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        <title>NewsReader at SemEval-2018 Task 5: Counting events by reasoning over event-centric-knowledge-graphs</title>
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            <namePart type="given">Saif</namePart>
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            <namePart type="given">Ekaterina</namePart>
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    <abstract>In this paper, we describe the participation of the NewsReader system in the SemEval-2018 Task 5 on Counting Events and Participants in the Long Tail. NewsReader is a generic unsupervised text processing system that detects events with participants, time and place to generate Event Centric Knowledge Graphs (ECKGs). We minimally adapted these ECKGs to establish a baseline performance for the task. We first use the ECKGs to establish which documents report on the same incident and what event mentions are coreferential. Next, we aggregate ECKGs across coreferential mentions and use the aggregated knowledge to answer the questions of the task. Our participation tests the quality of NewsReader to create ECKGs, as well as the potential of ECKGs to establish event identity and reason over the result to answer the task queries.</abstract>
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    <identifier type="doi">10.18653/v1/S18-1108</identifier>
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%0 Conference Proceedings
%T NewsReader at SemEval-2018 Task 5: Counting events by reasoning over event-centric-knowledge-graphs
%A Vossen, Piek
%Y Apidianaki, Marianna
%Y Mohammad, Saif M.
%Y May, Jonathan
%Y Shutova, Ekaterina
%Y Bethard, Steven
%Y Carpuat, Marine
%S Proceedings of the 12th International Workshop on Semantic Evaluation
%D 2018
%8 June
%I Association for Computational Linguistics
%C New Orleans, Louisiana
%F vossen-2018-newsreader
%X In this paper, we describe the participation of the NewsReader system in the SemEval-2018 Task 5 on Counting Events and Participants in the Long Tail. NewsReader is a generic unsupervised text processing system that detects events with participants, time and place to generate Event Centric Knowledge Graphs (ECKGs). We minimally adapted these ECKGs to establish a baseline performance for the task. We first use the ECKGs to establish which documents report on the same incident and what event mentions are coreferential. Next, we aggregate ECKGs across coreferential mentions and use the aggregated knowledge to answer the questions of the task. Our participation tests the quality of NewsReader to create ECKGs, as well as the potential of ECKGs to establish event identity and reason over the result to answer the task queries.
%R 10.18653/v1/S18-1108
%U https://aclanthology.org/S18-1108
%U https://doi.org/10.18653/v1/S18-1108
%P 660-666

Markdown (Informal)

[NewsReader at SemEval-2018 Task 5: Counting events by reasoning over event-centric-knowledge-graphs](https://aclanthology.org/S18-1108) (Vossen, SemEval 2018)

• NewsReader at SemEval-2018 Task 5: Counting events by reasoning over event-centric-knowledge-graphs (Vossen, SemEval 2018)

ACL

• Piek Vossen. 2018. NewsReader at SemEval-2018 Task 5: Counting events by reasoning over event-centric-knowledge-graphs. In Proceedings of the 12th International Workshop on Semantic Evaluation, pages 660–666, New Orleans, Louisiana. Association for Computational Linguistics.