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        <title>Out-of-domain FrameNet Semantic Role Labeling</title>
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        <namePart type="given">Silvana</namePart>
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    <name type="personal">
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    <name type="personal">
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            <title>Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers</title>
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            <namePart type="given">Mirella</namePart>
            <namePart type="family">Lapata</namePart>
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            <namePart type="given">Phil</namePart>
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            <namePart type="given">Alexander</namePart>
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    <abstract>Domain dependence of NLP systems is one of the major obstacles to their application in large-scale text analysis, also restricting the applicability of FrameNet semantic role labeling (SRL) systems. Yet, current FrameNet SRL systems are still only evaluated on a single in-domain test set. For the first time, we study the domain dependence of FrameNet SRL on a wide range of benchmark sets. We create a novel test set for FrameNet SRL based on user-generated web text and find that the major bottleneck for out-of-domain FrameNet SRL is the frame identification step. To address this problem, we develop a simple, yet efficient system based on distributed word representations. Our system closely approaches the state-of-the-art in-domain while outperforming the best available frame identification system out-of-domain. We publish our system and test data for research purposes.</abstract>
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        <date>2017-04</date>
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%0 Conference Proceedings
%T Out-of-domain FrameNet Semantic Role Labeling
%A Hartmann, Silvana
%A Kuznetsov, Ilia
%A Martin, Teresa
%A Gurevych, Iryna
%Y Lapata, Mirella
%Y Blunsom, Phil
%Y Koller, Alexander
%S Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers
%D 2017
%8 April
%I Association for Computational Linguistics
%C Valencia, Spain
%F hartmann-etal-2017-domain
%X Domain dependence of NLP systems is one of the major obstacles to their application in large-scale text analysis, also restricting the applicability of FrameNet semantic role labeling (SRL) systems. Yet, current FrameNet SRL systems are still only evaluated on a single in-domain test set. For the first time, we study the domain dependence of FrameNet SRL on a wide range of benchmark sets. We create a novel test set for FrameNet SRL based on user-generated web text and find that the major bottleneck for out-of-domain FrameNet SRL is the frame identification step. To address this problem, we develop a simple, yet efficient system based on distributed word representations. Our system closely approaches the state-of-the-art in-domain while outperforming the best available frame identification system out-of-domain. We publish our system and test data for research purposes.
%U https://aclanthology.org/E17-1045
%P 471-482

Markdown (Informal)

[Out-of-domain FrameNet Semantic Role Labeling](https://aclanthology.org/E17-1045) (Hartmann et al., EACL 2017)

• Out-of-domain FrameNet Semantic Role Labeling (Hartmann et al., EACL 2017)

ACL

• Silvana Hartmann, Ilia Kuznetsov, Teresa Martin, and Iryna Gurevych. 2017. Out-of-domain FrameNet Semantic Role Labeling. In Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers, pages 471–482, Valencia, Spain. Association for Computational Linguistics.