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.

本文介绍了一种建立在双向编码器变换器（BERT）架构上的命名实体识别（NER）模型的方法，具体使用了 SlovakBERT 模型。该 NER 模型从语音转文本转录获取的数据中提取地址部分。由于真实数据的稀缺性，使用 GPT API 生成了一个合成数据集。强调了在这种人工数据中模仿口语变异的重要性。我们的 NER 模型仅使用合成数据进行训练，并使用小型真实测试数据集进行评估。