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        <title>Improving Implicit Semantic Role Labeling by Predicting Semantic Frame Arguments</title>
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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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    <abstract>Implicit semantic role labeling (iSRL) is the task of predicting the semantic roles of a predicate that do not appear as explicit arguments, but rather regard common sense knowledge or are mentioned earlier in the discourse. We introduce an approach to iSRL based on a predictive recurrent neural semantic frame model (PRNSFM) that uses a large unannotated corpus to learn the probability of a sequence of semantic arguments given a predicate. We leverage the sequence probabilities predicted by the PRNSFM to estimate selectional preferences for predicates and their arguments. On the NomBank iSRL test set, our approach improves state-of-the-art performance on implicit semantic role labeling with less reliance than prior work on manually constructed language resources.</abstract>
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%0 Conference Proceedings
%T Improving Implicit Semantic Role Labeling by Predicting Semantic Frame Arguments
%A Do, Quynh Ngoc Thi
%A Bethard, Steven
%A Moens, Marie-Francine
%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 do-etal-2017-improving
%X Implicit semantic role labeling (iSRL) is the task of predicting the semantic roles of a predicate that do not appear as explicit arguments, but rather regard common sense knowledge or are mentioned earlier in the discourse. We introduce an approach to iSRL based on a predictive recurrent neural semantic frame model (PRNSFM) that uses a large unannotated corpus to learn the probability of a sequence of semantic arguments given a predicate. We leverage the sequence probabilities predicted by the PRNSFM to estimate selectional preferences for predicates and their arguments. On the NomBank iSRL test set, our approach improves state-of-the-art performance on implicit semantic role labeling with less reliance than prior work on manually constructed language resources.
%U https://aclanthology.org/I17-1010/
%P 90-99

Markdown (Informal)

[Improving Implicit Semantic Role Labeling by Predicting Semantic Frame Arguments](https://aclanthology.org/I17-1010/) (Do et al., IJCNLP 2017)

• Improving Implicit Semantic Role Labeling by Predicting Semantic Frame Arguments (Do et al., IJCNLP 2017)

ACL

• Quynh Ngoc Thi Do, Steven Bethard, and Marie-Francine Moens. 2017. Improving Implicit Semantic Role Labeling by Predicting Semantic Frame Arguments. In Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pages 90–99, Taipei, Taiwan. Asian Federation of Natural Language Processing.