  In this study, we focus on extracting knowledgeable snippets and annotating
knowledgeable documents from Web corpus, consisting of the documents from
social media and We-media. Informally, knowledgeable snippets refer to the text
describing concepts, properties of entities, or relations among entities, while
knowledgeable documents are the ones with enough knowledgeable snippets. These
knowledgeable snippets and documents could be helpful in multiple applications,
such as knowledge base construction and knowledge-oriented service. Previous
studies extracted the knowledgeable snippets using the pattern-based method.
Here, we propose the semantic-based method for this task. Specifically, a CNN
based model is developed to extract knowledgeable snippets and annotate
knowledgeable documents simultaneously. Additionally, a "low-level sharing,
high-level splitting" structure of CNN is designed to handle the documents from
different content domains. Compared with building multiple domain-specific
CNNs, this joint model not only critically saves the training time, but also
improves the prediction accuracy visibly. The superiority of the proposed
method is demonstrated in a real dataset from Wechat public platform.
