These converted documents were utilized to filter and extract relevant information. Through API calls to the IBM Deep Search platform, paragraphs containing terms from the materials mechanics ontology were extracted. These extracted paragraphs were segmented into sentences. The annotation of this corpus was done identically to the procedure outlined in the Materi-oMiner publication . The plain text files of the extracted paragraphs along with the materials mechanics ontology were loaded into the INCEpTION annotation tool , where relevant entities therein were annotated by the curator of the test set (Materi-oMiner dataset) to ensure consistency. The same subset of 28 target classes (C = 28) of the ontology was used and annotated as foreground classes as previously applied in the MaterioMiner dataset. The output from INCEpTION was formatted in CoNLL 2002 NER format , which is a widely employed format for NER tasks that uses a BIO tagging scheme for the annotations. The BIO tagging uses the 'B-', 'I-' prefixes, and 'O', to indicate the beginning, inside, and outside of an entity, respectively.