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We build a common-knowledge concept recognition system for a SystemsEngineer's Virtual Assistant (SEVA) which can be used for downstream tasks suchas relation extraction, knowledge graph construction, and question-answering.The problem is formulated as a token classification task similar to namedentity extraction. With the help of a domain expert and text processingmethods, we construct a dataset annotated at the word-level by carefullydefining a labelling scheme to train a sequence model to recognize systemsengineering concepts. We use a pre-trained language model and fine-tune it withthe labeled dataset of concepts. In addition, we also create some essentialdatasets for information such as abbreviations and definitions from the systemsengineering domain. Finally, we construct a simple knowledge graph using theseextracted concepts along with some hyponym relations.

Common-Knowledge Concept Recognition for SEVA