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Relativity Analytics Specialist – RelativityOne Questions with Complete Solutions
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Relativity Analytics Specialist – RelativityOne Questions with Complete Solutions
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Relativity Analytics Specialist – RelativityOne Questions with Complete SolutionsConceptual Analytics Correct Answer-helps you organize and assess the semantic content of large, diverse and/or unknown sets of documents. Unlike structured analytics, which relies on the specific structure of the content, conceptual analytics focuses on related concepts within documents, even if they don't share the same key terms and phrases.
Conceptual analytics helps reveal the facts of a case by doing the following: Correct Answer-Giving users an overview of the document collection through clustering
Helping users find similar documents with a right-click
Allowing users to build example sets of key issues
Running advanced keyword analysis
Structured v. Conceptual Correct Answer-Takes word order into consideration v. Leverages Latent Semantic Indexing (LSI), a mathematical approach to indexing documents
Doesn't require an index (requires a set) v. Requires an Analytics Index Enables the grouping of documents that are not necessarily conceptually similar, but that have similar content v. Uses co-occurrences of words and semantic relationships between concepts
Takes into account the placement of words and looks to see if new changes or words were added to a document v. Doesn't use word order
Analytics Indexes Correct Answer-It doesn't use any outside word lists, such as dictionaries or thesauri, and it isn't limited to a specific set of languages. Unlike textual indexing, word order is not a factor.
The searchable set and training set used for Analytics index creation are now referred to as the data source and training data source.
Permissions for the Search Index object must be kept in sync with permissions on the Analytics Index object.
The basis of conceptual analytics and Active Learning is an Analytics index. There are two types of indexes: Correct Answer-Conceptual - uses Latent Semantic Indexing (LSI) to discover concepts between documents. This indexing process is based solely on term co-occurrence.
The language, concepts, and relationships are defined entirely by the contents of your documents and learned by the index.
Classification - uses coded examples to build a Support Vector Machine (SVM) 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.
You can run the following Analytics operations on documents indexed by a conceptual index: Correct Answer-Analytics categorization sets
Clustering
Concept searching
Keyword expansion
Find similar documents
Securing an Analytics index Correct Answer-If you want to apply item-
level or workspace-level security to an Analytics index, you must secure
both the Analytics Index object and the Search Index object for that particular index.
Restricting a group from viewing an Analytics Index does not restrict them from searching on the index unless access to the corresponding Search Index is also restricted.
If you're applying item-level security from the Search Indexes tab, you may need to create a new view and add the security field to the view.
Analytics index console operations Correct Answer-Once you save the Analytics index, the Analytics index console appears. From the Analytics index console, you can perform the following operations: Populating the index
Monitoring population/build status
Retrying exceptions
Viewing conceptual index document exceptions
Showing population statistics
Showing index statistics
Population statistics and index statistics are only available for conceptual
indexes.
Populating the index Correct Answer-To populate the Analytics index on the full set of documents, click Run on the Analytics Index console, then choose Full from the modal that appears. This adds all documents from the data source and training data source to the ready-to-index list. Document "preprocessing" also occurs to clean up text. This includes the
following:
Numbers and symbols are ignored.
All words are made lowercase.
Filters found under Advanced Settings are applied (for example, email header filter).
Once population is complete, the index builds.
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