One of the most important tasks in NLP is named entity recognition (NER), i.e., to understand the semantic role of a term within a sentence. It is the pivot of relation extraction, which aims to establish connections between entities in a sentence according to their semantic function in texts. This is the core task in materials science once it enables machines to recognize materials, reaction conditions, reagents, and products and reliably extract relations between them. NER tools can be classified into three categories: Dictionary-lookup, rule-based, and ML-based . The first consists of a dictionary with a set of terms to be exactly matched in texts. This method is accurate; however, it requires a hugely comprehensible dictionary to extract a significant amount of data. Rule-based approaches loosen some restrictions for capturing information by setting rules that need to be satisfied rather than strict terminology. They are less accurate than the former strategy, but they facilitate extractions once they only demand some hand-crafted conditions to be fulfilled. Finally, ML-based methods are the most cutting-edge algorithms employed in NLP pipelines. They can collect information from text without a dictionary of terms or rules but instead from vector semantics . This approach is faster and can extract much more data compared to the previous ones; however, they require large amounts of data to be trained and parameters to be adjusted, being more computationally expensive.