@inproceedings{ray-etal-2018-ad3,
title = "{AD}3: Attentive Deep Document Dater",
author = "Ray, Swayambhu Nath and
Dasgupta, Shib Sankar and
Talukdar, Partha",
editor = "Riloff, Ellen and
Chiang, David and
Hockenmaier, Julia and
Tsujii, Jun'ichi",
booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
month = oct # "-" # nov,
year = "2018",
address = "Brussels, Belgium",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D18-1213/",
doi = "10.18653/v1/D18-1213",
pages = "1871--1880",
abstract = "Knowledge of the creation date of documents facilitates several tasks such as summarization, event extraction, temporally focused information extraction etc. Unfortunately, for most of the documents on the Web, the time-stamp metadata is either missing or can't be trusted. Thus, predicting creation time from document content itself is an important task. In this paper, we propose Attentive Deep Document Dater (AD3), an attention-based neural document dating system which utilizes both context and temporal information in documents in a flexible and principled manner. We perform extensive experimentation on multiple real-world datasets to demonstrate the effectiveness of AD3 over neural and non-neural baselines."
}
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<abstract>Knowledge of the creation date of documents facilitates several tasks such as summarization, event extraction, temporally focused information extraction etc. Unfortunately, for most of the documents on the Web, the time-stamp metadata is either missing or can’t be trusted. Thus, predicting creation time from document content itself is an important task. In this paper, we propose Attentive Deep Document Dater (AD3), an attention-based neural document dating system which utilizes both context and temporal information in documents in a flexible and principled manner. We perform extensive experimentation on multiple real-world datasets to demonstrate the effectiveness of AD3 over neural and non-neural baselines.</abstract>
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%0 Conference Proceedings
%T AD3: Attentive Deep Document Dater
%A Ray, Swayambhu Nath
%A Dasgupta, Shib Sankar
%A Talukdar, Partha
%Y Riloff, Ellen
%Y Chiang, David
%Y Hockenmaier, Julia
%Y Tsujii, Jun’ichi
%S Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
%D 2018
%8 oct nov
%I Association for Computational Linguistics
%C Brussels, Belgium
%F ray-etal-2018-ad3
%X Knowledge of the creation date of documents facilitates several tasks such as summarization, event extraction, temporally focused information extraction etc. Unfortunately, for most of the documents on the Web, the time-stamp metadata is either missing or can’t be trusted. Thus, predicting creation time from document content itself is an important task. In this paper, we propose Attentive Deep Document Dater (AD3), an attention-based neural document dating system which utilizes both context and temporal information in documents in a flexible and principled manner. We perform extensive experimentation on multiple real-world datasets to demonstrate the effectiveness of AD3 over neural and non-neural baselines.
%R 10.18653/v1/D18-1213
%U https://aclanthology.org/D18-1213/
%U https://doi.org/10.18653/v1/D18-1213
%P 1871-1880
Markdown (Informal)
[AD3: Attentive Deep Document Dater](https://aclanthology.org/D18-1213/) (Ray et al., EMNLP 2018)
ACL
- Swayambhu Nath Ray, Shib Sankar Dasgupta, and Partha Talukdar. 2018. AD3: Attentive Deep Document Dater. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 1871–1880, Brussels, Belgium. Association for Computational Linguistics.