Named Entity Recognition for Sheko Language Using Bidirectional LSTM

Abstract

Objectives: This study aims to advance Sheko language name entity Recognition first of its kind. Named Entity Recognition (NER) is one of the most important text processing in machine translation, text summarization, and information retrieval. Sheko language named entity recognition concerns in addressing the usage of the bidirectional Long Short-Term Memory (LSTM) model in recognizing tokens into predefined classes. Methods: A bidirectional long shortterm memory is used to model the NER for sheko language to identify words into seven predefined classes: Person, Organization, Geography, Natural Phenomenon, Geopolitical Entity, time, and other classes. As feature selection plays a vital role in long short-term memory framework, the experiment is conducted to discover the most suitable features for Sheko NER tagging task by using 63,813 words to train and test our model. Out of which is 70% for training and 30% for testing. Datasets were collected from Sheko Mizan Aman Radio Station (MARS), Sheko southern region mass media, Language, and Literature Department. Findings: Through several conducted experiments, Sheko NER has successfully achieved a performance of 97% test accuracy. From the experimental result, it is possible to determine that tag context is a significant feature in named entity recognition and classification for Sheko language. Finally, we have contributed a new architecture for Sheko NER which uses automatically features for Sheko named entity recognition which is not dependent on other NLP tasks, and added some preprocessing steps. We provide a comprehensive Comparison with other traditional NER algorithms.