Dating Documents using Graph Convolution Networks
This addresses the challenge of dating arbitrary web documents for applications in information retrieval and summarization, representing a novel deep learning application in this domain.
The paper tackled the problem of document dating, which is essential for tasks like retrieval and event detection, by proposing NeuralDater, a Graph Convolutional Network approach that exploits syntactic and temporal structures, resulting in a 19% absolute (45% relative) accuracy improvement over state-of-the-art baselines.
Document date is essential for many important tasks, such as document retrieval, summarization, event detection, etc. While existing approaches for these tasks assume accurate knowledge of the document date, this is not always available, especially for arbitrary documents from the Web. Document Dating is a challenging problem which requires inference over the temporal structure of the document. Prior document dating systems have largely relied on handcrafted features while ignoring such document internal structures. In this paper, we propose NeuralDater, a Graph Convolutional Network (GCN) based document dating approach which jointly exploits syntactic and temporal graph structures of document in a principled way. To the best of our knowledge, this is the first application of deep learning for the problem of document dating. Through extensive experiments on real-world datasets, we find that NeuralDater significantly outperforms state-of-the-art baseline by 19% absolute (45% relative) accuracy points.