论文标题
Nemo：频繁的推理方法，用于约束语言类型学特征预测2020年共享任务
NEMO: Frequentist Inference Approach to Constrained Linguistic Typology Feature Prediction in SIGTYP 2020 Shared Task
论文作者
论文摘要
本文介绍了对SigTyp 2020共享任务的NEMO提交，该任务涉及使用来自语言结构（WALS）世界地图集的数据来预测多种语言的语言类型学特征。我们采用频繁的推断来表示类型学特征之间的相关性，并使用此表示形式来训练简单的多级估计器来预测单个特征。我们描述了两个提交的基于脊回归的配置，这些配置在受约束任务中排名第二和第三。我们最好的配置达到了149种测试语言的微平均精度得分为0.66。
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.
