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.

本研究基于双向长短时记忆和条件随机场层构建阿姆哈拉语命名实体识别系统，并通过合成少数类过采样技术减轻分类问题，最终获得 93％的 F_1 得分，成为阿姆哈拉语命名实体识别的新的最优结果。