TextCases[text,form]

gives a list of all cases of text identified as being of type form that appear in text.

TextCases[text,{form1,form2,}]

gives an association of results for all the types formi.

TextCases[text,formspecprop]

gives the specified property for each result found.

TextCases[text,formspec{prop1,prop2,}]

gives a list of properties for each result found.

TextCases[text,spec,n]

gives the first n cases found.

Details and Options

  • In TextCases[text,], text can be a string, a file with plain text represented by File[], a ContentObject expression or a list of these text objects.
  • TextCases[{text1,text2,},] gives cases for each texti.
  • Identification type form can be:
  • "type"any text content type (e.g. "Noun", "City")
    Entity[,]a specific entity of a text content type
    form1|form2|
  • form matching any of the formi
  • Containing[outer,inner]forms of type outer containing type inner
    Verbatim["string"]a specific string to be matched exactly
    patterna string pattern to be matched
  • Possible choices for the property prop are:
  • "String"string of the identified text (default)
    "Position"start and end position of the string in text
    "Probability"estimated probability that the identification is correct
    "Interpretation"standard interpretation of the identified string
    "Snippet"a snippet around the identified string
    "HighlightedSnippet"a snippet with the identified string highlighted
    fapply f to the association containing all properties
    {prop1,prop2,}a list of property specifications
  • The following options can be given:
  • AcceptanceThresholdAutomaticminimum probability to accept identification
    PerformanceGoalAutomaticfavor algorithms with specific advantages
    TargetDevice"CPU"whether CPU or GPU computation should be used for entity detection
    VerifyInterpretationFalsewhether interpretability should be verified

Examples

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Basic Examples  (6)

Find the cities in a text:

Find the nouns in a sentence:

Find currency amounts and get interpretations:

Find cities, countries and dates in text:

Obtain probabilities and interpretations:

Find all the locations and get their positions:

Find all references to New York City in a text:

Scope  (5)

ContentObject and Files  (2)

Find instances of colors in a ContentObject:

Find quantities in a File:

Alternatives and Containing  (2)

Use Alternatives to match multiple types:

Find all sentences in a string that contain currency amounts:

Find all sentences in a string that contain countries:

Combine Alternatives and Containing to form highly structured queries:

Return Types  (1)

Specify multiple return types:

Show all the available properties in an Association:

Create a dataset with the properties of several types of entities:

Get the geodetic positions of the locations occurring in a text:

Options  (3)

AcceptanceThreshold  (1)

By default, all the detected entities have an estimated probability higher than 0.5:

Get only the entities that are highly probable to be correct by setting a high AcceptanceThreshold:

PerformanceGoal  (1)

Using PerformanceGoal->"Speed" can help to have faster detection, at the cost of lower accuracy:

VerifyInterpretation  (1)

By default, some entities cannot be interpreted, either because they are not correct or because they are not yet in the knowledgebase. In these cases, a string is returned instead of an interpretation:

Use VerifyInterpretation to filter out the entities that cannot be interpreted:

Applications  (6)

Word and Sentence Segmentation  (2)

Word segmentation preserves syntactic elements such as email addresses, URLs, and Twitter handles:

All the non-whitespace characters are grabbed with forms "Word" and "Punctuation":

Sentence segmentation intelligently ignores acronyms and other misleading boundaries:

Parts of Speech  (2)

Return all words of a given part of speech:

Make a table of word clouds from parts of speech:

Entities and Interpretable Objects  (2)

Find countries:

Return interpreted strings as Entity objects:

Find currency amounts in a Wikipedia article:

Convert to another currency:

Properties & Relations  (4)

TextCases handles the same types as TextPosition and TextContents and always identifies the same substrings as these functions for a given type:

TextCases is a generalization of TextPosition:

A dataset that is similar to the output of TextContents can be obtained using TextCases:

TextSentences is equivalent to TextCases[,"Sentence"]:

TextStructure splits texts into the same sentences:

TextWords is equivalent to TextCases[,"Word"]:

TextStructure splits texts into the same words and punctuation marks as TextCases[,"Word"|"Punctuation"]:

Neat Examples  (2)

Many entities (cities, countries, etc.) can be located on a map. TextCases allows you to find all these entities at once.

Take the Wikipedia article about rice:

Find all entities that can be pinpointed to a location:

Visualize the locations identified and their frequency in the text:

Show the number of mentions of each continent and country:

Take the Wikipedia article about world wars:

Find all sentences containing dates and extract their corresponding DateObject interpretations:

Display these dates on a timeline:

Display the extracted sentences on a timeline:

Introduced in 2015
 (10.2)
 |
Updated in 2019
 (12.0)