Text Entity Recognition

Identify named entities in your text to train your NLP models

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FAST

COMPETITIVE

INNOVATIVE

In the information world, we are continuously dealing with text content that can mean a variety of things. Rich sources of information such as the news, email, text or social media messages, contain all kinds of entities and depending on the type of information entities can mean multiple things such as events, people, relationships between people, places or organizations. Unlike structured data such as databases or tables, natural texts are made up of free-flowing information without a set schema. Regular expressions (regex) can help extract some entities such as phone numbers based on a pattern, but extracting other information like names of people, relationship between different entities or details of calendar events requires more advanced language processing in order for an NLP application to build the intelligence and refer such entities to Knowledge Graphs for example.

Depending on the scope of your project, Hybrid Lynx annotators offer entity recognition and entity relationship annotation for large volumes of content across multiple common and low resource languages. Reach out today to discuss your unique requirements.

REACH THE WORLD

Custom Multilingual Text Entity Recognition Training Data

Named Entity Recognition (NER)

Named Entity Linking (NEL)

Parts of Speech (POS) Detection

Keyphrase Identification & Extraction

Topic Recognition & Extraction

Named Entity Relationiship Extraction

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Multilingual Text Entity Recognition

Multiple Languages

Whether your algorithm requires annotated entity recognition in English or in a foreign language, we provide the right skills and scale to get the correct data. Algorithms depend on quality data to deliver quality output. The success of your project comes down to the quality of your and training data.

Versatile NLP Domains

Your algorithm works with a confined scope to learn from existing annotated data. We provide entity detection and annotation covering named entities, parts of speech, keyphrase extraction, topic recognition, named entity linking and relationship extraction.

Scale and Volume

Models are only successfull if they learn from good examples, using quality training data, but equally important is the quantity of data. We offer the scale with our multilingual annotation crowd to deliver large volumes of annotated entity recognition training data for your models.