Text Annotation

rProcess offers high-quality text annotation strategies and services to help deploy AI and machine learning in Medical AI, Finance & Insurance, and Government. Text annotation helps machine learning models understand text data. The data is parsed into categories such as phrases, sentences, and keywords based on project guidelines. Text annotation can be used to improve the performance of search engines, build chatbots, design a question-answering system, help students improve reading comprehension, and facilitate translation of text from one language to another.

rProcess’s text annotation services include sentiment analysis, intent analysis, named entity recognition (NER), natural language processing (NLP), and entity classification. Their team works with clients to calibrate their quality and throughput requirements and builds custom processes to support client needs and integrate with their existing workflows.

Types of Annotation

Text & NLP Annotation

We Annotate the images from an aisle in a store and determine if any items are out of stock on shelves

Keyword tagging for regional language:

Identify and tag the words containing Name, Place, Country name

Text to audio:

Transcription of text into audio for multiple languages

Sentiment Analysis:

Listening to a series of audio clips from various call center conversations. After listening to each clip, will decide the sentiment of the speaker, whether the speaker is Negative or Non-Negative (Positive and Neutral)

Intent Classification:

Customer Chat Service: helping the consumer to find an answer to their problem. a request from a consumer talking to a customer service agent, for that a Chatbot will determine the intent of the consumer. And provide service accordingly.

Utterance Collection:

Audio : We mark the start and stop time of a speech segment in the audio clip. To determine the number of speakers spoken in the audio clip.
Text : Will select the best intent category for the list of service support sentences.

Keyword Tagging:

Text Extractions – Identifying Persons names, Organizations and Addresses, in a manner that could differentiate them from Miscellaneous texts that look like Persons/ organizations/ Addresses
Recipes tagging – text tagging to extract the words (tokens) and phrases that represent actions, ingredients, cookware, timing or temperatures in recipe instruction steps.