# Named Entity Recognition for Address Extraction in Speech-to-Text Transcriptions Using Synthetic Data

arXiv: https://arxiv.org/abs/2402.05545 · HTML: https://mlsanity.com/paper/2402.05545/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 1 of its 4 references that are in the map.
- First posted on arXiv 8 Feb 2024.

## Builds on (most cited first, 1 of 1)

- [SlovakBERT: Slovak Masked Language Model](https://mlsanity.com/paper/2109.15254/) (2021)

## Abstract (arXiv)

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.