Relativity Analytics WEEK ONE: Create
and Run Conceptual Indexes Exam |
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Verified | Latest Update 2026/2027 Exam
| Questions with 100% Correct Answers |
Verified | Latest Update 2026/2027
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Terms in this set (65)
Conceptual Analytics Conceptual analytics helps you organize
Overview 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 conceps within documents,
even if they don't share the same key
terms and phrases. Using these features,
you can cut down on review time by
more quickly assessing your document
set to facilitate workflow.
,Conceptual Analytics After using structured data analytics to
Operations group your documents, you can run
Analytics operations to identify
conceptual relationships present within
them. For instance, you can identify which
topics contain certain issues of interest,
which contain similar concepts, and/or
which contain various permutations of a
given term.
Conceptual Analytics First To run conceptual Analytics operations,
Step - Analytics Index you must first create an Analytics index.
Conceptual analytics helps 1) Giving users an overview of the
reveal the facts of a case by document collection through clustering
doing the following: 2) Helping users find similar documents
with a right-click
3) Allowing users to build example sets
of key issues
4) Running advanced keyword analysis"
,Structured analytics 1. Takes word order into consideration
2. Doesn't require an index (requires a
set)
3. Enables the grouping of documents
that are not necessarily conceptually
similar, but that have similar content
4. Takes into account the placement of
words and looks to see if new changes or
words were added to a document
Conceptual analytics 1. Leverages Latent Semantic Indexing
(LSI), a mathematical approach to
indexing documents
2. Requires an Analytics Index
3. Uses co-occurrences of words and
semantic relationships between concepts
4. Doesn't use word order
5. Unlike traditional searching methods
like dtSearch, Analytics is an entirely
mathematical approach to indexing
documents. It does not use any outside
word lists, such as dictionaries or
thesauri, and it is not limited to a specific
set of languages. Unlike textual indexing,
word order is not a factor.
, Conceptual Analytics Unlike traditional searching methods like
Approach dtSearch, Analytics is an entirely
mathematical approach to indexing
documents. It does not use any outside
word lists, such as dictionaries or
thesauri, and it is not limited to a specific
set of languages. Unlike textual indexing,
word order is not a factor.
Analytics Indexes The basis of conceptual analytics and
active learning is an Analytics index.
There are two types of indexes:
conceptual and classification
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. For
more information, see Analytics and
Latent Semantic Indexing (LSI).
and Run Conceptual Indexes Exam |
Questions with 100% Correct Answers |
Verified | Latest Update 2026/2027 Exam
| Questions with 100% Correct Answers |
Verified | Latest Update 2026/2027
Save
Terms in this set (65)
Conceptual Analytics Conceptual analytics helps you organize
Overview 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 conceps within documents,
even if they don't share the same key
terms and phrases. Using these features,
you can cut down on review time by
more quickly assessing your document
set to facilitate workflow.
,Conceptual Analytics After using structured data analytics to
Operations group your documents, you can run
Analytics operations to identify
conceptual relationships present within
them. For instance, you can identify which
topics contain certain issues of interest,
which contain similar concepts, and/or
which contain various permutations of a
given term.
Conceptual Analytics First To run conceptual Analytics operations,
Step - Analytics Index you must first create an Analytics index.
Conceptual analytics helps 1) Giving users an overview of the
reveal the facts of a case by document collection through clustering
doing the following: 2) Helping users find similar documents
with a right-click
3) Allowing users to build example sets
of key issues
4) Running advanced keyword analysis"
,Structured analytics 1. Takes word order into consideration
2. Doesn't require an index (requires a
set)
3. Enables the grouping of documents
that are not necessarily conceptually
similar, but that have similar content
4. Takes into account the placement of
words and looks to see if new changes or
words were added to a document
Conceptual analytics 1. Leverages Latent Semantic Indexing
(LSI), a mathematical approach to
indexing documents
2. Requires an Analytics Index
3. Uses co-occurrences of words and
semantic relationships between concepts
4. Doesn't use word order
5. Unlike traditional searching methods
like dtSearch, Analytics is an entirely
mathematical approach to indexing
documents. It does not use any outside
word lists, such as dictionaries or
thesauri, and it is not limited to a specific
set of languages. Unlike textual indexing,
word order is not a factor.
, Conceptual Analytics Unlike traditional searching methods like
Approach dtSearch, Analytics is an entirely
mathematical approach to indexing
documents. It does not use any outside
word lists, such as dictionaries or
thesauri, and it is not limited to a specific
set of languages. Unlike textual indexing,
word order is not a factor.
Analytics Indexes The basis of conceptual analytics and
active learning is an Analytics index.
There are two types of indexes:
conceptual and classification
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. For
more information, see Analytics and
Latent Semantic Indexing (LSI).
