@inproceedings{dasgupta-etal-2018-automatic-extraction,
title = "Automatic Extraction of Causal Relations from Text using Linguistically Informed Deep Neural Networks",
author = "Dasgupta, Tirthankar and
Saha, Rupsa and
Dey, Lipika and
Naskar, Abir",
editor = "Komatani, Kazunori and
Litman, Diane and
Yu, Kai and
Papangelis, Alex and
Cavedon, Lawrence and
Nakano, Mikio",
booktitle = "Proceedings of the 19th Annual {SIG}dial Meeting on Discourse and Dialogue",
month = jul,
year = "2018",
address = "Melbourne, Australia",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W18-5035",
doi = "10.18653/v1/W18-5035",
pages = "306--316",
abstract = "In this paper we have proposed a linguistically informed recursive neural network architecture for automatic extraction of cause-effect relations from text. These relations can be expressed in arbitrarily complex ways. The architecture uses word level embeddings and other linguistic features to detect causal events and their effects mentioned within a sentence. The extracted events and their relations are used to build a causal-graph after clustering and appropriate generalization, which is then used for predictive purposes. We have evaluated the performance of the proposed extraction model with respect to two baseline systems,one a rule-based classifier, and the other a conditional random field (CRF) based supervised model. We have also compared our results with related work reported in the past by other authors on SEMEVAL data set, and found that the proposed bi-directional LSTM model enhanced with an additional linguistic layer performs better. We have also worked extensively on creating new annotated datasets from publicly available data, which we are willing to share with the community.",
}
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%0 Conference Proceedings
%T Automatic Extraction of Causal Relations from Text using Linguistically Informed Deep Neural Networks
%A Dasgupta, Tirthankar
%A Saha, Rupsa
%A Dey, Lipika
%A Naskar, Abir
%Y Komatani, Kazunori
%Y Litman, Diane
%Y Yu, Kai
%Y Papangelis, Alex
%Y Cavedon, Lawrence
%Y Nakano, Mikio
%S Proceedings of the 19th Annual SIGdial Meeting on Discourse and Dialogue
%D 2018
%8 July
%I Association for Computational Linguistics
%C Melbourne, Australia
%F dasgupta-etal-2018-automatic-extraction
%X In this paper we have proposed a linguistically informed recursive neural network architecture for automatic extraction of cause-effect relations from text. These relations can be expressed in arbitrarily complex ways. The architecture uses word level embeddings and other linguistic features to detect causal events and their effects mentioned within a sentence. The extracted events and their relations are used to build a causal-graph after clustering and appropriate generalization, which is then used for predictive purposes. We have evaluated the performance of the proposed extraction model with respect to two baseline systems,one a rule-based classifier, and the other a conditional random field (CRF) based supervised model. We have also compared our results with related work reported in the past by other authors on SEMEVAL data set, and found that the proposed bi-directional LSTM model enhanced with an additional linguistic layer performs better. We have also worked extensively on creating new annotated datasets from publicly available data, which we are willing to share with the community.
%R 10.18653/v1/W18-5035
%U https://aclanthology.org/W18-5035
%U https://doi.org/10.18653/v1/W18-5035
%P 306-316
Markdown (Informal)
[Automatic Extraction of Causal Relations from Text using Linguistically Informed Deep Neural Networks](https://aclanthology.org/W18-5035) (Dasgupta et al., SIGDIAL 2018)
ACL