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This paper presents a novel framework for multi-folder email classification using graph mining as the underlying technique. Although several techniques exist (e.g., SVM, TF-IDF, n-gram) for addressing this problem in a delimited context, they heavily rely on extracting high-frequency keywords, thus ignoring the
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
source, specifically Yahoo's ldquosuggested keywordsrdquo. These keywords are based on co-occurrence data across queries. The classifier, which is built offline with training data, makes use of the top-n results during training, but not duing testing. Thus, there is an asymmetry between the training and testing data. We
Audio tags correspond to keywords that people use to describe different aspects of a music clip. With the explosive growth of digital music available on the Web, automatic audio tagging, which can be used to annotate unknown music or retrieve desirable music, is becoming increasingly important. This can be achieved by
learning approach. We use a graphical model, Dynamic Conditional Random Fields (DCRFs), for training our classifier. Our approach is based on semantic analysis of text to classify the predicates describing coexpression relationship rather than detecting the presence of keywords. We compared our results of sentence
Traditional text learning algorithms need labeled documents to supervise the learning process, but labeling documents of a specific class is often expensive and time consuming. We observe it is convenient to use some keywords(i.e. class-descriptions) to describe class sometimes. However, short class-description
and converts it into routing keywords. Accent identification is the most important factor for improving the performance of natural language call-routing systems because accents vary widely, even within the same country or community. This variation occurs when non-native speakers start to learn a second language; the
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