@inproceedings{guillou-etal-2020-incorporating,
title = "Incorporating Temporal Information in Entailment Graph Mining",
author = "Guillou, Liane and
Bijl de Vroe, Sander and
Hosseini, Mohammad Javad and
Johnson, Mark and
Steedman, Mark",
editor = "Ustalov, Dmitry and
Somasundaran, Swapna and
Panchenko, Alexander and
Malliaros, Fragkiskos D. and
Hulpu{\textcommabelow{s}}, Ioana and
Jansen, Peter and
Jana, Abhik",
booktitle = "Proceedings of the Graph-based Methods for Natural Language Processing (TextGraphs)",
month = dec,
year = "2020",
address = "Barcelona, Spain (Online)",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.textgraphs-1.7",
doi = "10.18653/v1/2020.textgraphs-1.7",
pages = "60--71",
abstract = "We present a novel method for injecting temporality into entailment graphs to address the problem of spurious entailments, which may arise from similar but temporally distinct events involving the same pair of entities. We focus on the sports domain in which the same pairs of teams play on different occasions, with different outcomes. We present an unsupervised model that aims to learn entailments such as win/lose → play, while avoiding the pitfall of learning non-entailments such as win ̸→ lose. We evaluate our model on a manually constructed dataset, showing that incorporating time intervals and applying a temporal window around them, are effective strategies.",
}
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%0 Conference Proceedings
%T Incorporating Temporal Information in Entailment Graph Mining
%A Guillou, Liane
%A Bijl de Vroe, Sander
%A Hosseini, Mohammad Javad
%A Johnson, Mark
%A Steedman, Mark
%Y Ustalov, Dmitry
%Y Somasundaran, Swapna
%Y Panchenko, Alexander
%Y Malliaros, Fragkiskos D.
%Y Hulpu\textcommabelows, Ioana
%Y Jansen, Peter
%Y Jana, Abhik
%S Proceedings of the Graph-based Methods for Natural Language Processing (TextGraphs)
%D 2020
%8 December
%I Association for Computational Linguistics
%C Barcelona, Spain (Online)
%F guillou-etal-2020-incorporating
%X We present a novel method for injecting temporality into entailment graphs to address the problem of spurious entailments, which may arise from similar but temporally distinct events involving the same pair of entities. We focus on the sports domain in which the same pairs of teams play on different occasions, with different outcomes. We present an unsupervised model that aims to learn entailments such as win/lose → play, while avoiding the pitfall of learning non-entailments such as win ̸→ lose. We evaluate our model on a manually constructed dataset, showing that incorporating time intervals and applying a temporal window around them, are effective strategies.
%R 10.18653/v1/2020.textgraphs-1.7
%U https://aclanthology.org/2020.textgraphs-1.7
%U https://doi.org/10.18653/v1/2020.textgraphs-1.7
%P 60-71
Markdown (Informal)
[Incorporating Temporal Information in Entailment Graph Mining](https://aclanthology.org/2020.textgraphs-1.7) (Guillou et al., TextGraphs 2020)
ACL
- Liane Guillou, Sander Bijl de Vroe, Mohammad Javad Hosseini, Mark Johnson, and Mark Steedman. 2020. Incorporating Temporal Information in Entailment Graph Mining. In Proceedings of the Graph-based Methods for Natural Language Processing (TextGraphs), pages 60–71, Barcelona, Spain (Online). Association for Computational Linguistics.