Implementing NER in nanotechnology involves several steps:
Data Collection: Collecting a large corpus of research papers, patents, and reports related to nanotechnology. Annotation: Manually annotating a subset of the corpus to train NER models. This involves tagging texts with relevant entities such as nanoparticles, quantum dots, and fullerenes. Model Training: Using annotated data to train machine learning models that can automatically recognize and classify entities in unstructured text. Integration: Incorporating the trained NER models into existing data processing pipelines to enhance research and development efforts.
