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.

翻译：尽管发展中国家公司活动的金融数据对于发展研究和经济分析至关重要，但此类数据目前尚不存在。在本项目中，我们开发并评估了两种基于自然语言处理（NLP）的技术以解决该问题。首先，我们针对发展中国家金融文本数据领域构建了定制化数据集，并探索了多种信息抽取方法。随后，我们研究了基于Transformer的T5模型的文本到文本方法，旨在同时完成命名实体识别（NER）与关系抽取。研究发现，该模型能够学习输出对应实体及其关系的定制化文本结构数据，在联合任务中，我们的最优T5模型实现了92.44%的准确率、68.25%的精确率和54.20%的召回率。其次，我们探索了采用序列化NER与关系抽取的方法。针对NER任务，我们利用SpaCy运行了预训练与微调模型，并基于SpaCy的依存句法分析输出与启发式规则开发了自定义关系抽取模型以确定实体关系。在该序列任务中，我们获得了84.72%的准确率、6.06%的精确率和5.57%的召回率。