The text categorization service classifies text into different categories according to a predefined taxonomy or dictionary. What differentiates the Bitext platform is the fact that our analysis categorizes noun phrases, adjectival phrases or verbal phrases in context, beyond isolated keywords. You can ignore “like” when acting as a conjunction “This flavour is like the rest” and focus on when it works as a verb “I like this flavour”.
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Do you want to classify text to use it for monthly reports, to trigger workflows, build better chatbots or automatize verbatim codification?
Bitext Text Categorization offer you categorization beyond isolated keywords.

How does Text Categorization work?

For a reliable categorization process, our tool uses first Deep Linguistic Analysis to detect entities, concepts and verb phrases (e.g. “Barack Obama”, “global warming”, “increase in prices”, “took off”). The linguistic representation of the text is then checked against an user build dictionary containing the taxonomy. When a word or phrase in the text corresponds to a dictionary entry, the category for that entry is assigned to the text.

Categorization Example

In the domain of mobile phones, a typical example of categorization will take into account concepts such as “screen”, “case”, “cover”, “camera”, “battery” which all belong to the PRODUCT category as nouns only.

Therefore, sentences like “I love the screen on my new Kindle Fire” or “I’ve bought a great new cover for my iPad” will be classified as belonging to the PRODUCT category.

However, sentences like “I hate it when they screen my iPad at security” or “I hope they’re going to cover the new Galaxy Tab in next week’s review” do not, because “screen” and “cover” are analyzed as verbs.

Creating your dictionary for text categorization

The categorization service works with a user-supplied taxonomy, but sometimes there is no pre-existing dictionary or thesaurus of categories that can be easily integrated.

But we can also help with that: our concept and entity extraction services can be used to analyze documents belonging to the target domain in order to boot-strap the taxonomy building process. By extracting the most relevant concepts, entities, and verb phrases from a corpus of documents, the process of assigning rules to categories can be significantly reduced.

You can also account on our expertise to help you with the creation of your dictionary trough our linguistic consultancy services.

Does it works trough Boolean operators?

No. Our approach goes beyond keyword matching so you will be able to create simple but accurate rules that will substitute the AND, OR and NOT operators. And forget about the NEAR as it is handled by our parsing engine.

Writing reports with Text Categorization

Do you have to write monthly reports managing loads of data? We know this can be very time and resource consuming. Text Categorization can help you extracting only the information you are interested in and dividing that data into the different categories you have pre-defined.

Check out a real example and see how easy is your data understandable:

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