Technical Report on classification of literature related to children speech disorder
Journal:
arXiv
Published Date:
May 20, 2025
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.
