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        <title>Revisiting the Design Issues of Local Models for Japanese Predicate-Argument Structure Analysis</title>
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        <namePart type="given">Yuichiroh</namePart>
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    <abstract>The research trend in Japanese predicate-argument structure (PAS) analysis is shifting from pointwise prediction models with local features to global models designed to search for globally optimal solutions. However, the existing global models tend to employ only relatively simple local features; therefore, the overall performance gains are rather limited. The importance of designing a local model is demonstrated in this study by showing that the performance of a sophisticated local model can be considerably improved with recent feature embedding methods and a feature combination learning based on a neural network, outperforming the state-of-the-art global models in F1 on a common benchmark dataset.</abstract>
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%0 Conference Proceedings
%T Revisiting the Design Issues of Local Models for Japanese Predicate-Argument Structure Analysis
%A Matsubayashi, Yuichiroh
%A Inui, Kentaro
%Y Kondrak, Greg
%Y Watanabe, Taro
%S Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers)
%D 2017
%8 November
%I Asian Federation of Natural Language Processing
%C Taipei, Taiwan
%F matsubayashi-inui-2017-revisiting
%X The research trend in Japanese predicate-argument structure (PAS) analysis is shifting from pointwise prediction models with local features to global models designed to search for globally optimal solutions. However, the existing global models tend to employ only relatively simple local features; therefore, the overall performance gains are rather limited. The importance of designing a local model is demonstrated in this study by showing that the performance of a sophisticated local model can be considerably improved with recent feature embedding methods and a feature combination learning based on a neural network, outperforming the state-of-the-art global models in F1 on a common benchmark dataset.
%U https://aclanthology.org/I17-2022
%P 128-133

Markdown (Informal)

[Revisiting the Design Issues of Local Models for Japanese Predicate-Argument Structure Analysis](https://aclanthology.org/I17-2022) (Matsubayashi & Inui, IJCNLP 2017)

• Revisiting the Design Issues of Local Models for Japanese Predicate-Argument Structure Analysis (Matsubayashi & Inui, IJCNLP 2017)

ACL

• Yuichiroh Matsubayashi and Kentaro Inui. 2017. Revisiting the Design Issues of Local Models for Japanese Predicate-Argument Structure Analysis. In Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers), pages 128–133, Taipei, Taiwan. Asian Federation of Natural Language Processing.