Corpus Topic Modeling Algorithm
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A Corpus Topic Modeling Algorithm is a modeling algorithm that can be implemented to a topic modeling system solve a topic modeling task.
- Context:
- It can be a Document Topic Clustering Algorithm.
- It can be:
- a Probabilistic Topic Modeling Algorithm, such as LDA (Latent Dirichlet Allocation).
- a Manual Topic Modeling Process (as done, for example, by librarians).
- a Neural Topic Modeling Algorithm, like those using BERT embeddings.
- It is typically employed to discover hidden thematic structures within a large collection of documents.
- ...
- Example(s):
- BERTopic, which leverages BERT embeddings to cluster and identify topics in text data.
- LDA (Latent Dirichlet Allocation), a probabilistic model that discovers topics based on word distributions.
- NMF (Non-negative Matrix Factorization), an approach that factorizes the document-term matrix to uncover topics.
- ...
- Counter-Example(s):
- a Text Clustering Algorithm, which groups texts based on similarity but may not uncover underlying topics.
- a Word Similarity Learning Algorithm, which focuses on the relationships between individual words rather than the thematic content of documents.
- a Document Classification Algorithm, which assigns predefined labels to documents rather than discovering topics.
- See: Model, Machine Learning Algorithm, Statistical Modeling Algorithm.