Named Entity Recognition for Address Extraction in Speech-to-Text
  Transcriptions Using Synthetic Data
Bibiána Lajčinová Patrik Valábek Michal Spišiak
This paper introduces an approach for building a Named Entity Recognition
(NER) model built upon a Bidirectional Encoder Representations from
Transformers (BERT) architecture, specifically utilizing the SlovakBERT model.
This NER model extracts address parts from data acquired from speech-to-text
transcriptions. Due to scarcity of real data, a synthetic dataset using GPT API
was generated. The importance of mimicking spoken language variability in this
artificial data is emphasized. The performance of our NER model, trained solely
on synthetic data, is evaluated using small real test dataset.