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Dating Documents using Graph Convolution Networks

Conference Paper Slides Poster

Source code and dataset for ACL 2018 paper: Document Dating using Graph Convolution Networks.

Overview of NeuralDater (proposed method). NeuralDater exploits syntactic and temporal structure in a document to learn effective representation, which in turn are used to predict the document time. NeuralDater uses a Bi-directional LSTM (Bi-LSTM), two Graph Convolution Networks (GCN) – one over the dependency tree and the other over the document’s temporal graph – along with a softmax classifier, all trained end-to-end jointly. Please refer paper for more details.

Dependencies

Dataset:

Preprocessing:

For getting temporal graph of new documents. The following steps need to be followed:

Usage:

Citing:

Please cite the following paper if you use this code in your work.

@InProceedings{neuraldater2018,
  author = "Vashishth, Shikhar and Dasgupta, Shib Sankar and Ray, Swayambhu Nath and Talukdar, Partha",
  title = "Dating Documents using Graph Convolution Networks",
  booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
  year = "2018",
  publisher = "Association for Computational Linguistics",
  pages = "1605--1615",
  location = "Melbourne, Australia",
  url = "http://aclweb.org/anthology/P18-1149"
}