Timestamping Documents and Beliefs

• URL: http://arxiv.org/abs/2106.14622v1
• Date: Wed, 9 Jun 2021 02:12:18 GMT
• Title: Timestamping Documents and Beliefs
• Authors: Swayambhu Nath Ray
• Abstract summary: Document dating is a challenging problem which requires inference over the temporal structure of the document. In this paper we propose NeuralDater, a Graph Convolutional Network (GCN) based document dating approach. We also propose AD3: Attentive Deep Document Dater, an attention-based document dating system.
• Score: 1.4467794332678539
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Most of the textual information available to us are temporally variable. In a world where information is dynamic, time-stamping them is a very important task. Documents are a good source of information and are used for many tasks like, sentiment analysis, classification of reviews etc. The knowledge of creation date of documents facilitates several tasks like summarization, event extraction, temporally focused information extraction etc. Unfortunately, for most of the documents on the web, the time-stamp meta-data is either erroneous or missing. Thus document dating is a challenging problem which requires inference over the temporal structure of the document alongside the contextual information 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. We also pointed out some limitations of NeuralDater and tried to utilize both context and temporal information in documents in a more flexible and intuitive manner proposing AD3: Attentive Deep Document Dater, an attention-based document dating system. To the best of our knowledge these are the first application of deep learning methods for the task. Through extensive experiments on real-world datasets, we find that our models significantly outperforms state-of-the-art baselines by a significant margin.

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