Deiteo AI - Data Labelling Company

Deiteo Text and NLP

Text annotation is the process of creating metadata in the form of labels for text data by tagging keywords, phrases, and sentences suitable for machine learning models

types of Text and NLP Services

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AI NLP and text annotation

OCR Labeling

The procedure used to transform an image of text into a machine-readable text format is known as optical character recognition (OCR). Your computer will store the scan as an image file, for instance, if you scan a form or a receipt. The words in the picture file cannot be edited, searched for, or recorded using a text editor.

Named-Entity Recognition

Named-entity recognition aims to detect and categorize named entities in unstructured text into pre-defined categories such as human names, organizations, places, medical codes, time expressions, quantities, monetary values, percentages, etc. Named Entity Recognition (NER) in NLP classifies unstructured input into specified categories.

AI NLP and text annotation
AI NLP and text annotation

Sentiment Analysis

Sentiment Analysis, often known as opinion mining, is a kind of natural language processing (NLP) that classifies information as positive, negative, or neutral based on the tone of the words used to describe it. Analysis of consumer comments on a company's brand or product may be a gold mine for learning more about what those customers want, therefore it makes sense that many companies turn to sentiment analysis on textual data.

Intent Classification

A sentence is automatically analyzed by an intent classifier and sorted into one of many intent categories (buy, downgrade, unsubscribe, demo request, etc.). Insight into the motivations behind consumer inquiries, process automation, and new discoveries are all made possible by this.

AI NLP and text annotation
AI NLP and text annotation

Text Summarization

The challenge of text summarizing is to convey the essential content of a document while decreasing the number of phrases and words used to express it. Extractive and abstractive approaches may be broadly grouped together to describe the methods used to glean information from raw text material for the purpose of training a summarization model.

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