions to community sites based on user ratings. The size of available training data is enlarged by taking into consideration questions without user ratings, which in turn benefits the learned model. Question or other user-generated submissions are obtained by crawling Internet-accessible Web sites including community sites. Questions and other submissions, even when not tagged, voted or indicated as “popular” or “interesting” by users are quantitatively indentified as “interesting.”
Abstract:
Techniques for unsupervised management of a question and answer (QA) forum include labeling of answers for quality purposes, and identification of experts. In a QA thread, a ranking of answers may include an initial labeling of the longest answer in each thread as the best answer. Such a labeling provides an initial point of reference. Then, in an iterative manner answerers are ranked using the labeling. The ranking of answerers allows selection of experts and poor or inexpert answerers. A label update is performed using the experts (and perhaps inexpert answerers) as input. The label update may be used to train a model, which may describe quality of answers in one or more QA threads and an indication of expert and inexpert answerers. The iterative process may be ended upon convergence or upon a maximum number of iterations.
Abstract:
A method and apparatus for extracting advertisement keywords in association with situations of scenes of video include: establishing a knowledge database including a classification hierarchy for classifying situations of scenes of video and an advertisement keyword list, segmenting a video script corresponding to a received video in units of scenes, and determining a situation corresponding to each scene with reference to the knowledge database, and extracting an advertisement keyword corresponding to the situation of a scene of the received video with reference to the knowledge database.
Abstract:
Techniques for unsupervised management of a question and answer (QA) forum include labeling of answers for quality purposes, and identification of experts. In a QA thread, a ranking of answers may include an initial labeling of the longest answer in each thread as the best answer. Such a labeling provides an initial point of reference. Then, in an iterative manner answerers are ranked using the labeling. The ranking of answerers allows selection of experts and poor or inexpert answerers. A label update is performed using the experts (and perhaps inexpert answerers) as input. The label update may be used to train a model, which may describe quality of answers in one or more QA threads and an indication of expert and inexpert answerers. The iterative process may be ended upon convergence or upon a maximum number of iterations.