TextCases
Listing of Text Content Types »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
- TextCases is used to perform several natural language processing tasks such as part-of-speech tagging or named entity recognition.
- 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 form1form2… - 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 pattern a 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 f apply f to the association containing all properties {prop1,prop2,…} a list of property specifications - The following options can be given:
-
AcceptanceThreshold Automatic minimum probability to accept identification PerformanceGoal Automatic favor algorithms with specific advantages TargetDevice "CPU" whether CPU or GPU computation should be used for entity detection VerifyInterpretation False whether interpretability should be verified - TextCases uses machine learning. Its methods, training sets and biases included therein may change and yield varied results in different versions of the Wolfram Language.
- TextCases may download resources that will be stored in your local object store at $LocalBase and can be listed using LocalObjects[] and removed using ResourceRemove.
Examples
open allclose allBasic Examples (6)
Scope (5)
ContentObject and Files (2)
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)
Entities and Interpretable Objects (2)
Return interpreted strings as Entity objects:
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:
Text
Wolfram Research (2015), TextCases, Wolfram Language function, https://reference.wolfram.com/language/ref/TextCases.html (updated 2019).
CMS
Wolfram Language. 2015. "TextCases." Wolfram Language & System Documentation Center. Wolfram Research. Last Modified 2019. https://reference.wolfram.com/language/ref/TextCases.html.
APA
Wolfram Language. (2015). TextCases. Wolfram Language & System Documentation Center. Retrieved from https://reference.wolfram.com/language/ref/TextCases.html