Technical Report on classification of literature related to children speech disorder
This work provides a foundation for automating literature reviews in speech-language pathology, addressing a domain-specific need for researchers and clinicians.
The authors tackled the problem of systematically classifying scientific literature on childhood speech disorders by applying NLP-based topic modeling to 4,804 articles, achieving strong topic coherence with an LDA model coherence score of 0.42 and low outlier topics in BERTopic.
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.