Relativity Analytics Specialist Exam - 10.3- Answered

conceptual analytics - ANSWER-helps you organize and assess the semantic content of large, diverse and/or unknown sets of documents conceptual analytics helps reveal the facts of a case by doing the following - ANSWER-1. giving users an overview of the document collection through clustering 2. helping users find similar documents with a right-clock 3. allowing users to build example sets of key issues 4. running advanced keyword analysis conceptual analytics index - ANSWER-uses latent semantic indexing (LSI) to discover concepts between documents. this indexing process is based solely on term co-occurence. the language, concepts, and relationships are defined entirely by the contents of your documents and learned by the index. classification analytics index - ANSWER-uses coded examples to build a support vector machine (SVI) to predict a document's relevance. this index is used solely by the active learning application. classification indexes learn how terms are related to categories based on the contents of your documents and coding decisions made within the active learning project. LSI (latent semantic indexing) - ANSWER-a wholly mathematical approach to indexing documents. instead of using any outside word lists, such as a dictionary or a thesaurus, LSI leverages sophisticated mathematics to discover term correlations and conceptuality within documents. LSI (latent semantic indexing) is language-agnostic - true/false - ANSWER-true concept space - ANSWER-a mathematical model of your document relationships used by LSI concept rank - ANSWER-a measurement of item similarity using a rank value hyperplane - ANSWER-a high-dimensional model used by SVM that analytics uses to help differentiate between relevant and not relevant documents rank - ANSWER-measures the strength or confidence the model has in a document being relevant or not relevant. this is measured on a scale of 100 to 0 in SVM training set - ANSWER-a set of documents that the system uses to learn the language, the correlation between terms, and the conceptual value of documents. this only applies to conceptual indexes