NEMO: Frequentist Inference Approach to Constrained Linguistic Typology Feature Prediction in SIGTYP 2020 Shared Task
Year: 2020
Type: conference-paper
Abstract: This paper describes the NEMO submission to SIGTYP 2020 shared task (Bjerva et al., 2020) which deals with prediction of linguis tic typological features for multiple languages using the data derived from World Atlas of Language Structures (WALS).We employ fre quentist inference to represent correlations be tween typological features and use this repre sentation to train simple multiclass estimators that predict individual features.We describe two submitted ridge regressionbased configu rations which ranked second and third overall in the constrained task.Our best configuration achieved the microaveraged accuracy score of 0.66 on 149 test languages.
Authors: Alexander Gutkin, Richard Sproat
Institutions: Google (United States), Google (United Kingdom)
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
OpenAlex ID: W3092957054