@inproceedings{taniguchi-etal-2018-joint,
title = "Joint Modeling for Query Expansion and Information Extraction with Reinforcement Learning",
author = "Taniguchi, Motoki and
Miura, Yasuhide and
Ohkuma, Tomoko",
editor = "Thorne, James and
Vlachos, Andreas and
Cocarascu, Oana and
Christodoulopoulos, Christos and
Mittal, Arpit",
booktitle = "Proceedings of the First Workshop on Fact Extraction and {VER}ification ({FEVER})",
month = nov,
year = "2018",
address = "Brussels, Belgium",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W18-5506",
doi = "10.18653/v1/W18-5506",
pages = "34--39",
abstract = "Information extraction about an event can be improved by incorporating external evidence. In this study, we propose a joint model for pseudo-relevance feedback based query expansion and information extraction with reinforcement learning. Our model generates an event-specific query to effectively retrieve documents relevant to the event. We demonstrate that our model is comparable or has better performance than the previous model in two publicly available datasets. Furthermore, we analyzed the influences of the retrieval effectiveness in our model on the extraction performance.",
}
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%0 Conference Proceedings
%T Joint Modeling for Query Expansion and Information Extraction with Reinforcement Learning
%A Taniguchi, Motoki
%A Miura, Yasuhide
%A Ohkuma, Tomoko
%Y Thorne, James
%Y Vlachos, Andreas
%Y Cocarascu, Oana
%Y Christodoulopoulos, Christos
%Y Mittal, Arpit
%S Proceedings of the First Workshop on Fact Extraction and VERification (FEVER)
%D 2018
%8 November
%I Association for Computational Linguistics
%C Brussels, Belgium
%F taniguchi-etal-2018-joint
%X Information extraction about an event can be improved by incorporating external evidence. In this study, we propose a joint model for pseudo-relevance feedback based query expansion and information extraction with reinforcement learning. Our model generates an event-specific query to effectively retrieve documents relevant to the event. We demonstrate that our model is comparable or has better performance than the previous model in two publicly available datasets. Furthermore, we analyzed the influences of the retrieval effectiveness in our model on the extraction performance.
%R 10.18653/v1/W18-5506
%U https://aclanthology.org/W18-5506
%U https://doi.org/10.18653/v1/W18-5506
%P 34-39
Markdown (Informal)
[Joint Modeling for Query Expansion and Information Extraction with Reinforcement Learning](https://aclanthology.org/W18-5506) (Taniguchi et al., EMNLP 2018)
ACL