用深度学习从建筑文本中自动提炼术语与上下位关系。
Deep Learning and Natural Language Processing in the Field of Construction
- 先用统计和n-gram提取建筑术语，再通过语言模式和网络查询优化。
- 结合多种词向量模型，准确识别术语间的上下位关系。
- 经专家评估验证，适合建筑知识图谱构建与智能检索。
本文提出一种完整的建筑领域超义关系抽取流程，包含两个主要步骤：术语提取与上下位关系检测。首先，通过语料库分析方法，利用统计和词n-gram分析从建筑技术规范文本中提取领域术语，并结合语言模式和网络查询进行筛选优化，提升术语质量。其次，采用基于多种词嵌入模型及组合的机器学习方法，实现从提取术语中识别上下位关系。术语提取结果由6名领域专家进行人工评估，上下位关系检测在多个数据集上进行了测试。整体方法表现良好，结果具有显著实用价值。
原文摘要 · Abstract (English)
This article presents a complete process to extract hypernym relationships in the field of construction using two main steps: terminology extraction and detection of hypernyms from these terms. We first describe the corpus analysis method to extract terminology from a collection of technical specifications in the field of construction. Using statistics and word n-grams analysis, we extract the domain's terminology and then perform pruning steps with linguistic patterns and internet queries to improve the quality of the final terminology. Second, we present a machine-learning approach based on various words embedding models and combinations to deal with the detection of hypernyms from the extracted terminology. Extracted terminology is evaluated using a manual evaluation carried out by 6 experts in the domain, and the hypernym identification method is evaluated with different datasets. The global approach provides relevant and promising results.
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