text-mining-science

Topic modeling and scientific entity extraction from research literature text corpora is done using classical NLP pipelines. Apply LDA or NMF to discover latent themes, and use regex-based scientific NER to pull out methods, metrics, datasets, genes, chemicals, and diseases.

Trend detection across time windows in scientific abstracts is performed by combining TF-IDF, RAKE-inspired phrase scoring, and temporal comparisons. This approach identifies research fronts by comparing term frequency changes across different years in the literature.

Claim-like sentences and keywords can be extracted from full-text research papers using classical NLP pipelines rather than deep learning models. TF-IDF vectorization, RAKE-inspired phrase scoring, and pattern-based claim extraction pull structured insights directly from the text.

Topic modeling for scientific corpora requires Python tooling such as scikit-learn for vectorization and matrix decompositions. NLTK is optional and used specifically for text preprocessing tasks like tokenization before applying LDA or NMF algorithms.

Regex-based scientific NER for extracting methods, datasets, and metrics relies on configurable pattern logic rather than contextual understanding. This means it may miss entities not matching predefined patterns and requires manual pattern tuning for different scientific domains.

Document-topic representations for literature search and recommendation are built by applying LDA or NMF topic modeling to the text corpus. The resulting topic distributions and keyword vectors serve as foundational features to support similarity calculations and recommendation workflows.