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Due to the exponential growth of available text documents in digital form, it is of great importance to develop techniques for automatic document classification based on the textual contents. Earlier document classification techniques have used keyword-based features and related statistics to achieve good results when
In this paper, a new method for question classification is proposed, which employs ensemble learning algorithms to train multiple question classifiers. These component learners are combined to produce the final hypothesis. In detail, the feature spaces are obtained through extracting high-frequency keywords from
Most web search engines use only the search keywords for searching. Due to the ambiguity of semantics and usages of the search keywords, the results are noisy and many of them do not match the user's search goals. This paper presents the design of an intelligent Search Bot, which operates as an agent for a user by
Today location technologies are integrated into many devices enabling location-based services. Movement data recorded with these devices can be uploaded to web sites and shared with others. Movement data can be organized using keywords and semantic tags, e.g. walking and running. Our main goal is to automatically
incidents documents in text or typing keywords. Furthermore, the rules resulting from the rule generation from the decision tree could be properly kept in a knowledge database in order to support and assist with future assignments.
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