The paper presents the results from the experiments with two large language models (LLMs) - T5 and Llama – for extracting events from a Bulgarian event corpus. The two models were pretrained by us on 35 Billion Token Bulgarian Corpus. The extraction was performed within the context of one sentence. Our approach aims at balancing the ACE-oriented approach that uses triggers in event detection, and the MUC-oriented one that uses more general event types. The evaluation relies on the IoU (Intersection over Union) of token spans and is twofold. The first one refers to the predicted event token span. Here if the span is correct, the semantic roles within the event are further checked. The second one refers to the triple of an event type, its semantic roles and participants. The results are promising. A qualitative evaluation is provided as well.

The paper presents an automatic annotation and visualization system for documents in the field of Social Sciences and Humanities. The annotation is on two levels, named Entities and Events. The system combines automatically generated annotations from language models with a powerful text editor that is extended to accommodate manual annotation. The goal is to support the extraction of information from historical documents by scientists in the SS&H field. At the time of writing of the paper, the system is still in development.

The paper presents a new BERT model, fine-tuned for parsing of Bulgarian texts. This model is extended with a new neural network layer in order to incorporate shallow syntactic information during the training phase. The results show statistically significant improvement over the baseline. Thus, the addition of syntactic knowledge - even partial - makes the model better. Also, some error analysis has been conducted on the results from the parsers. Although the architecture has been designed and tested for Bulgarian, it is also scalable for other languages. This scalability was shown here with some experiments and evaluation on an English treebank with a comparable size.