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Machine-learning searches work only as well as their statistical model. Sometimes, no number of iterations can fix a statistical model with limited flexibility. That's why being able to customize the statistical model to meet the challenges of a particular data set is so important for success.
Cavo Health focused its attention for semantic predictive coding on Latent Semantic Indexing (LSI), but have added some significant capabilities to what we are now calling Enhanced Latent Semantic Indexing (ELSI) (patent pending). We have added three customizable similarity measurements to the statistical model so that it more accurately and more quickly finds similar documents to a reference set. Traditional LSI uses the average cosine similarity between the query vector and the document vectors in the seed set. This approach has a serious drawback that it will give a lower score to a document that is highly similar to one of the seed document but dissimilar to most of the seed documents. Some applications may require ranking documents based on the similarity of their metadata in addition to the content-based similarities regardless of the reliability of the metadata in your particular data collection. Cavo Health enhances LSI to make it work in both of these cases. Cavo Health provides three different similarity metrics that contribute to the score of a document given an input seed document set.