Search

Location:  Search Menu

General

Using the OpenSearch search engine, the Lexbe eDiscovery Platform (LEP) offers comprehensive, multi-index search methods to search for specific documents across an entire case using various search options.    

LEP offers search results generated from Optical Character Recognition (OCR), which captures image-based text, as well as native extraction, meaning text that is excluded from print and/or hidden/embedded text. Going a step further, LEP offers an additional index to search across translated documents, using a multi-lingual search index.

**Update**

As of July 15, 2023, all search functionality has been moved to the Browse page; however, all information on this Help Page remains accurate. For more information, please see Browse & Search (Shared Features) 

Noise Words

Noise words do not exist in OpenSearch. As such, all words are indexed.

Boolean Searches

LEP supports advanced Boolean searches. A Boolean search consists of a group of words or phrases linked by connectors such as AND and OR that indicate relationship or logic. Supported Boolean operators are listed below with examples. Please note, Boolean operators must be capitalized when used.

AND

Example Search Term:  Apple AND Pear

Expected Results:  Returns documents that only contain both of the words Apple AND Pear.

OR

Example Search Term:  Apple OR Pear  

Expected Results:  Returns documents that contain either of the words Apple OR Pear.

**A single space can be used in place of the OR operator. In this example, the search term would read:  Apple Pear

NOT or AND NOT

Example Search Term:  Apple NOT Pear

Example Search Term:  Apple AND NOT Pear

Expected Results:  Returns documents that contain the word Apple but not Pear.

Parenthesis

To use more than one connector, use parentheses to indicate the precise search criteria.  For example, apple AND pear OR orange juice could mean (apple AND pear) OR orange, or it could mean apple AND (pear OR orange).

Phrase Search

Example Search Term:  "Apple Pear"

Expected Results:  Returns documents that contain the exact phrase Apple Pear.

Wildcard Searches

Single and multi character wildcard searches are supported in LEP when searching document content or metadata. Examples of some supported wildcard searches are included below.

Single Character Wildcard:  ?

Example Search Term:  Appl?

Expected Results:  Documents containing Apply or Apple

Example Search Term:  F?x

Expected Results:  Documents containing Fix, Fox, Fax, etc.

Multi Character Wildcard:  *

Example Search Term:  Appl*

Expected Results:  Documents containing Apply, Apples, Application, Appliance, etc.

Example Search Term:  Fa*

Expected Results:  Documents containing Fax, Faxes, Fade, Fad, Fail, etc.

Fuzzy Searches

Fuzzy searching will find a word even if it is slightly misspelled. For example, a fuzzy search for "liti3ation" will find "litigation." Fuzzy searching can be useful when searching text that may contain typographical errors or for text that has been scanned using optical character recognition (OCR). More specifically, Fuzzy searching finds all terms with a maximum of one change, where a change is the insertion, deletion or substitution of a single character, or transposition of two adjacent characters. 

Fuzzy Search Character:  ~

Example Search Term:  Brwn~

Expected Results:  Documents containing Brown, Brawn, etc.

Example Search Term:  Erica~

Expected Results:  Documents containing Erika, Ericka, etc.

Example Search Term:  Hide~

Expected Results:  Documents containing Hides, Hider, Hid, etc.

Proximity Searches

Word Distance

A proximity search allows users to specify a maximum distance between words and/or phrases in a document. OpenSearch counts the words between the specified terms, so in the example the quick brown fox ran, the term quick is within two of ran. 

Search Term vs. Relevance of Search Hits

Proximity searches in LEP search for the terms in either order, so in the example "fox quick"~5, even though fox appears first in the search term, documents where quick appears before fox will also be returned. The closer the text is to the order specified in the search term, the more relevant that document is considered to be. When compared to the above example, the phrase "quick fox" would be considered more relevant than "quick brown fox".

