NEMO：SIGTYP 2020共享任务中约束语言类型特征预测的频率主义推理方法
NEMO: Frequentist Inference Approach to Constrained Linguistic Typology Feature Prediction in SIGTYP 2020 Shared Task
摘要：
本文介绍了NEMO提交给SIGTYP 2020的共享任务，该任务使用来自世界语言结构地图集（WALS）的数据来预测多种语言的语言类型学特征。我们采用频率论推理来表示类型特征之间的相关性，并使用这种表示来训练简单的多类估计器，预测单个特征。我们描述了两个提交的岭回归为基础的配置，排名第二和第三的整体约束任务。我们的最佳配置在149种测试语言上实现了0.66的微平均准确度得分。
英文摘要：
This paper describes the NEMO submission to SIGTYP 2020 shared task which deals with prediction of linguistic typological features for multiple languages using the data derived from World Atlas of Language Structures (WALS). We employ frequentist inference to represent correlations between typological features and use this representation to train simple multi-class estimators that predict individual features. We describe two submitted ridge regression-based configurations which ranked second and third overall in the constrained task. Our best configuration achieved the micro-averaged accuracy score of 0.66 on 149 test languages.
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。
