Collect, Label and Validate Text-Based Training Data
People express ideas and intent in different ways creating a complex job for your Natural Language engines. Our text-based AI training data provides high quality datasets in multiple languages and domains to improve your NLU, NLG or TTS engines.
Conversational AI Text Collection
Text datasets consisting of conversations between 2 entities.
Text Variant Collection
Datasets consisting of text variants around a specific concept.
Text Validation
Validates the quality of any text based dataset on specified criteria.
Named Entity Tagging for NER
Annotate and classify single entities in a sentence into pre-defined categories.
Multiple Named-Entity Tagging
Annotate and classify multiple entities in a sentence into pre-defined categories.
Sentiment Tagging
Annotate sentences for sentiment, ex. good, neutral, bad.
Semantic Annotation
Annotate and classify sentences or phrases by domain and intent.
Text Quality Guarantee
Natural language systems rely on multiple quality metrics to function optimally. We combine Word Error Rate (WER) measurements with our ML algorithms and human in the loop validations to ensure your models operate at maximum accuracy.
Spelling and GrammarChecks for proper syntax for each language. |
Inter-Annotator AgreementsF1 score >.8 for all annotations with dynamic judgment utilized to perform tiebreakers. |
Word Error RateGuaranteed <5%. |
NativenessEnsures the datasets use native speakers for transcriptions. |
Success Stories
Mastercard’s R&D Labs needed unique, multi-lingual text data that covered 20 designated payment scenarios in English and Spanish, and they needed it fast.
Keeping a nation’s lights on means constantly inspecting electricity poles for damage. EDP partnered with DefinedCrowd to improve Asset Performance Management processes.
With the rise of voice technology, this leading global provider of audio equipment wanted to develop an automatic speech recognition (ASR) model.
A global electronics maker came to DefinedCrowd with the goal of building more inclusive facial recognition models, requiring accurately annotated images with highly specific criteria.
Smart companies see the pile of unstructured text floating through the digital realm as a strategic goldmine of consumer insights.
A Fortune 500 Tech company needed comprehensive speech training data in French that accounted for a wide range of dialects, requiring diverse data in terms of age, gender and regional dialects.
A visionary Fortune 500 Tech company leveraged sentiment analysis models to dig beyond surface-level understandings to extract granular-level insights.
