## 8 Discussion
Moreover, LaBSE benefits from the sheer volume of these bilingual sentences and sentences in related languages within groups such as Germanic, Romanic, Slavic, etc. There is always a risk that languages not covered by the list of 109 languages may not be well recognized. However, their similarity to other supported languages can still provide some benefits. The second part of the Section 8, focusing on the open-set text classification experiments and their outcomes, will enable us to address the second research question (described at the end of Section 2). We will accomplish this by dividing it into more detailed sub-questions:  
1. How accurately can the binary classifier distinguish between _known_ and _new_ topics? 2. What is the optimal feature type for the binary classifier, and why are other types less effective? 3. How does the performance of the open-class classifier vary across specific topics? The open-set text classification problem was addressed by training a binary classification model to distinguish _known_ topics from _new_ ones. The results, summarized in Table 3, reveal that the LaBSE vectorizer is the best option similar to closed-set classification. Despite this, we cannot depend on the previously trained closed-set classification model, as neither the softmax probabilities nor the outputs of the penultimate layer are adequate to detect the emergence of new topics. Moreover, the cosine similarities, which assess the similarity between the cluster centers of 18 known classes (averaged from all their training instance vectors) and the vectors of new texts, also fail to provide a reliable measure. Consequently, we explored the similarity values (see Table 5) between the cluster centers of these known classes, hoping that the instances of different classes would appear in distinct clusters without overlap. Contrary to expectations, these values range from 0.836 to 0.099, indicating the absence of a clear threshold that could effectively delineate the boundariesof known classes due to their overlap, thereby complicating the search for new ones in the entire vectorization space. These findings also clarify why memory-based approaches are unsuitable for our closed-set topic classification problem. The results presented in Table 4 demonstrate the accuracy of the open-class classifier in detecting new topics. The accuracy for all classes is above 90%, except for the NATO category, which reached 79.6%. A manual analysis of texts belonging to this topic revealed several issues: the training dataset for the NATO category includes various military and sometimes political actions, which can be easily mistaken for other topics such as Migration and TerroristAttack, especially where military involvement is aimed at stopping or controlling these situations. Similar to the multi-class closed-set topic classification experiments, the crosslingual LaBSE model in the binary open-set text classification method has again demonstrated its strength, this time as the vectorizer, thereby outperforming other feature types. This success is attributed to the crosslingual mechanisms, which enable the model to understand texts from different languages and discern the similarity of their content. In contrast, other researchers in similar studies have adopted completely different methodologies. The cosine similarity-based approach applied to word2 vec, as in [31], does not demonstrate promising results, mainly due to its less sophisticated and non-crosslingual vectorization model. Some authors, such as in [32], utilized the OpenMax approach; however, this method is more suitable when new topics appear infrequently and, thus, such softmax refinements are not constant. The Binary Relevance approach, as detailed in [33], is typically adjusted for multi-label classification problems. Training many binary models for each topic is an alternative solution, which would allow for the detection of a new topic if all binary models returned a negative class result.