  Named Entity Recognition is an information extraction task that serves as a
preprocessing step for other natural language processing tasks, such as machine
translation, information retrieval, and question answering. Named entity
recognition enables the identification of proper names as well as temporal and
numeric expressions in an open domain text. For Semitic languages such as
Arabic, Amharic, and Hebrew, the named entity recognition task is more
challenging due to the heavily inflected structure of these languages. In this
paper, we present an Amharic named entity recognition system based on
bidirectional long short-term memory with a conditional random fields layer. We
annotate a new Amharic named entity recognition dataset (8,070 sentences, which
has 182,691 tokens) and apply Synthetic Minority Over-sampling Technique to our
dataset to mitigate the imbalanced classification problem. Our named entity
recognition system achieves an F_1 score of 93%, which is the new
state-of-the-art result for Amharic named entity recognition.
