👋 We will be at South Summit Madrid 8th-10th June. Visit us to see how our platform can help you training your AI Models (Booth 182)
We know every annotation project is slightly different from each other and sometimes a couple of changes on the UI or flow can make a huge difference in labeling performance and dataset quality. M47AI Platform is built with that mind and we continue to integrate our customer’s favorite annotation types in the platform to match every possible NLP Use Case.
Named-Entity Recognition (NER)
NER is the process of identifying and categorizing named entities in a given text. Examples of categories are organizations, locations, time, names, money, and rate.
Part Of Speech (POS Tagging)
POS Tagging is the process of tagging words based on their grammatical, or part of speech, function, depending on the definition of the word and the function that it does in the utterance.
Text Classification (single choice or multiple choice) is the process of assigning a set of predefined categories to an open-ended text or document.
Sentiment Analysis is the process of identifying and extracting subjective information and author's affective state within a given text to determine whether data is positive, negative or neutral.
Entity linking is the process of connecting entity mentions in the text using a predefined set of relational categories.
Question Answering is a process for which annotators define a set of questions that can be answered with specific parts of a given text.
Intent Creation is the process of manually writing user sentences and tag the entities with the aim to achieve a certain goal in a specific domain.
Text Summarization is the process of shortening a set of data, to create a subset (a summary) that represents the most important or relevant information within the original content.
Semantic Textual Similarity
Semantic Textual Similarity deals with determining how similar two pieces of texts are. This can take the form of assigning a score from 1 to 5.
Text entailment is a directional relation between text fragments. The relation holds whenever the truth of one text fragment follows from another text.
Text Intent Recognition
Text Intent Recognition is the task of taking a written or spoken input, and classifying it based on what the user wants to achieve.