Massive volumes of finance-related data are created on the Internet daily, whether on question-answering forums, news articles, or stock analysis sites. This data can be critical in the decision-making process for targeting investments in the stock market.  

Extracting information from such sources in order to utilize the volumes of data, which is impossible to process manually is the core of this session. The study presented is based on the employment of different models for word embedding and different Deep Learning classification architectures for extracting the entities and predicting relations between them.  

Furthermore, the multilingual abilities of a joint pipeline are being explored by combining English and German corpora. For both subtasks, we will show state-of-the-art performances of 97.69% F1 score for named entity recognition and 89.70% F1 score for relation extraction. 

We had Igor Mishkovski PhD, professor at the Faculty for Computer Science and Engineering, dive deep into subject, in the Re:Imagine session that we held on 29.11.2022, and below, you can watch the whole session from start to finish! 

Topics covered:

  • Introduction; 
  • Dataset description; 
  • Methodologies; 
  • Results and Discussion; 
  • Future Work and Conclusion. 

Click play for the full session!