# Joint Extraction of Multiple Relations and Entities from Building Code Clauses
## 4 Conclusions  
Chinese code clauses frequently contain complex semantic relations in terms of the multiple relations among named entities and the overlapping relation problem, leading to difficulty for the extraction of engineering constraints. Unfortunately, existing machine-learning based methods for regulation information extraction are inadequate to address the complexity of the semantic relations between entities. In this study, a joint extraction model was proposed for extracting multiple relations and entities from code clauses.  
In the proposed model, a hybrid deep learning algorithm combined with a decomposition strategy was applied. In this way, multiple relations, especially overlapping relations, can be extracted. Meanwhile, non-related pairs were excluded through the judicious recognition of the subject entities. An experiment was conducted to validate the performance of the proposed model, which achieved an average precision, recall, and F-measure of 0.8808, 0.8519, and 0.8661, respectively. The experimental results indicate that the proposed model is promising for extracting multiple relations and entities from building codes. With the proposed joint extraction model, the engineering constraints can be better extracted and then formally represented with the assistance of the ontological description of semantic relationships between named entities. In this way, the code clauses written in a natural language can be automatically processed and understood by computers.  
Although a collection of domain-specific entity and relation types was investigated, only the principle types of entities and relations appearing in Chinese building codes were considered as extraction targets. In the future, more entity/relation types will be considered to test the proposed method. Although the experimental results showed a favorable outcome, the proposed methodology was only tested on a relatively small size of datasets because a significant amount of time and human efforts would be required to create a larger dataset. Thus, addressing the dataset size problem is needed in the future to further verify and refine the performance of the presented approach. In addition, this research has mainly focused on the process of information extraction; the question of how to automatically convert the extracted triplets into logical inferences for automated code compliance verification should be further explored in future work.  
Conceptualization, F.L. and Y.S. ([PERSON]); methodology, F.L. and Y.S. ([PERSON]); software, F.L. and Y.S. ([PERSON]); validation, Y.S. ([PERSON]) and Y.S. ([PERSON]); formal analysis, F.L. and Y.S. ([PERSON]); investigation, F.L. and Y.S. ([PERSON]); resources, F.L. and Y.S. ([PERSON]); data curation, F.L. and Y.S. ([PERSON]); writing-original draft preparation, F.L. and Y.S. ([PERSON]); writing-review and editing, Y.S. ([PERSON]) and Y.S. ([PERSON]); visualization, F.L. and Y.S. ([PERSON]); supervision, Y.S. ([PERSON]) and Y.S. ([PERSON]); project administration, Y.S. ([PERSON]); funding acquisition, Y.S. ([PERSON]). All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (Grant No.71271137).  
The authors thank all of the reviewers for their comments on this article.  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **Precision** & **Recall** & **F-Measure** \\ \hline Whole Model & 0.8808 & 0.8519 & 0.8661 \\ -word-level embedding & 0.8535 & 0.8005 & 0.8261 \\ -character-level embedding & 0.8647 & 0.8094 & 0.8362 \\ -sentence-level features & 0.8692 & 0.8136 & 0.8404 \\ -start tagging features & 0.8582 & 0.8065 & 0.8315 \\ -joint learning & 0.8287 & 0.7772 & 0.8021 \\ \hline \hline \end{tabular}
\end{table}
Table 6: An ablation study on the test set.