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  • × author_ss:"Zhang, Z."
  1. Lin, M.; Zhang, Z.: Question-driven segmentation of lecture speech text : towards intelligent e-learning systems (2008) 0.00 
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    Abstract
    Recently, lecture videos have been widely used in e-learning systems. Envisioning intelligent e-learning systems, this article addresses the challenge of information seeking in lecture videos by retrieving relevant video segments based on user queries, through dynamic segmentation of lecture speech text. In the proposed approach, shallow parsing such as part of-speech tagging and noun phrase chunking are used to parse both questions and Automated Speech Recognition (ASR) transcripts. A sliding-window algorithm is proposed to identify the start and ending boundaries of returned segments. Phonetic and partial matching is utilized to correct the errors from automated speech recognition and noun phrase chunking. Furthermore, extra knowledge such as lecture slides is used to facilitate the ASR transcript error correction. The approach also makes use of proximity to approximate the deep parsing and structure match between question and sentences in ASR transcripts. The experimental results showed that both phonetic and partial matching improved the segmentation performance, slides-based ASR transcript correction improves information coverage, and proximity is also effective in improving the overall performance.
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  2. Ding, Y.; Jacob, E.K.; Zhang, Z.; Foo, S.; Yan, E.; George, N.L.; Guo, L.: Perspectives on social tagging (2009) 0.00 
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  3. Sarnikar, S.; Zhang, Z.; Zhao, J.L.: Query-performance prediction for effective query routing in domain-specific repositories (2014) 0.00 
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    Abstract
    The effective use of corporate memory is becoming increasingly important because every aspect of e-business requires access to information repositories. Unfortunately, less-than-satisfying effectiveness in state-of-the-art information-retrieval techniques is well known, even for some of the best search engines such as Google. In this study, the authors resolve this retrieval ineffectiveness problem by developing a new framework for predicting query performance, which is the first step toward better retrieval effectiveness. Specifically, they examine the relationship between query performance and query context. A query context consists of the query itself, the document collection, and the interaction between the two. The authors first analyze the characteristics of query context and develop various features for predicting query performance. Then, they propose a context-sensitive model for predicting query performance based on the characteristics of the query and the document collection. Finally, they validate this model with respect to five real-world collections of documents and demonstrate its utility in routing queries to the correct repository with high accuracy.
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  4. Zhang, Z.; Zhang, Z.; Law, R.: Editorial responsiveness, journal quality, and total review time : an empirical analysis (2012) 0.00 
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  5. Srihari, R.K.; Zhang, Z.: Exploiting multimodal context in image retrieval (1999) 0.00 
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  6. Li, J.; Zhang, Z.; Li, X.; Chen, H.: Kernel-based learning for biomedical relation extraction (2008) 0.00 
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  7. Ren, P.; Chen, Z.; Ma, J.; Zhang, Z.; Si, L.; Wang, S.: Detecting temporal patterns of user queries (2017) 0.00 
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  8. Suakkaphong, N.; Zhang, Z.; Chen, H.: Disease named entity recognition using semisupervised learning and conditional random fields (2011) 0.00 
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  9. Zhang, Z.; Li, Q.; Zeng, D.; Ga, H.: Extracting evolutionary communities in community question answering (2014) 0.00 
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  10. Zhang, Z.; Heuer, A.; Engel, T.; Meinel, C.: DAPHNE - a tool for distributed Web authoring and publishing (1999) 0.00 
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