# Text Alignment in the Service of Text Reuse Detection
## 7 Conclusions  
In this study, we developed and evaluated a new text alignment approach to improve text reuse detection in Hebrew and Aramaic texts. By integrating the Smith-Waterman algorithm with a novel distance metric and utilizing fastText embeddings, we addressed key challenges, including word transpositions, orthographic variations, and multi-word alignments. Our approach demonstrated an 11% increase in the F1 score, highlighting substantial improvements over existing methods.  
Our findings emphasize the importance of adapting text alignment strategies to accommodate the linguistic and morphological complexities of ancient texts. This enhanced alignment capability deepens our understanding of text reuse and intertextual relationships in historical documents, contributing a valuable tool to digital humanities, particularly for languages with rich morphological structures.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Methods** & \(\mathbf{F_{1}}\) & **Recall** & **Precision** \\ \hline NW-Fast & \(-0.69\) & \(-0.61\) & \(-0.68\) \\ SW-Fast-Hebrew & \(+0.72\) & \(+0.59\) & \(+0.69\) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Spearman correlation between text size differences and F1 score, precision, and recall for the ‘text reuses’ group.  
Figure 6: Influence of size differences between paired texts on alignment quality for the ‘text reuses’ group. The top figure presents results using the Needleman–Wunsch algorithm with FastText-Hebrew, while the bottom figure shows results using the Smith–Waterman algorithm with FastText-Hebrew. The x-axis represents text size, and the y-axis indicates alignment scores.  
Beyond text reuse detection, our approach proposes a typological correction mechanism, which could be integrated into automatic transcription pipelines to enhance text normalization and improve the accuracy of machine-generated transcriptions. This is particularly relevant given the anticipated transcription of millions of ancient Hebrew manuscripts, reinforcing the necessity of digital humanities methodologies for large-scale textual analysis.  
However, our study has some limitations. The size of the ground truth dataset was limited, which may affect the generalizability of our findings. Additionally, varying size differences between aligned texts present challenges in consistently applying and evaluating the method's effectiveness. Further research using larger and more diverse datasets is needed to assess the scalability and broader applicability of our approach.  
Future work should focus on expanding this alignment method to other languages and text corpora, refining distance metrics to further improve accuracy, and integrating the method into digital manuscript transcription projects. By extending our approach beyond Hebrew and Aramaic, we aim to contribute to the next generation of computational tools for historical text analysis, bridging the gap between traditional philological methods and modern NLP techniques.  
Writing--original draft, H.M., T.K. and M.L. All authors have read and agreed to the published version of the manuscript.  
This study was partially supported by the Israel Ministry of Science and Technology (MOST), research grant No. 3-17516.  
Not applicable.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
The authors declare no conflicts of interest.