Classification of the Requirement Sentences of the US DOT Standard Specification Using Deep Learning Algorithms

Kahyun Jeon, Ghang Lee, H. David Jeong

Research output: Chapter in Book/Report/Conference proceedingChapter

5 Citations (Scopus)


This aim of this study is to classify requirement sentences from the specifications of US DOT using natural language processing (NLP) and a deep neural network. At the contract phase of the project, the requirements analysis of contract documents is a significant task to prevent claims or disputes caused by ambiguous or missing clauses, but it is highly human-intensive and difficult to identify requirements within a given short period. In this article, the requirement sentences identification model was proposed based on deep-learning algorithms. First, the critical terms that define what the requirement sentence is were identified, and then all sentences were labeled using the pre-defined critical terms. Second, three vectorizing methods were used, including two pre-trained methods—GloVe and Word2Vec—and a self-trained method to produce word embedding. Third, the automated classification of requirements sentences was experimented using three deep-learning models: the convolutional neural network (CNN), the long-short-term memory (LSTM), and the combination of CNN+LSTM. In the evaluation of nine total experiments, the results showed that the F1 scores of the CNN model were the highest at 92.9% and 92.4% for both the Word2Vec model and the Glove model. This study provided a way to achieve a high level of classification accuracy with simple deep-learning models and pre-trained embedding models.

Original languageEnglish
Title of host publicationLecture Notes in Civil Engineering
Number of pages9
Publication statusPublished - 2021

Publication series

NameLecture Notes in Civil Engineering
ISSN (Print)2366-2557
ISSN (Electronic)2366-2565

Bibliographical note

Publisher Copyright:
© 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG.

All Science Journal Classification (ASJC) codes

  • Civil and Structural Engineering


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