NLP can be both context-agnostic and contextual . Contextual embedding aims at understanding the sequence-level semantics and thereby learns different vector representations for polysemous words or words that bear different meanings in different contexts. Contextual NLP can be used for tasks such as sequence classification , question answering , language modeling and translation . Context-aware algorithms such as ELMo , BERT and GPT-2 have shown state-of-the-art performance in several NLP tasks. Such tools differ from traditional word embeddings, where a single word can have different embedding based on the context it has been used. However, the concept of context varies from problem to problem and creating a pipeline that caters to a specific problem is still a necessary undertaking. NLP-based research in materials science domain mostly use context-agnostic NLP to manually annotate whole articles and build classification tools to extract relevant keywords . In this study, we utilize the context-aware algorithm BERT to extract information on different aspects of perovskites synthesis. BERT is a bidirectionally trained model and can learn the context of a word based on all the other surrounding words. This has further been elaborated in section 2.2.