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Explore how semantic vectors and machine learning can be used to discover research ideas in this 49-minute conference talk from GOTO Copenhagen 2016. Learn about UNSILO's mission to enrich scientific content and improve discoverability across domains. Dive into key challenges in full-text search, keyword usage, and ontologies. Discover UNSILO's innovative approaches, including exhaustive concept extraction, complete semantic mapping, and phrase extraction. Understand the application of word embeddings, Word2Vec, and ontology-augmented vector spaces. Explore how synsets are built using vector cosine similarity and how human-readable fingerprints are created. See demonstrations of UNSILO's discovery widgets for easier content exploration. Gain insights into future directions for research idea discovery and content enrichment in scientific publishing.
Discovering Research Ideas Using Semantic Vectors and Machine Learning