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

• URL: http://arxiv.org/abs/2402.05545v1
• Date: Thu, 8 Feb 2024 10:29:11 GMT
• Title: Named Entity Recognition for Address Extraction in Speech-to-Text Transcriptions Using Synthetic Data
• Authors: Bibi\'ana Laj\v{c}inov\'a, Patrik Val\'abek and Michal Spi\v{s}iak
• Abstract summary: This paper introduces an approach for building a Named Entity Recognition (NER) model built upon a Bidirectional Representations from Transformers (BERT) architecture. This NER model extracts address parts from data acquired from speech-to-text transcriptions. The performance of our NER model, trained solely on synthetic data, is evaluated using small real test dataset.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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