当前位置:
X-MOL 学术
›
arXiv.cs.CL
›
论文详情
Our official English website, www.x-mol.net, welcomes your
feedback! (Note: you will need to create a separate account there.)
Common-Knowledge Concept Recognition for SEVA
arXiv - CS - Computation and Language Pub Date : 2020-03-26 , DOI: arxiv-2003.11687 Jitin Krishnan, Patrick Coronado, Hemant Purohit, and Huzefa Rangwala
arXiv - CS - Computation and Language Pub Date : 2020-03-26 , DOI: arxiv-2003.11687 Jitin Krishnan, Patrick Coronado, Hemant Purohit, and Huzefa Rangwala
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:
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,
更新日期:2020-03-27
中文翻译:
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,