Search (4 results, page 1 of 1)

  • × year_i:[2010 TO 2020}
  • × author_ss:"Chen, H.-H."
  1. Lee, L.-H.; Chen, H.-H.: Mining search intents for collaborative cyberporn filtering (2012) 0.03 
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    Abstract
    This article presents a search-intent-based method to generate pornographic blacklists for collaborative cyberporn filtering. A novel porn-detection framework that can find newly appearing pornographic web pages by mining search query logs is proposed. First, suspected queries are identified along with their clicked URLs by an automatically constructed lexicon. Then, a candidate URL is determined if the number of clicks satisfies majority voting rules. Finally, a candidate whose URL contains at least one categorical keyword will be included in a blacklist. Several experiments are conducted on an MSN search porn dataset to demonstrate the effectiveness of our method. The resulting blacklist generated by our search-intent-based method achieves high precision (0.701) while maintaining a favorably low false-positive rate (0.086). The experiments of a real-life filtering simulation reveal that our proposed method with its accumulative update strategy can achieve 44.15% of a macro-averaging blocking rate, when the update frequency is set to 1 day. In addition, the overblocking rates are less than 9% with time change due to the strong advantages of our search-intent-based method. This user-behavior-oriented method can be easily applied to search engines for incorporating only implicit collective intelligence from query logs without other efforts. In practice, it is complementary to intelligent content analysis for keeping up with the changing trails of objectionable websites from users' perspectives.
  2. Huang, H.-H.; Wang, J.-J.; Chen, H.-H.: Implicit opinion analysis : extraction and polarity labelling (2017) 0.03 
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    Abstract
    Opinion words are crucial information for sentiment analysis. In some text, however, opinion words are absent or highly ambiguous. The resulting implicit opinions are more difficult to extract and label than explicit ones. In this paper, cutting-edge machine-learning approaches - deep neural network and word-embedding - are adopted for implicit opinion mining at the snippet and clause levels. Hotel reviews written in Chinese are collected and annotated as the experimental data set. Results show the convolutional neural network models not only outperform traditional support vector machine models, but also capture hidden knowledge within the raw text. The strength of word-embedding is also analyzed.
  3. Lee, L.-H.; Juan, Y.-C.; Tseng, W.-L.; Chen, H.-H.; Tseng, Y.-H.: Mining browsing behaviors for objectionable content filtering (2015) 0.02 
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  4. Tsai, M.-.F.; Chen, H.-H.; Wang, Y.-T.: Learning a merge model for multilingual information retrieval (2011) 0.02 
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    Content
    Beitrag in einem Themenschwerpunkt "Managing and Mining Multilingual Documents". Vgl.: 10.1016/j.ipm.2009.12.002.

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