## 3 Results of the Bibliographic Literature Analysis Assisted by Artificial Intelligence Tools
### Text Clustering  
Following this, the articles were grouped based on the examination of their textual content using linguistic similarities, employing the K-means algorithm--a straightforward and widely recognized unsupervised clustering technique [23]. This algorithm computes the separation between data points (here, article abstracts) and assembles nearby points, suggesting their resemblance. A desirable cluster is characterized by shorter distances among its members compared to the distances between distinct clusters. Furthermore, identifying the most suitable number of clusters is crucial for gathering information.  
To achieve this, two approaches were utilized: the elbow technique and the average silhouette method. The elbow technique computes the within-cluster sum of squares (SSE: the total of the squared distances between each data point and its assigned cluster's center). Nevertheless, there's a point where increasing the number of clusters (K) doesn't substantially reduce the SSE, and the rate of reduction starts to diminish. This point is typically referred to as the elbow point (the inflection point on the graph) and is often used as a sign of the suitable number of clusters [24].  
The average silhouette score method calculates silhouette values that reflect how alike the language of the items is to their assigned topic cluster in comparison to other clusters. A greater average silhouette score across all articles suggests that the clustering effectively groups similar articles together [25]. The two methods employed enabled us to ascertain the optimal number of clusters (19 clusters; Figure 2), which represents the most fitting way to structure and categorize the articles according to their content.  
Figure 1: **(a)** Frequency bar chart of the 20 most common words, and (**b**) word cloud of the 75 most common words (the higher the size of the words, the higher their frequency), from the 1342 papers about _Lemma minor_.  
The K-means algorithm allows us to identify which abstract belongs to each cluster and so, to quantify the number of papers included in each of the 19 clusters (numbered from 0 to 18) obtained (Figure 3a). A cluster is stronger, the greater the number of papers it contains.  
The program allows us to visualize the relationship among the obtained clusters (19) in a 2D scatter plot using a Principal Component Analysis (PCA) (Figure 3b). In this scenario, PC1 represents the axis where the data points (including documents, words, and sentences) are most dispersed. This indicates that PC1 is the most significant feature for explaining the variability within the dataset. On the other hand, PC2 captures the variability in the data that PC1 does not, offering an additional dimension for distinguishing the data. As can be seen in this figure, there are clusters whose points show a high dispersion (i.e., clusters 2 or 8) from their centroid (black point) and others whose points are very close to it (i.e.,  
Figure 2: (a) Silhouette and (b) elbow graphs for the determination of the optimal cluster number from the _Lemma minor_ dataset.  
clusters 3, 15, or 16). In addition, some clusters are close to others (i.e., cluster 15 is close to cluster 16) but separated by a considerable distance in other cases (i.e., clusters 11 and 10 are separated by a long distance), indicating that each one includes different terms and information.  
An idea of the evolution of interest in each of the clusters over the last years (2013-2024) can be seen in Figure 4.