Topic Stability over Noisy Sources
Computation and Language
2015-08-06 v1 Information Retrieval
Abstract
Topic modelling techniques such as LDA have recently been applied to speech transcripts and OCR output. These corpora may contain noisy or erroneous texts which may undermine topic stability. Therefore, it is important to know how well a topic modelling algorithm will perform when applied to noisy data. In this paper we show that different types of textual noise will have diverse effects on the stability of different topic models. From these observations, we propose guidelines for text corpus generation, with a focus on automatic speech transcription. We also suggest topic model selection methods for noisy corpora.
Cite
@article{arxiv.1508.01067,
title = {Topic Stability over Noisy Sources},
author = {Jing Su and Oisín Boydell and Derek Greene and Gerard Lynch},
journal= {arXiv preprint arXiv:1508.01067},
year = {2015}
}