The text classification service involves sorting a large array of disparate data following the logic established by the client. For instance, by genre or topic.

Text Classification

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Text Classification for Machine learning and AI systems

Technical assignment template

What goals can you achieve by Text Classification

SERVICES → LABELME.RU

Text classification is necessary for training recommendation and ranking models. For example, a dataset in which articles are sorted by genre can become the basis of an algorithm that selects articles according to user preferences or can automatically recognize the types of documents and structure their location in your databases. The more classes your technical assignment has and the more precisely you define classes, the more accurate such an algorithm will be.

Types of text classification we work with

Document classification

We will process any array of text documents so that you can train the algorithm to recognize and sort your documentation. We have developed our macros for each type of document. They simplify the classification process and minimize the number of errors.

Classification by genre

We'll review your technical specifications or help to form a ranking system by classes for your tasks. From fiction to scientific works. We select a team of performers following the specifics of the texts.

Classification by topic

For the automatic ranking or recommendation algorithm to accurately determine the class of the text, it is crucial to give it as many specific examples as possible. Our specialists will carefully study all provided texts or help to collect them. This way, it'll be easier for your specialist to achieve their goals.

Application examples

The LabelMe team is working on data labeling for a wide variety of businesses:

Computational linguistics

The use of neural networks simplifies the processing and content analysis of large volumes of textual information. For example, a person doesn’t have to manually sort texts by genres, themes, and other parameters. We’ll prepare a dataset using which you can train a neural network and automate the text classification processes.

Anti-spam systems

We have such cases among our orders. The client provided technical specifications with criteria for determining spam messages and datasets. We have developed an effective sorting system. In a short time, we prepared a dataset with which made it possible to determine the types of advertising mailings.

Social media

We helped improve recommendation algorithms and sort posts, articles, and texts by thematic classes. Thanks to this dataset, the customer developed a solution that did not need hashtags. The algorithm was based on the content of certain variables.
For example, for the "sports" section, these variables are "competitions," "championship," "tournament," "national team," and so on. Based on this, the algorithm recommends posts and accounts according to users' preferences.

Banks, accounting, and law

We have extensive experience in labeling various documents for all basic types: passports, birth certificates, TIN, SNILS, driver's licenses, and tax reports of private entrepreneurs and legal entities.
We have our own macros for each type of document. It increases the classification speed without loss of quality. This dataset type will be the key to automating internal processes. Machine performs routine data processing tasks, not a person.