Technical Report on classification of literature related to children speech disorder

TL;DR

This report introduces NLP-based methods using LDA and BERTopic to classify and analyze a large corpus of scientific articles on childhood speech disorders, aiding automated literature review processes.

Contribution

It applies and compares LDA and BERTopic models with domain-specific preprocessing to identify meaningful thematic clusters in speech disorder literature.

Findings

LDA achieved a coherence score of 0.42 and perplexity of -7.5.

BERTopic showed less than 20% outlier topics, indicating effective classification.

14 clinically meaningful clusters were identified.

Abstract

This technical report presents a natural language processing (NLP)-based approach for systematically classifying scientific literature on childhood speech disorders. We retrieved and filtered 4,804 relevant articles published after 2015 from the PubMed database using domain-specific keywords. After cleaning and pre-processing the abstracts, we applied two topic modeling techniques - Latent Dirichlet Allocation (LDA) and BERTopic - to identify latent thematic structures in the corpus. Our models uncovered 14 clinically meaningful clusters, such as infantile hyperactivity and abnormal epileptic behavior. To improve relevance and precision, we incorporated a custom stop word list tailored to speech pathology. Evaluation results showed that the LDA model achieved a coherence score of 0.42 and a perplexity of -7.5, indicating strong topic coherence and predictive performance. The BERTopic model exhibited a low proportion of outlier topics (less than 20%), demonstrating its capacity to classify heterogeneous literature effectively. These results provide a foundation for automating literature reviews in speech-language pathology.