Examples of using Text classification in English and their translations into Spanish
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Then, text classification was performed.
Highlights of our Text Classification API.
Register for the webinar andlearn how to customize your text classification!
In this way, the Text Classification API can categorize texts according to such categories.
C library for modeling,Information Retrieval and Text Classification.
Text classification was carried out with this model, and messages with no category assigned were removed.
Using of context vectors in text classification.
Text Classification assigns one or more categories to a text to facilitate its management, allowing to filter, sort, or group texts. .
When to use Deep Categorization instead of Text Classification?
The classification models of MeaningCloud's Text Classification API combine a statistical model and/or classification rules.
Try it in our demonstrator, using the Text Classification API test console or read the supported models' documentation.
What kind of classification can I carry out with the Text Classification API?
We began by presenting how to do text classification with MeaningCloud and why it is necessary to develop models that are adapted to each specific application scenario.
Resources in an Uniform Way for Text Classification Tasks.
Our Text Classification API supports IAB's standard contextual taxonomy, enabling content tagging in compliance with this model in large volumes and with great speed, and easing the participation in the new online advertising ecosystem.
We have increased our set of predefined models for the Text Classification API.
MeaningCloud provides categorization functionalities through its Text Classification API, which offers different predefined and standard classification models e.g.
They typically use bag of words features to identify spam e-mail,an approach commonly used in text classification.
Sentiment analysis, extraction of entities, text classification, content moderation.
It consists of an add-in integrated into Microsoft Excel that can offer perform sentiment analysis and text classification, among other tasks.
In MeaningCloud, we integrated the IAB taxonomy in the Text Classification API in 2015: check our demo to evaluate the automatic classification capabilities of the tool.
Use instead our Solution for Semantic Publishing,featuring APIs like Topics Extraction, Text Classification and Automatic Summarization.
The Text Clustering API complements the abilities of the Topics Extraction and Text Classification ones(which employ predefined taxonomies and dictionaries), providing more flexible and dynamic analytics and enabling to discover meaningful subjects and unexpected relations between documents.
Get free access to each and every one of the APIs:topics extraction, text classification, sentiment analysis and many more!
Other functionalities like language recognition and text classification help us to clean the noisy streams of comments.
Users frequently ask us through our support line how to perform text classification according to application-specific taxonomies.
If you would like to identify what organization's departments are mentioned, the text classification model must include particular categories representing each department.
The information generated will be valuable,since it will enable the development of more accurate text classification models that help to identify the roots and the consequences of the problem.
Or, if you want to identify which department of a company is being mentioned, the text classification model should feature particular categories representing each department.