一句话：提出基于学习的领域术语表自动构建方法，从代码标识符与文档中抽取概念、别名和关系。
针对现有基于语言启发式或词频统计从软件文档识别领域术语准确率低的问题，本文提出一种基于学习的领域术语表自动构建方法。该方法从代码标识符和自然语言概念定义中抽取高质量种子术语，训练领域特定预测模型，依据提及领域概念的句子的词汇与语义上下文识别术语；随后合并同一概念的别名到规范名，为每个概念选择解释句，并识别概念间的 is-a、has-a 和 related-to 关系。在深度学习和 Hadoop 领域应用，分别获得 5,382 和 2,069 个概念及 16,962 和 6,815 条关系。评估验证了提取术语表的准确性，及其对不同项目不同文档间知识融合与获取的有效性。
A domain glossary that organizes domain-specific concepts and their aliases and relations is essential for knowledge acquisition and software development. Existing approaches use linguistic heuristics or term-frequency-based statistics to identify domain specific terms from software documentation, and thus the accuracy is often low. In this paper, we propose a learning-based approach for automatic construction of domain glossary from source code and software documentation. The approach uses a set of high-quality seed terms identified from code identifiers and natural language concept definitions to train a domain-specific prediction model to recognize glossary terms based on the lexical and semantic context of the sentences mentioning domain-specific concepts. It then merges the aliases of the same concepts to their canonical names, selects a set of explanation sentences for each concept, and identifies "is a", "has a", and "related to" relations between the concepts. We apply our approach to deep learning domain and Hadoop domain and harvest 5,382 and 2,069 concepts together with 16,962 and 6,815 relations respectively. Our evaluation validates the accuracy of the extracted domain glossary and its usefulness for the fusion and acquisition of knowledge from different documents of different projects.
DOI 原文 ·
@inproceedings{paperbot5109,
title = {A learning-based approach for automatic construction of domain glossary from source code and documentation},
author = {Chong Wang and Xin Peng and Mingwei Liu and Zhenchang Xing and Xuefang Bai and Bing Xie and Tuo Wang},
booktitle = {FSE},
year = {2019},
doi = {10.1145/3338906.3338963}
}