The entity extraction service detects and extracts:
- Proper names such as: Lionel Messi, Tom Brady, Puerto Rico, United Nations. These ones can be classified to different categories:
people, places, organizations
- Numeric entities like: bank accounts or phone numbers
- Alphanumeric entities as: car plates, web addresses
- Social media users and hashtags
The service detects entities even though they may be written in different forms: for example: 20:00, 20 hours, 20h, 8pm…).
In addition, it applies a normalisation process to the entities into a standard form in order to consistently handle all instances of the same entity (NYSE, New York Stock Exchange, NY Stock Exchange are instances of the same entity).
The service can provide on demand the detection of entities which are not written in upper case: “I am in new york”.
Bitext’s linguistic engine assigns types to entities depending on syntactic rules: for example, in the sentence “I live at Barack Obama” the name of the president is interpreted as the name of an avenue, whereas in the sentence “As Barack Obama said” the proper noun is identified as the name of the US president. This feature is provided on demand.
Our cloud services help market research professionals and data scientists perform sentiment analysis, categorization and entity & concept extraction, easily and effectively.
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