  Named entity recognition is an important task in natural language processing.
It is very well studied for rich language, but still under explored for
low-resource languages. The main reason is that the existing techniques
required a lot of annotated data to reach good performance. Recently, a new
distributional representation of words has been proposed to project named
entities from a rich language to a low-resource one. This representation has
been coupled to a neural network in order to project named entities from
English to Ewondo, a Bantu language spoken in Cameroon. Although the proposed
method reached appreciable results, the size of the used neural network was too
large compared to the size of the dataset. Furthermore the impact of the model
parameters has not been studied. In this paper, we show experimentally that the
same results can be obtained using a smaller neural network. We also emphasize
the parameters that are highly correlated to the network performance. This work
is a step forward to build a reliable and robust network architecture for named
entity projection in low resource languages.

Computation and Language;Machine Learning