ANEC: An Amharic Named Entity Corpus and Transformer Based Recognizer
This work addresses the problem of named entity recognition for Amharic speakers and NLP applications, but it is incremental as it applies existing methods to a new dataset.
The authors tackled the challenge of named entity recognition for Amharic, a Semitic language with heavy inflection, by creating a new dataset of 8,070 sentences and developing a system using bidirectional LSTM with CRF, achieving a state-of-the-art F1 score of 93%.
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.