I am new to AI. I am working on an application that text classification via machine learning. The application needs to classify different parts of an HTML document. For example, most webpages have the head, menu, sidebar, footer, main content, etc. I want to use a text classifier to classify these parts of an HTML document, and to identify different types of forms on the page.
It would be very helpful if anyone could provide detailed guidance on this subject.
Examples of a similar application would also be very helpful.
I am looking for more technical suggestions, relating to code & implementation.
I can assign labels to html tag attributes, like class or id
like for first item:
TrainClassifier(label: "Menu", value: "menu-1", attribute: "class", position-in-string: "21%", tag: "div");
"menu-1" (attribute value)
"class" (attribute name)
"21" (tag position in string)
"div" (tag name)
"Menu" (classified as label)
What neural network library, can take the above inputs, and classify them in to labels (i.e. Menu).
All users cannot create regex or XPath, they need more easy approach, so it is important, to make the software intelligent, user can highlight the part of HTML document he/she needs, using web browser control, and train the software till it can work on its own.
but I don't know how to make the software train using AI,
the AI I am looking for is like it should be able to accept various inputs, and classify on the basis of that, as I have already said new to AI, don't know much about it.
It would be helpful to me if I get an answer to the question I have asked, like what library I should use, and how to implement, answers suggesting Xpath or Regex or other methods pls don't answer, it often happens that you get all suggestions but the one you need.