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        <title>Event Argument Identification on Dependency Graphs with Bidirectional LSTMs</title>
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        <namePart type="given">Alex</namePart>
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            <title>Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)</title>
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            <namePart type="given">Greg</namePart>
            <namePart type="family">Kondrak</namePart>
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        <name type="personal">
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    <abstract>In this paper we investigate the performance of event argument identification. We show that the performance is tied to syntactic complexity. Based on this finding, we propose a novel and effective system for event argument identification. Recurrent Neural Networks learn to produce meaningful representations of long and short dependency paths. Convolutional Neural Networks learn to decompose the lexical context of argument candidates. They are combined into a simple system which outperforms a feature-based, state-of-the-art event argument identifier without any manual feature engineering.</abstract>
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%0 Conference Proceedings
%T Event Argument Identification on Dependency Graphs with Bidirectional LSTMs
%A Judea, Alex
%A Strube, Michael
%Y Kondrak, Greg
%Y Watanabe, Taro
%S Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)
%D 2017
%8 November
%I Asian Federation of Natural Language Processing
%C Taipei, Taiwan
%F judea-strube-2017-event
%X In this paper we investigate the performance of event argument identification. We show that the performance is tied to syntactic complexity. Based on this finding, we propose a novel and effective system for event argument identification. Recurrent Neural Networks learn to produce meaningful representations of long and short dependency paths. Convolutional Neural Networks learn to decompose the lexical context of argument candidates. They are combined into a simple system which outperforms a feature-based, state-of-the-art event argument identifier without any manual feature engineering.
%U https://aclanthology.org/I17-1083
%P 822-831

Markdown (Informal)

[Event Argument Identification on Dependency Graphs with Bidirectional LSTMs](https://aclanthology.org/I17-1083) (Judea & Strube, IJCNLP 2017)

• Event Argument Identification on Dependency Graphs with Bidirectional LSTMs (Judea & Strube, IJCNLP 2017)

ACL

• Alex Judea and Michael Strube. 2017. Event Argument Identification on Dependency Graphs with Bidirectional LSTMs. In Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pages 822–831, Taipei, Taiwan. Asian Federation of Natural Language Processing.