Formatting

As demonstrated above, proximity searches must be enclosed in quotes, followed by the tilde, and then the desired distance represented numerically. Please note, that when using phrases within a proximity search, the phrase must be enclosed in both quotes and parenthesis to be properly formatted. 

Currently, LEP supports the following types of proximity searches:

Words

Example Search Term:  "quick fox"~2

Expected Results:  Returns documents where the words quick and fox appear within two words of each other.

Words with Wildcards

Example Search Term:  "fox* quick"~10

Expected Results:  Returns documents where the words fox, foxes, foxy, etc. appear within ten words of quick.

Words and Phrases

Example Search Term:  "fox ("quick brown")"~3

Expected Results:  Returns documents where the word fox appears within three words of the phrase "quick brown."  

Words with Wildcards and Phrases

Example Search Term:  "("quick brown") fox*"~5

Expected Results:  Returns documents where the phrase "quick brown" appears within five words of fox, foxes, foxy, etc.

Words with Wildcards, Phrases, and Boolean Connectors

Example Search Term:  "fox* (red OR "quick brown")"~5 AND hunt

Expected Results:  Returns documents where the word fox, foxes, foxy, etc. appears within five words of red OR the phrase "quick brown", AND hunt is also present. 

Nested Proximity Searches

Example Search Term:  "("quick fox*"~2) red"~5

Expected Results:  Returns documents where quick and fox, foxes, foxy, etc. appear within two words of each other, and they also appear within five words of red.

Metadata Searches

In addition to being able to filter on metadata fields, LEP also supports the searching of certain metadata fields via the Search page. The specific fields are as follows:

Metadata Fields         Search Syntax

To   metadata.to:

From   metadata.from:

Cc   metadata.cc:

Bcc   metadata.bcc:

Subject   metadata.subject:

Author   metadata.author:

Doc Type metadata.docType:

Doc Title   metadata.docTitle:

Doc Original Title   metadata.docOriginalTitle:

Last Modified By metadata.lastModifiedBy:

Master Date metadata.masterDate:

All Metadata Fields   metadata.\*:

Searching a Date or Date Range

To search a date range, enter the range as shown below. Users must (i) use a hyphen as the separator, (ii) enter the date(s) in YYYY-MM-DD format, and (iii) capitalize the word "TO."

Specific Date

Example Search Term:  metadata.masterDate:2019-01-25

Expected Results:  Returns all documents with a Master Date of 1/25/2019.

Full Date Range

Example Search Term:  metadata.masterDate:[2019-01-25 TO 2020-11-13]

Expected Results:  Returns all documents with a Master Date of 1/25/2019, through and including, 11/13/2020.

Partial Date Range

Example Search Term:  metadata.masterDate:[2019 TO 2020]

Expected Results:  Returns all documents with a Master Date of 1/1/2019, through and including, 1/1/2020.

Document Viewer:  Hits Tab

The Search is designed to open documents in the Hits view. Users can easily see where in the document the search terms are being found. Click on the Text hyperlink next to each section of hits to jump to that place in the document.

Document Viewer:  Document Comparison Tool

The built in document comparison tool can be used directly from the Search page.  For more information see Document Comparison Tool.

Layouts and Layout Quick Links (Shared Function)

The Search page also allows users to save preferred field layouts and views.  See Shared Features (Browse & Search) for more information. 

ESI Culling

The process of culling (reducing or filtering) electronically stored information (ESI) is designed to defensibly reduce a large collection of documents to a smaller set of potentially responsive documents requiring further review.  Depending on how documents were collected and other factors, culling can reduce data sets by 90% or more (if broadly collected) and thereby significantly reduce eDiscovery costs.  Culling can be done by Custodian, by date range, and by text.  See ESI Culling for more information.  

Lexbe Professional Services is available to assist with any culling needs or keyword searching.  Contact your sales consultant for a quote, or email professionalservices@lexbe.com to speak to a Professional Services representative about any questions related to searching or culling.