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Multi-class string classification

What algorithms should I use to perform job classification based on resume data?Appropriate algorithm for string (not document) classification?CNN Multi-class vs Binary Class Image ClassificationMulti-class classification v.s. Binary classificationEvaluate a model based on precision for multi class classificationEvaluation methods for multi-class classificationMulti Class Classification on large dataset with over 600 classessolving multi-class imbalance classification using smote and OSSHierarchical classification with multi-class predictor for every parent nodeMulti Class Text Classification?

0

$begingroup$

Currently working on Resume Rarser tool using doc2vec.

The main assumption that I take when parsing resume is that each line of text (docx, pdf etc) contains information of one class.

Although there are some cases when one line of text could contain several classes. For example,

2016-2018 University of Glasgow, Nanotechnology

It contains education period, university and specialization.

To fix this, I am able to split the string into bi/trigrams and classify the string correctly.

If I classify the bigrams of normal strings though it gives much worse results.

The question that I have is How to detect this kind of multi-class strings?

text-mining multiclass-classification word2vec

share|improve this question

asked yesterday

GraygoodGraygood

83

$endgroup$

• 
  
  
  

  
  

  
  
  
  
  
  

  
  $begingroup$
  Do the classes just have deterministic and clear category of data? (Ex. name, year, location, etc.)
  $endgroup$
  – alireza zolanvari
  yesterday
  

  
  
  
  


• 
  
  
  

  
  

  
  
  
  
  
  

  
  $begingroup$
  @alirezazolanvari yes, actually text also gets clusterized into info, education, experience etc for better further classification of each cluster
  $endgroup$
  – Graygood
  yesterday
  

  
  
  
  


• 
  
  
  

  
  

  
  
  
  
  
  

  
  $begingroup$
  What I also found, that numbers in such strings as sample take too much 'attention' for classifier.
  $endgroup$
  – Graygood
  yesterday
  

  
  
  
  


• 
  
  
  

  
  

  
  
  
  
  
  

  
  $begingroup$
  According to what I know, all of these clusters have some specific (and not necessarily unique) keywords which can be used in this case (Ex. Education cluster keywords: university, school, etc.)
  $endgroup$
  – alireza zolanvari
  yesterday
  

  
  
  
  

add a comment | 

0

$begingroup$

Currently working on Resume Rarser tool using doc2vec.

The main assumption that I take when parsing resume is that each line of text (docx, pdf etc) contains information of one class.

Although there are some cases when one line of text could contain several classes. For example,

2016-2018 University of Glasgow, Nanotechnology

It contains education period, university and specialization.

To fix this, I am able to split the string into bi/trigrams and classify the string correctly.

If I classify the bigrams of normal strings though it gives much worse results.

The question that I have is How to detect this kind of multi-class strings?

text-mining multiclass-classification word2vec

share|improve this question

asked yesterday

GraygoodGraygood

83

$endgroup$

• 
  
  
  

  
  

  
  
  
  
  
  

  
  $begingroup$
  Do the classes just have deterministic and clear category of data? (Ex. name, year, location, etc.)
  $endgroup$
  – alireza zolanvari
  yesterday
  

  
  
  
  


• 
  
  
  

  
  

  
  
  
  
  
  

  
  $begingroup$
  @alirezazolanvari yes, actually text also gets clusterized into info, education, experience etc for better further classification of each cluster
  $endgroup$
  – Graygood
  yesterday
  

  
  
  
  


• 
  
  
  

  
  

  
  
  
  
  
  

  
  $begingroup$
  What I also found, that numbers in such strings as sample take too much 'attention' for classifier.
  $endgroup$
  – Graygood
  yesterday
  

  
  
  
  


• 
  
  
  

  
  

  
  
  
  
  
  

  
  $begingroup$
  According to what I know, all of these clusters have some specific (and not necessarily unique) keywords which can be used in this case (Ex. Education cluster keywords: university, school, etc.)
  $endgroup$
  – alireza zolanvari
  yesterday
  

  
  
  
  

add a comment | 

$begingroup$

Currently working on Resume Rarser tool using doc2vec.

The main assumption that I take when parsing resume is that each line of text (docx, pdf etc) contains information of one class.

Although there are some cases when one line of text could contain several classes. For example,

2016-2018 University of Glasgow, Nanotechnology

It contains education period, university and specialization.

To fix this, I am able to split the string into bi/trigrams and classify the string correctly.

If I classify the bigrams of normal strings though it gives much worse results.

The question that I have is How to detect this kind of multi-class strings?

text-mining multiclass-classification word2vec

share|improve this question

asked yesterday

GraygoodGraygood

83

$endgroup$

share|improve this question

asked yesterday

GraygoodGraygood

83

share|improve this question

asked yesterday

GraygoodGraygood

83

asked yesterday

GraygoodGraygood

83

asked yesterday

GraygoodGraygood

83