What is Text Classification in Machine Learning?
Text Classification is the process of classifying the texts and assigning tags to natural language texts within the predetermined set of categories. Apart from manual, automated Classification API are also used to categorize the key texts in a document to utilize the important words.
Though, API can do this job automatically, but if you are looking to classify the texts for machine learning or AI, you should go with manually done by the experts. As, the API may not work properly, or fail to classify the different types of keywords in a document.
Text classification machine learning with precision of data is very important to train the AI model for accurate predictions. And if you use API, then an algorithm combines statistical document classification with rule-based filtering, but accuracy would be not as much as it is done manually.
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Text Classification for NLP Dataset
The text classification NLP helps machines to classify the important keywords and perceive them through natural language processing. In NLP the crucial words and sentence needs to be classified or annotated with added metadata so that machine learning algorithm can understate the typescript used by humans while communicating with others in different scenarios.
To train your AI or machine learning model you need right Text classification dataset and it is possible when you hire a professional text annotation service provider. These experts are expert in text classification process using their own cognitive skills and past experiences. With such professionals you can get the best quality training data sets to train your text classification model or for machine learning.
Cogito is providing the data and text classification service for machine learning and AI. It is expert is data annotation with specialization in image annotation services for deep learning. It is working with world class professionals to complete the data classification process with best level of accuracy at lowest price. This article was originally featured on Here
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