Question classification is very important for question answering. This paper present our research work on question classification through machine learning approach. In order to train the learning model, we designed a rich set of features that are predictive of question categories. An important component of question answering systems is question classification. The task of question classification is to predict the entity type of the answer of a natural language question. Question classification is typically done using machine learning techniques. Different lexical, syntactical and semantic features can be extracted from a question. In this work we combined lexical, syntactic and semantic features which improve the accuracy of classification. Furthermore, we adopted three different classifiers: Nearest Neighbors (NN), Naïve Bayes (NB), and Support Vector Machines (SVM) using two kinds of features: bag-of-words and bag-of n grams.Furthermore, we discovered that when we take SVM classifier and combine the semantic, syntactic, lexical feature we found that it will improve the accuracy of classification. We tested our proposed approaches on the well-known UIUC dataset and succeeded to achieve a new record on the accuracy of classification on this dataset.

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There has been a significant surge in web-based data interchange and digital entertainment consumption. The growing interest in digital watermarking over the last decade is undoubtedly attributable to the growing necessity for copyright protection. The use of video watermarking in copy control, transmission surveillance, fingerprints, video authentication, and copyright is growing exponentially. The primary characteristics of information concealment are capacity, safety, and resilience. Typically, video watermarking techniques prioritize resilience. In this article, we explore the concept of different watermarking techniques as well as the components necessary to create a watermarked that is effective for preventing piracy an important applications, and also focusing on the many areas of video watermarking approaches.

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