Novelty with Transformers and Clustering

Document analysis and understanding is an active area of research in the applied NLP community. In this talk, Alex Cunliffe demonstrates an unsupervised method to organize a body of text into a set of topics and outliers.This approach uses a transformer model that has been fine-tuned for semantic similarity (SentenceTransformers hyperlink: sbert.net). It can be used to quickly review a large set of documents to identify areas of interest or concern without requiring a human to exhaustively read through each document one-by-one. She demonstrates this approach applied to the lyrics of an early-2000s hit musical piece.


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