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        <title>Learning Interpretable Negation Rules via Weak Supervision at Document Level: A Reinforcement Learning Approach</title>
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    <name type="personal">
        <namePart type="given">Nicolas</namePart>
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        <namePart type="given">Stefan</namePart>
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        <dateIssued>2019-jun</dateIssued>
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    <abstract>Negation scope detection is widely performed as a supervised learning task which relies upon negation labels at word level. This suffers from two key drawbacks: (1) such granular annotations are costly and (2) highly subjective, since, due to the absence of explicit linguistic resolution rules, human annotators often disagree in the perceived negation scopes. To the best of our knowledge, our work presents the first approach that eliminates the need for world-level negation labels, replacing it instead with document-level sentiment annotations. For this, we present a novel strategy for learning fully interpretable negation rules via weak supervision: we apply reinforcement learning to find a policy that reconstructs negation rules from sentiment predictions at document level. Our experiments demonstrate that our approach for weak supervision can effectively learn negation rules. Furthermore, an out-of-sample evaluation via sentiment analysis reveals consistent improvements (of up to 4.66%) over both a sentiment analysis with (i) no negation handling and (ii) the use of word-level annotations from humans. Moreover, the inferred negation rules are fully interpretable.</abstract>
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    <identifier type="doi">10.18653/v1/N19-1038</identifier>
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%0 Conference Proceedings
%T Learning Interpretable Negation Rules via Weak Supervision at Document Level: A Reinforcement Learning Approach
%A Pröllochs, Nicolas
%A Feuerriegel, Stefan
%A Neumann, Dirk
%S Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)
%D 2019
%8 jun
%I Association for Computational Linguistics
%C Minneapolis, Minnesota
%F prollochs-etal-2019-learning
%X Negation scope detection is widely performed as a supervised learning task which relies upon negation labels at word level. This suffers from two key drawbacks: (1) such granular annotations are costly and (2) highly subjective, since, due to the absence of explicit linguistic resolution rules, human annotators often disagree in the perceived negation scopes. To the best of our knowledge, our work presents the first approach that eliminates the need for world-level negation labels, replacing it instead with document-level sentiment annotations. For this, we present a novel strategy for learning fully interpretable negation rules via weak supervision: we apply reinforcement learning to find a policy that reconstructs negation rules from sentiment predictions at document level. Our experiments demonstrate that our approach for weak supervision can effectively learn negation rules. Furthermore, an out-of-sample evaluation via sentiment analysis reveals consistent improvements (of up to 4.66%) over both a sentiment analysis with (i) no negation handling and (ii) the use of word-level annotations from humans. Moreover, the inferred negation rules are fully interpretable.
%R 10.18653/v1/N19-1038
%U https://aclanthology.org/N19-1038
%U https://doi.org/10.18653/v1/N19-1038
%P 407-413

Markdown (Informal)

[Learning Interpretable Negation Rules via Weak Supervision at Document Level: A Reinforcement Learning Approach](https://aclanthology.org/N19-1038) (Pröllochs et al., NAACL 2019)

• Learning Interpretable Negation Rules via Weak Supervision at Document Level: A Reinforcement Learning Approach (Pröllochs et al., NAACL 2019)

ACL

• Nicolas Pröllochs, Stefan Feuerriegel, and Dirk Neumann. 2019. Learning Interpretable Negation Rules via Weak Supervision at Document Level: A Reinforcement Learning Approach. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pages 407–413, Minneapolis, Minnesota. Association for Computational Linguistics.