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  • × author_ss:"Ren, P."
  1. Zhang, Y.; Ren, P.; Rijke, M. de: ¬A taxonomy, data set, and benchmark for detecting and classifying malevolent dialogue responses (2021) 0.01 
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    Abstract
    Conversational interfaces are increasingly popular as a way of connecting people to information. With the increased generative capacity of corpus-based conversational agents comes the need to classify and filter out malevolent responses that are inappropriate in terms of content and dialogue acts. Previous studies on the topic of detecting and classifying inappropriate content are mostly focused on a specific category of malevolence or on single sentences instead of an entire dialogue. We make three contributions to advance research on the malevolent dialogue response detection and classification (MDRDC) task. First, we define the task and present a hierarchical malevolent dialogue taxonomy. Second, we create a labeled multiturn dialogue data set and formulate the MDRDC task as a hierarchical classification task. Last, we apply state-of-the-art text classification methods to the MDRDC task, and report on experiments aimed at assessing the performance of these approaches.
    Source
    Journal of the Association for Information Science and Technology. 72(2021) no.12, S.1477-1497
    Type
    a
  2. Ren, P.; Chen, Z.; Ma, J.; Zhang, Z.; Si, L.; Wang, S.: Detecting temporal patterns of user queries (2017) 0.01 
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    Abstract
    Query classification is an important part of exploring the characteristics of web queries. Existing studies are mainly based on Broder's classification scheme and classify user queries into navigational, informational, and transactional categories according to users' information needs. In this article, we present a novel classification scheme from the perspective of queries' temporal patterns. Queries' temporal patterns are inherent time series patterns of the search volumes of queries that reflect the evolution of the popularity of a query over time. By analyzing the temporal patterns of queries, search engines can more deeply understand the users' search intents and thus improve performance. Furthermore, we extract three groups of features based on the queries' search volume time series and use a support vector machine (SVM) to automatically detect the temporal patterns of user queries. Extensive experiments on the Million Query Track data sets of the Text REtrieval Conference (TREC) demonstrate the effectiveness of our approach.
    Source
    Journal of the Association for Information Science and Technology. 68(2017) no.1, S.113-128
    Type
    a

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