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In the blogosphere, the amount of digital content is expanding and for search engines, new challenges have been imposed. Due to the changing information need, automatic methods are needed to support blog search users to filter information by different facets. In our work, we aim to support blog search with genre and facet information. Since we focus on the news genre, our approach is to classify blogs...
The optimal exploitation of the information provided by hyperspectral images requires the development of advanced image processing tools. This paper introduces a new hierarchical structure representation for such images using binary partition trees (BPT). Based on region merging techniques using statistical measures, this region-based representation reduces the number of elementary primitives and...
This paper presents the building of part-of-speech Tagger for Malayalam Language using Support Vector Machine (SVM). POS tagger plays an important role in Natural language applications like speech recognition, natural language parsing, information retrieval and information extraction. This supervised machine learning POS tagging approach requires a large amount of annotated training corpus to tag...
Hierarchical taxonomies are used to organize and retrieve information in many domains, especially those dealing with large and rapidly growing amounts of information. In many of these domains data also tends to be multi-label in nature. In this paper, we consider the problem of automated text classification in these scenarios. We present a post-processing based approach that performs smoothing on...
Question classification plays a crucial important role in the question answering system. Recent research on question classification for open-domain mostly concentrates on using machine learning methods to resolve the special kind of text classification. This paper presents our research about Chinese question classification using machine learning method and gives our approach based on SVM and semantic...
A robust and efficient technique for automatic music mood annotation is presented. A song's mood is expressed by a supervised machine learning approach based on musical features extracted from the raw audio signal. A ground truth, used for training, is created using both social network information systems and individual experts. Tests of 7 different classification configurations have been performed,...
We consider the problem of content extraction from online news Web pages. To explore to what extent the syntactic markup and the visual structure of a Web page facilitate the extraction of its content, we compare two state-of-the-art classifiers as first instantiations of a general framework that allows for proper model comparison. To this end, we introduce the publicly available NEWS600 corpus, a...
Indexing, retrieval, and summarization in recordings of meetings have, to date, focused largely on the propositional content of what participants say. Although objectively relevant, such content may not be the sole or even the main aim of potential system users. Instead, users may be interested in information bearing on conversation flow. We explore the automatic detection of one example of such information,...
Past studies on emotion classification focus on the writerpsilas emotional state. This research addresses the reader aspect instead. The classification of documents into reader-emotion categories has several applications. One of them is to integrate reader-emotion classification into a Web search engine to allow users to retrieve documents that contain relevant contents and at the same time instill...
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