Common-Knowledge Concept Recognition for SEVA
This work addresses concept recognition for a Systems Engineer's Virtual Assistant, but it is incremental as it applies existing methods to a new domain-specific dataset.
The authors tackled the problem of recognizing common-knowledge concepts in systems engineering text by building a token classification system, which they used to construct a knowledge graph with hyponym relations.
We build a common-knowledge concept recognition system for a Systems Engineer's Virtual Assistant (SEVA) which can be used for downstream tasks such as relation extraction, knowledge graph construction, and question-answering. The problem is formulated as a token classification task similar to named entity extraction. With the help of a domain expert and text processing methods, we construct a dataset annotated at the word-level by carefully defining a labelling scheme to train a sequence model to recognize systems engineering concepts. We use a pre-trained language model and fine-tune it with the labeled dataset of concepts. In addition, we also create some essential datasets for information such as abbreviations and definitions from the systems engineering domain. Finally, we construct a simple knowledge graph using these extracted concepts along with some hyponym relations.