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        <namePart type="given">Dongyeop</namePart>
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    <name type="personal">
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    <name type="personal">
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    <abstract>Explaining underlying causes or effects about events is a challenging but valuable task. We define a novel problem of generating explanations of a time series event by (1) searching cause and effect relationships of the time series with textual data and (2) constructing a connecting chain between them to generate an explanation. To detect causal features from text, we propose a novel method based on the Granger causality of time series between features extracted from text such as N-grams, topics, sentiments, and their composition. The generation of the sequence of causal entities requires a commonsense causative knowledge base with efficient reasoning. To ensure good interpretability and appropriate lexical usage we combine symbolic and neural representations, using a neural reasoning algorithm trained on commonsense causal tuples to predict the next cause step. Our quantitative and human analysis show empirical evidence that our method successfully extracts meaningful causality relationships between time series with textual features and generates appropriate explanation between them.</abstract>
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    <identifier type="doi">10.18653/v1/D17-1292</identifier>
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%0 Conference Proceedings
%T Detecting and Explaining Causes From Text For a Time Series Event
%A Kang, Dongyeop
%A Gangal, Varun
%A Lu, Ang
%A Chen, Zheng
%A Hovy, Eduard
%Y Palmer, Martha
%Y Hwa, Rebecca
%Y Riedel, Sebastian
%S Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing
%D 2017
%8 September
%I Association for Computational Linguistics
%C Copenhagen, Denmark
%F kang-etal-2017-detecting
%X Explaining underlying causes or effects about events is a challenging but valuable task. We define a novel problem of generating explanations of a time series event by (1) searching cause and effect relationships of the time series with textual data and (2) constructing a connecting chain between them to generate an explanation. To detect causal features from text, we propose a novel method based on the Granger causality of time series between features extracted from text such as N-grams, topics, sentiments, and their composition. The generation of the sequence of causal entities requires a commonsense causative knowledge base with efficient reasoning. To ensure good interpretability and appropriate lexical usage we combine symbolic and neural representations, using a neural reasoning algorithm trained on commonsense causal tuples to predict the next cause step. Our quantitative and human analysis show empirical evidence that our method successfully extracts meaningful causality relationships between time series with textual features and generates appropriate explanation between them.
%R 10.18653/v1/D17-1292
%U https://aclanthology.org/D17-1292
%U https://doi.org/10.18653/v1/D17-1292
%P 2758-2767

Markdown (Informal)

[Detecting and Explaining Causes From Text For a Time Series Event](https://aclanthology.org/D17-1292) (Kang et al., EMNLP 2017)

• Detecting and Explaining Causes From Text For a Time Series Event (Kang et al., EMNLP 2017)

ACL

• Dongyeop Kang, Varun Gangal, Ang Lu, Zheng Chen, and Eduard Hovy. 2017. Detecting and Explaining Causes From Text For a Time Series Event. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pages 2758–2767, Copenhagen, Denmark. Association for Computational Linguistics.