Deep Learning and Natural Language Processing in the Field of Construction

2025-01-14

AI Summary

To address the challenge of automatically identifying domain-specific technical terms and their hypernyms in construction technical specifications, this paper proposes an end-to-end hypernym relation extraction framework. First, it enhances domain term quality by integrating statistical analysis, n-gram mining, linguistic rules, and web-query–assisted term pruning. Second, it introduces a multi-source word embedding fusion strategy—combining Word2Vec, GloVe, and BERT—to jointly model term extraction and hypernym identification. This is the first work to achieve co-optimization of both tasks in the construction vertical domain, significantly improving semantic generalization capability. Human evaluation by six domain experts yields a term identification accuracy of 92.3% and a hypernym recognition F1-score of 86.7%, outperforming state-of-the-art baseline methods.

Technology Category

• Natural Language Processing: Information Extraction
• Machine Learning: Feature Construction/Reformulation
• Knowledge Representation and Reasoning: Diagnosis and Abductive Reasoning

Application Category

• Search and Retrieval-Augmented AI: Vertical and domain-specific search
• Semantics and Knowledge: Scalable techniques for the creation, curation, publication, maintenance, and consumption of large, Web-based, structured, reusable, knowledge graphs and ontologies
• Web Mining and Content Analysis: Bridging structured and unstructured data

Abstract

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.

Problem

• Computer Technology
• Natural Language Processing
• Architectural Vocabulary Recognition

Innovation

• Natural Language Processing
• Machine Learning
• Vocabulary Classification