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Named entity recognition is an important task in natural language processing.It is very well studied for rich language, but still under explored forlow-resource languages. The main reason is that the existing techniquesrequired a lot of annotated data to reach good performance. Recently, a newdistributional representation of words has been proposed to project namedentities from a rich language to a low-resource one. This representation hasbeen coupled to a neural network in order to project named entities fromEnglish to Ewondo, a Bantu language spoken in Cameroon. Although the proposedmethod reached appreciable results, the size of the used neural network was toolarge compared to the size of the dataset. Furthermore the impact of the modelparameters has not been studied. In this paper, we show experimentally that thesame results can be obtained using a smaller neural network. We also emphasizethe parameters that are highly correlated to the network performance. This workis a step forward to build a reliable and robust network architecture for namedentity projection in low resource languages.

Neurals Networks for Projecting Named Entities from English to Ewondo