Learning to Capitalize with Character-Level Recurrent Neural Networks: An Empirical Study

摘要

In this paper, we investigate case restoration for text without case information. Previous such work operates at the word level. We pro-pose an approach using character-level recurrent neural networks (RNN), which performs competitively compared to language modeling and conditional random fields (CRF) approaches. We further provide quantitative and qualitative analysis on how RNN helps improve truecasing.