  Requirements identification in textual documents or extraction is a tedious
and error prone task that many researchers suggest automating. We manually
annotated the PURE dataset and thus created a new one containing both
requirements and non-requirements. Using this dataset, we fine-tuned the BERT
model and compare the results with several baselines such as fastText and ELMo.
In order to evaluate the model on semantically more complex documents we
compare the PURE dataset results with experiments on Request For Information
(RFI) documents. The RFIs often include software requirements, but in a less
standardized way. The fine-tuned BERT showed promising results on PURE dataset
on the binary sentence classification task. Comparing with previous and recent
studies dealing with constrained inputs, our approach demonstrates high
performance in terms of precision and recall metrics, while being agnostic to
the unstructured textual input.
