# Automating the Information Extraction from Semi-Structured Interview Transcripts

arXiv: https://arxiv.org/abs/2403.04819 · HTML: https://mlsanity.com/paper/2403.04819/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 0 of its 13 references that are in the map.
- First posted on arXiv 7 Mar 2024.

## Built on by (most cited first, 5 of 5)

- [Text Annotation via Inductive Coding: Comparing Human Experts to LLMs in Qualitative Data Analysis](https://mlsanity.com/paper/2512.00046/) (2025)
- [Exploring the Human-LLM Synergy in Advancing Theory-driven Qualitative Analysis](https://mlsanity.com/paper/2405.05758/) (2024)
- [A Structured Literature Review on Traditional Approaches in Current Natural Language Processing](https://mlsanity.com/paper/2505.12970/) (2025)
- [From Quotes to Concepts: Axial Coding of Political Debates with Ensemble LMs](https://mlsanity.com/paper/2601.15338/) (2026)
- [Designing Computational Tools for Exploring Causal Relationships in Qualitative Data](https://mlsanity.com/paper/2602.06506/) (2026)

## Abstract (arXiv)

This paper explores the development and application of an automated system designed to extract information from semi-structured interview transcripts. Given the labor-intensive nature of traditional qualitative analysis methods, such as coding, there exists a significant demand for tools that can facilitate the analysis process. Our research investigates various topic modeling techniques and concludes that the best model for analyzing interview texts is a combination of BERT embeddings and HDBSCAN clustering. We present a user-friendly software prototype that enables researchers, including those without programming skills, to efficiently process and visualize the thematic structure of interview data. This tool not only facilitates the initial stages of qualitative analysis but also offers insights into the interconnectedness of topics revealed, thereby enhancing the depth of qualitative analysis.