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Apply Tisane Entity Extraction to Open Measures Odnoklassniki
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About Tisane Entity Extraction
Detect mentions of people, organizations, locations, filenames, phone numbers, crypto addresses, and more.
Entities are elements of relevance or interest in the text. Tisane extracts both standard entities and those relevant to trust & safety/law enforcement applications.
Standard entities are names of people, their social roles, organizations, places, and so on. We also extract cryptocurrency addresses, bank accounts, credit card numbers, phone numbers, software package names, and more.
Every entity entry is an object made of:
type - the type of the entity
name - a standard name, if exists; otherwise, the string that was logged
subtypes - more detailed additional types
subtype - the first subtype (for backward compatibility purposes)
mentions - an array of all detected mentions, with:
offset
length
sentence_index
text
wikidata - a Wikidata ID, if exists
About Open Measures Odnoklassniki
Open Measures collection on OK(Odnoklassniki) began by aggregating around 5,000 OK group URLs that appeared across the existing collections and fed these into the crawler. The crawler then spiders across these groups by collecting posts, comments and group members.
Quickly apply Tisane Entity Extraction to Open Measures Odnoklassniki with a Datstreamer Pipeline.
Step 1
Start your Pipeline with Open Measures Odnoklassniki
Web data is the foundation of any pipeline. You can leverage a wide range of data sources to power your pipelines—whether it's data from our partner network, your internal systems, or any publicly available web data.
Step 2
Add Tisane Entity Extraction with an Operation
Datastreamer lets you accelerate your data usage by applying operations like structuring, enriching, joining, and filtering—choose from hundreds of prebuilt, plug-and-play options.
Step 3
That's it! You have just connected Tisane Entity Extraction and Open Measures Odnoklassniki
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