Data annotation service
Even seemingly insurmountable problems can be solved with Artificial Intelligence, but only if the data is collected in sufficient quantity and quality to train the AI effectively. By interacting with natural language more effectively, we help your machine learning algorithms get the answers they need.
Text Labeling
Annotating text with text labels helps to identify the type of text or certain parts of text. This helps your NLP models to comprehend the text and process it. Clearly labelled data makes a big difference. Having the right labeled data for production inference is an important part of your NLP pipeline.
Text Classification
Whatever your needs are, our team of experts will classify your search engine results or perform sentiment analysis for your user feedback. With supervised learning approaches to your project, we deliver accurate text classifications to get it right the first time.
Audio Transcription
We provide large scale transcription for audio files of different frequencies. Using our team’s expertise, we can create an annotated transcript that accurately matches your ML model.
Audio Classification
Listeners identify the content of audio files, classifying them into different categories that are pre-determined or derived from the audio. Topics discussed in an audio, types of content found in audio, like music, news, natural conversation, or background sounds like chatter or nature can be identified.
Handwritten Text Transcription
In order to understand handwritten scripts, optical character recognition (OCR) processors require large datasets of typed text. Machine-learning models benefit from handwritten transcription because it helps them learn how to recognize text across a variety of scripts and styles of writing.
Entity Recognition
Our annotators read large volumes of text in their own language and identify entities such as people’s names, places, references, and much more. By recognizing named entities, machine learning models can understand the content much better and infer more accurately in a production environment.
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