再论日语述论元结构分析的局部模型设计问题
Revisiting the Design Issues of Local Models for Japanese Predicate-Argument Structure Analysis
摘要：
日语述论元结构分析的研究趋势正在从具有局部特征的逐点预测模型转向旨在搜索全局最优解的全局模型。然而，现有的全球模型往往只采用相对简单的本地功能，因此，整体性能的提高是相当有限的。设计一个本地模型的重要性在这项研究中证明了一个复杂的本地模型的性能可以大大提高与最近的特征嵌入方法和基于神经网络的特征组合学习，优于国家的最先进的全球模型在$F_1$上一个共同的基准数据集。
英文摘要：
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 $F_1$ on a common benchmark dataset.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
