arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2003.11687cs.CLcs.LGstat.ML
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SEVA的公共知识概念识别

Common-Knowledge Concept Recognition for SEVA

Department of Computer Science, George Mason University(乔治梅森大学计算机科学系);Instrument Development Center, NASA Goddard Space Flight Center(美国国家航空航天局戈达德太空飞行中心仪器开发中心);Information Sciences & Technology Department, George Mason University(乔治梅森大学信息科学与技术系);George Mason University(乔治梅森大学)

Jitin Krishnan, Patrick Coronado, Hemant Purohit, Huzefa Rangwala

摘要:

我们建立了一个系统工程师的虚拟助手(SEVA),可用于下游任务,如关系抽取,知识图谱建设,和问答的公共知识概念识别系统。该问题被制定为一个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签方案来训练序列模型来识别系统工程概念,从而构建了一个在单词级注释的数据集。我们使用预先训练的语言模型,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必要的数据集。最后,我们使用这些提取的概念以及一些下义关系构建了一个简单的知识图谱。

英文摘要:

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.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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