Scoring - Text classification

Hi Team,

I want to Score people based on their vocabulary to drive how relevant that person is for that skill

Insights much appreciated.

please point me to good resources


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Hello May I know which technique or method or algorithm are you using please! based on your post has two options the first one can you try using the Ontology or data structure to arrange the data, the second options can you try to use the parameter based on the rule based. Best and luck


I am yet to start but how to generate score ? pls suggest

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First step is data Ingestion It involves importing the required data, and Extracting text from PDF and docs ,database marking the fields with their respective meanings.We tweak or preprocess the resume text data so that it can be used in the further steps.

• spacy’s rule-based matcher engines and components let find the words and phrases we are looking for their relationships Which helps in phrase matching and builds candidate profile and going head we will convert strings of the key words to store in data frame and count the frequency of the skill words in each category and visualize the candidates with the skill frequency.

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Hi there!
As i saw your comment, it was great i did this before for using please read my paper and look at it you will see what i did how to classifiy the documents by SVM and pattern matching.

  1. [ Question classification using support vector machine and pattern matching].
  2. [Pattern-matching based for Arabic question answering: a challenge perspective].
  3. [Combined support vector machine and pattern matching for Arabic Islamic Hadith question classification system].
    Please regarding to these papers, you can see how i classify but i didn’t use the rule based but the way i work same what you have done some.
    And also you can make the relevant between words using the concepts of words, this based on the Ontology how to arrange the words from your documents.
    Please, any suggestion share with me iam interesting in your area.
    Thank you very much!