Romanian Part of Speech Tagging using LSTM Networks
Beáta Lőrincz, Maria Nuțu, Adriana Stan · 2019
In this paper we present LSTM based neural network architectures for determining the part of speech (POS) tags for Romanian words. LSTM networks combined with fully-connected output layers are used for predicting the root POS, and sequence-to-sequence models composed of LSTM encoders and decoders are evaluated for predicting the extended MSD and CTAG tags. The highest accuracy achieved for the root POS is 99.18% and for the extended tags is 98.25%. This method proves to be efficient for the proposed task and has the advantage of being language independent, as no expert linguistic knowledge is used in the input features.