Technical Report on classification of literature related to children speech disorder

2025-05-20

AI Summary

The rapid growth of literature on childhood speech disorders impedes efficient manual systematic reviews. Method: We developed an NLP-driven automated classification system leveraging 4,804 PubMed-indexed publications from 2015 onward. Our approach innovatively integrates BERTopic and LDA topic modeling, augmented by a domain-specific stopword list tailored to speech-language pathology, thereby enhancing clinical interpretability and classification accuracy. Contribution/Results: The system identifies 14 clinically meaningful topic clusters. LDA achieves a coherence score of 0.42 and perplexity of −7.5; BERTopic yields an outlier topic proportion below 20%. This work represents the first synergistic application of BERTopic and LDA in speech pathology literature analysis, delivering a reproducible, high-fidelity, and clinically grounded automation framework for evidence-based practice and knowledge graph construction.

Technology Category

• Natural Language Processing: Speech
• Data Mining & Knowledge Management: Conversational Systems for Recommendation & Retrieval
• Application Domains: Humanities & Computational Social Science

Application Category

• Web Mining and Content Analysis: Topic discovery and tracking
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Search and Retrieval-Augmented AI: Large language models for search

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.

Problem

• Classifying scientific literature on childhood speech disorders using NLP
• Identifying latent thematic structures in speech disorder research articles
• Automating literature reviews in speech-language pathology via topic modeling

Innovation

• NLP-based approach for literature classification
• LDA and BERTopic for topic modeling
• Custom stop word list for precision