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In this study, we focus on extracting knowledgeable snippets and annotatingknowledgeable documents from Web corpus, consisting of the documents fromsocial media and We-media. Informally, knowledgeable snippets refer to the textdescribing concepts, properties of entities, or relations among entities, whileknowledgeable documents are the ones with enough knowledgeable snippets. Theseknowledgeable snippets and documents could be helpful in multiple applications,such as knowledge base construction and knowledge-oriented service. Previousstudies extracted the knowledgeable snippets using the pattern-based method.Here, we propose the semantic-based method for this task. Specifically, a CNNbased model is developed to extract knowledgeable snippets and annotateknowledgeable documents simultaneously. Additionally, a "low-level sharing,high-level splitting" structure of CNN is designed to handle the documents fromdifferent content domains. Compared with building multiple domain-specificCNNs, this joint model not only critically saves the training time, but alsoimproves the prediction accuracy visibly. The superiority of the proposedmethod is demonstrated in a real dataset from Wechat public platform.

Hierarchical Neural Network for Extracting Knowledgeable Snippets and Documents