  Despite the need for financial data on company activities in developing
countries for development research and economic analysis, such data does not
exist. In this project, we develop and evaluate two Natural Language Processing
(NLP) based techniques to address this issue. First, we curate a custom dataset
specific to the domain of financial text data on developing countries and
explore multiple approaches for information extraction. We then explore a
text-to-text approach with the transformer-based T5 model with the goal of
undertaking simultaneous NER and relation extraction. We find that this model
is able to learn the custom text structure output data corresponding to the
entities and their relations, resulting in an accuracy of 92.44\%, a precision
of 68.25\% and a recall of 54.20\% from our best T5 model on the combined task.
Secondly, we explore an approach with sequential NER and relation extration.
For the NER, we run pre-trained and fine-tuned models using SpaCy, and we
develop a custom relation extraction model using SpaCy's Dependency Parser
output and some heuristics to determine entity relationships \cite{spacy}. We
obtain an accuracy of 84.72\%, a precision of 6.06\% and a recall of 5.57\% on
this sequential task.
