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Automatic classification of news articles is a relevant problem due to the large amount of news generated every day, so it is crucial that these news are classified to allow for users to access to information of interest quickly and effectively. On the one hand, traditional classification systems represent documents as bag-of-words (BoW), which are oblivious to two problems of language: synonymy and...
Bug reporting is essentially an uncoordinated process. The same bugs could be repeatedly reported because users or testers are unaware of previously reported bugs. As a result, extra time could be spent on bug triaging and fixing. In order to reduce redundant effort, it is important to provide bug reporters with the ability to search for previously reported bugs. The search functions provided by the...
This paper proposes an unsupervised two-stage approach to automatically extract keywords from spoken documents. In the first stage, for each candidate term we compute a topic coherence and term significance measure (TCS) based on probabilistic latent semantic analysis (PLSA) models. In the second stage, we take the candidate terms with highest and lowest TCS scores as positive and negative examples...
There is a substantial body of work on the extraction of relations from texts, most of which is based on pattern matching or on applying tree kernel functions to syntactic structures. Whereas pattern application is usually more efficient, tree kernels can be superior when assessed by the F-measure. In this paper, we introduce a hybrid approach to extracting meronymy relations, which is based on both...
The use of domain knowledge is generally found to improve query efficiency in content filtering applications. In particular, tangible benefits have been achieved when using knowledge-based approaches within more specialized fields, such as medical free texts or legal documents. However, the problem is that sources of domain knowledge are time consuming to build and equally costly to maintain. As a...
With the advent of web 2.0, billions of videos are now freely available online. Meanwhile, rich user generated information for these videos such as tags and online encyclopedia offer us a chance to enhance the existing video analysis technologies. In this paper, we propose a mash-up framework to realize video category recommendation by leveraging web information from different sources. Under this...
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