# Text Alignment in the Service of Text Reuse Detection
## 6 Results
### Variant Sizes of Text Reuses
the top sub-figure (NW-W2V) and the middle sub-figure (NW-Fast) highlights an increase in recall, particularly in the upper-left quadrant, while the remainder of the spectrum remains relatively unchanged. This improvement is reflected in the more significant negative correlations. Implementing subsequent refinements, specifically the Hebrew enhancements, tempered the recall improvement to 83%, slightly reducing the negative correlation. When analyzing the optimal performance of the Needleman-Wunsch algorithm, it is evident that alignment quality declines more sharply as size disparities increase. Conversely, the Smith-Waterman algorithm exhibits a weak to moderate negative correlation across all distance function combinations. Moreover, the impact of substituting the word embedding model closely mirrored that observed in the NW algorithm, significantly increasing recall from 73% to 92%. However, unlike the NW algorithm, this effect was distributed across the entire spectrum in the SW algorithm, resulting in no change to the correlation, which remained weakly to moderately negative. In contrast to the NW algorithm, the Hebrew enhancements further increased recall and outperformed all other configurations. Text Witnesses vs. Text Reusers  
Owing to the composition of our ground truth dataset, the instances are not uniformly distributed across the spectrum of size differentials. The cases within our dataset can be divided into two distinct groups. The first group comprises 86% of the dataset and is characterized by negligible size disparities within the range of [0-80]. This cluster can be perceived as two copies of the exact text, with minor modifications interspersed. We designate this cluster as 'text witnesses'. Conversely, the secondary group comprises only 14% of the dataset and is characterized by substantial size differences between the aligned texts, ranging from 81 to 500. This suggests this cluster primarily consists of minor text reuse between the paired texts. We refer to this second cluster as 'text reuses'. Due to the substantial disparity in the number of cases between the two groups in our dataset and their distinct characteristics, we evaluated alignment quality separately for the best distance function in each group. The Spearman correlation for the 'text witnesses' group is presented in Table 3. The results indicate a slight difference in correlation between the two alignment methods, exhibiting moderate negative correlations. This suggests that the quality of all alignment methods slightly declines as size differences increase, maintaining the same proportion. This effect is more pronounced in the NW algorithm and is visually depicted in Figure 5. \begin{table}
\begin{tabular}{l c c c} \hline \hline
**Methods** & \(\mathbf{F_{1}}\) & **Recall** & **Precision** \\ \hline NW-W2V & \(-0.51\) & \(-0.48\) & \(-0.35\) \\ NW-Fast & \(-0.66\) & \(-0.59\) & \(-0.62\) \\ NW-Fast-Hebrew & \(-0.56\) & \(-0.51\) & \(-0.52\) \\ SW-W2V & \(-0.49\) & \(-0.49\) & \(-0.36\) \\ SW-Fast & \(-0.45\) & \(-0.44\) & \(-0.38\) \\ SW-Fast-Hebrew & \(-0.45\) & \(-0.44\) & \(-0.38\) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The Spearman correlation between text size differences and F1 score, precision, and recall for the entire dataset reveals distinct patterns across alignment methods. The Needleman–Wunsch algorithm exhibits a moderate to strong negative correlation with text size variations, whereas the Smith–Waterman algorithm demonstrates a weaker to moderate negative correlation. The 'text reuse' group exhibits a significant disparity between the NW and SW algorithms, as shown in Table 4. The NW-Fast combination demonstrates a strong negative correlation, indicating that alignment quality deteriorates as the length of text reuse decreases. Conversely, the SW-Fast-Hebrew combination exhibits a strong positive correlation, suggesting that shorter text reuse leads to improved alignment.