CrowdFlower’s Data Enrichment Platform Supports Eight New Languages

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crowdflower_logoCrowdFlower, a leading data enrichment platform to help data scientists collect, clean and label data to make it useful, recently announced support for eight new languages: Hindi, Arabic, Indonesian, Turkish, Italian, Russian, Vietnamese, and Chinese as well as enhanced support for French, German, Portuguese and Spanish. Businesses can now tap into these new Language Crowds to enrich data that requires language proficiency.

Many global and local businesses need to enrich data that requires foreign language proficiency to ensure accuracy and the correct interpretation of data. Performing this work in-house is not only time-consuming and but also may become impossible without access to enough people with the necessary language skills. Outsourcing is not only expensive, but also difficult to ensure a high level of data quality. Additionally, there are still limitations to natural language processing technology (NLP), especially in languages other than English.

Human intelligence is the best way to accurately interpret and categorize data across multiple languages. We’ve used a combination of geographic targeting and our existing Language Crowds in the past to enable our customers to enrich data in dozens of languages, but with the addition of these eight new crowds we’ve streamlined the process and made it even faster for customers to get the high quality data they need,” said Lukas Biewald, founder and CEO of CrowdFlower.

CrowdFlower combines a network of over five million contributors with artificial intelligence to provide companies with consistent, high quality results and complete tasks computers cannot. Companies can target tasks to one or more Language Crowds to do a wide variety of data enrichment projects including tuning search relevance on ecommerce websites, sentiment analysis of social content and text moderation.

This latest generation of Language Crowds was developed in collaboration with CrowdFlower’s Data Science team and contributors were carefully selected for new Language Crowds based on a model that takes into account the candidates’ geolocation, browser data, tasking habits and performance history.

 

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