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.

中文翻译：

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SEVA 的常识概念识别

我们为系统工程师的虚拟助手 (SEVA) 构建了一个常识概念识别系统，该系统可用于关系提取、知识图构建和问答等下游任务。该问题被表述为类似于命名实体提取的令牌分类任务。在领域专家和文本处理方法的帮助下，我们通过仔细定义标记方案来训练序列模型来识别系统工程概念，从而构建了一个词级注释的数据集。我们使用预训练的语言模型并使用标记的概念数据集对其进行微调。此外，我们还为系统工程领域的缩写和定义等信息创建了一些基本数据集。最后，