Use entity analysis to detect personal data in a secure way to ensure compliance with European GDPR Data Privacy legislation
The entity extraction service delivers structure, clarity, and insight out of raw text data.
Locate and classify of entities such as names, persons, and organizations using a combination of NLP technologies:
  • Deep Linguistic Analysis based on grammars
  • Alphanumeric pattern detection using regular expressions
  • Monolingual and multilingual dictionaries
  Entity Extraction

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Want to become compliant with EU's new GDPR legislation? Are you looking for information about your market? Who are the influencers? Which brands are more popular?
Entity Extraction provides you all the structured information you are looking for but without noise: only clear and accurate data, minimizing false positives.
Integrate with our REST API or deploy the entity engine on premise.

Extract and classify different types of entities

The entity extraction service detects and extracts: - Proper names such as: Lionel Messi, Tom Brady, Puerto Rico, United Nations. These ones can be classified into different categories: people, places, organizations - Numeric entities like: bank accounts, money amounts or phone numbers - Alphanumeric entities as: car plates, web addresses, dates, identity cards - Emails,URLs, Social media users and hashtags

Normalizes variants into standard forms

The service detects entities even though they may be written in different forms: for example: 20:00, 20 hours, 20h, 8 pm…). In addition, it applies a normalization process to the entities, presenting them in 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”.

Our technology distinguishes between Barack Obama (person) and Barack Obama (avenue)

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.

sentiment analysis

Sentiment Analysis

text categorization

Categorization

entity extraction

Entity Extraction

concept extraction

Concept Extraction

Test-drive our Text Analytics tools!

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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