## 2 Related Work
Additionally, their applicability is limited by language support, necessitating machine translation into languages that zero-shot models support, which complicates the pipeline. The paper [31] tests several approaches. One of them is a straightforward approach using word2 vec to vectorize text documents and calculate cosine similarities between the mean of all document vectors and a test example. However, due to the similarities often being too close or even overlapping, it was concluded that cosine similarity at the document level is unsuitable for open-set classification. Another tested approach involved using CNNs to extract useful features from the data. Various CNN architectures were experimented with, modifying the softmax layer to include an unseen class by replacing it with an OpenMax layer. This layer uses a learned distance metric to account for the open-set risk, enhancing the model's ability to handle unknown classes. The modified model employs an ensemble approach to decision-making using activations in the penultimate layer and is incremental in nature, meaning it does not require retraining to accommodate new unknown classes. In general, the OpenMax method [32] is a very popular approach that enhances traditional neural network classifiers for open-set recognition by adjusting the final softmax layer. This adjustment estimates the likelihood of an input being from an unknown class by modeling the tail of the activation distributions for each known class and recalibrating the softmax scores. This provides a measure of uncertainty that effectively helps to reject unknown inputs. One more possible approach to deal with the open-set problems is Binary Relevance, which treats each label as independent as in [33] with CNN, classifying each as relevant or irrelevant through a binary classifier trained specifically for that label. This approach uses multiple binary models, each trained with a sigmoid function threshold of 0.5, to decide label relevance. However, Binary Relevance is more appropriate for multi-label classification scenarios, not open-class classification problems. Our comprehensive review of existing approaches highlights a significant gap in current technologies and methodologies. Despite the various systems and models available, none fully meet the complex requirements of our media monitoring system. Our analysis reveals that, while many systems provide solutions tailored to specific linguistic or topical scopes, none adequately address the simultaneous processing of a diverse range of languages and the emergence of unseen topics. Moreover, existing research rarely addresses three critical aspects: (1) comparing a broad spectrum of analytical techniques--classification, memory-based, and generative methods--within a single study; (2) exploring these techniques under both multilingual scenarios (training and testing on many languages) and crosslingual scenarios (training on one language, e.g., English in our case, and testing on any language in the dataset); (3) tackling both closed-set classification problems (where the training and testing are on a predefined number of classes) and open-set classification problems (where new classes can emerge). In response to these deficiencies, our research contributes to the field of media monitoring in several key ways:  
* Comparative analysis across techniques. Our study rigorously compares various vectorization and topic classification methods, including trainable/fine-tunable, memory-based, and generative approaches. * Comparative analysis across multilingual and crosslingual scenarios. * Addressing closed-set vs. open-set topic classification problems. * Incorporating additional clustering mechanisms. Besides identifying the emergence of a new topic, we introduce an additional clustering mechanism to differentiate between multiple new topics and accurately label them. * Integration into our media monitoring systems.