How Named Entity Recognition and Document Comprehension Unlock Geosciences and Engineering Semantic Search without Big Data

By combining Named Entity Recognition model trained on a tiny labeled dataset with a generalist Reading Comprehension engine, this abstract shows how to implement an efficient Semantic Search engine which can complete and sometime replace traditional keywords-based search engine. The proposed solution does not require massive amount of annotated data for training the models involved, taking advantage of transfer learning and model adaptation allowed by BERT and BiDAF model architectures. Because no Big Data is needed, such solution is very easy to implement at an early stage of any project related to Geosciences and Petroleum Engineering knowlegge management project.