Machine learning or NLP approach to convert string about month ,year into datesDate Extraction in Pythonextract calendar event information from unstructured textWhy are NLP and Machine Learning communities interested in deep learning?Aspect based sentiment analysis using machine learning approachStackOverflow Tags Predictor…Suggest an Machine Learning Approach please?Confused about the different aspects in Machine LearningMachine learning - Algorithm suggestion for my problem using NLPCommercial Software for Interactive Machine Learning and Annotation in NLPImproving automated ingestion system using Machine Learning and/or NLPConverting dates to appropriate form to train machine learning modelforecast product demand in one week using machine learning approach

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Machine learning or NLP approach to convert string about month ,year into dates


Date Extraction in Pythonextract calendar event information from unstructured textWhy are NLP and Machine Learning communities interested in deep learning?Aspect based sentiment analysis using machine learning approachStackOverflow Tags Predictor…Suggest an Machine Learning Approach please?Confused about the different aspects in Machine LearningMachine learning - Algorithm suggestion for my problem using NLPCommercial Software for Interactive Machine Learning and Annotation in NLPImproving automated ingestion system using Machine Learning and/or NLPConverting dates to appropriate form to train machine learning modelforecast product demand in one week using machine learning approach













3












$begingroup$


I'm currently in the process of developing a program with the capability of converting human style of representing year into actual dates.
Example : last year last month into December 2018
string may be complete sentence like : what were you doing 5 years ago



it will gives 2014



The purpose is to evalute human style of represting year or date into actual date, i have created collection of this type of strings and matching them with regex.



I have read some machine learning but I'm not sure which algorithm suits this problem the best or if I should consider using NLP.



Does anyone have a suggestion of what algorithm to use or where I can find the necessary literature to solve my problem?



Thanks for any contribution!










share|improve this question









$endgroup$




bumped to the homepage by Community 4 mins ago


This question has answers that may be good or bad; the system has marked it active so that they can be reviewed.










  • 1




    $begingroup$
    See : datascience.stackexchange.com/questions/45854/…
    $endgroup$
    – Shamit Verma
    Feb 20 at 6:34















3












$begingroup$


I'm currently in the process of developing a program with the capability of converting human style of representing year into actual dates.
Example : last year last month into December 2018
string may be complete sentence like : what were you doing 5 years ago



it will gives 2014



The purpose is to evalute human style of represting year or date into actual date, i have created collection of this type of strings and matching them with regex.



I have read some machine learning but I'm not sure which algorithm suits this problem the best or if I should consider using NLP.



Does anyone have a suggestion of what algorithm to use or where I can find the necessary literature to solve my problem?



Thanks for any contribution!










share|improve this question









$endgroup$




bumped to the homepage by Community 4 mins ago


This question has answers that may be good or bad; the system has marked it active so that they can be reviewed.










  • 1




    $begingroup$
    See : datascience.stackexchange.com/questions/45854/…
    $endgroup$
    – Shamit Verma
    Feb 20 at 6:34













3












3








3


2



$begingroup$


I'm currently in the process of developing a program with the capability of converting human style of representing year into actual dates.
Example : last year last month into December 2018
string may be complete sentence like : what were you doing 5 years ago



it will gives 2014



The purpose is to evalute human style of represting year or date into actual date, i have created collection of this type of strings and matching them with regex.



I have read some machine learning but I'm not sure which algorithm suits this problem the best or if I should consider using NLP.



Does anyone have a suggestion of what algorithm to use or where I can find the necessary literature to solve my problem?



Thanks for any contribution!










share|improve this question









$endgroup$




I'm currently in the process of developing a program with the capability of converting human style of representing year into actual dates.
Example : last year last month into December 2018
string may be complete sentence like : what were you doing 5 years ago



it will gives 2014



The purpose is to evalute human style of represting year or date into actual date, i have created collection of this type of strings and matching them with regex.



I have read some machine learning but I'm not sure which algorithm suits this problem the best or if I should consider using NLP.



Does anyone have a suggestion of what algorithm to use or where I can find the necessary literature to solve my problem?



Thanks for any contribution!







machine-learning python nlp nltk regex






share|improve this question













share|improve this question











share|improve this question




share|improve this question










asked Feb 20 at 6:30









bipul kumarbipul kumar

214




214





bumped to the homepage by Community 4 mins ago


This question has answers that may be good or bad; the system has marked it active so that they can be reviewed.







bumped to the homepage by Community 4 mins ago


This question has answers that may be good or bad; the system has marked it active so that they can be reviewed.









  • 1




    $begingroup$
    See : datascience.stackexchange.com/questions/45854/…
    $endgroup$
    – Shamit Verma
    Feb 20 at 6:34












  • 1




    $begingroup$
    See : datascience.stackexchange.com/questions/45854/…
    $endgroup$
    – Shamit Verma
    Feb 20 at 6:34







1




1




$begingroup$
See : datascience.stackexchange.com/questions/45854/…
$endgroup$
– Shamit Verma
Feb 20 at 6:34




$begingroup$
See : datascience.stackexchange.com/questions/45854/…
$endgroup$
– Shamit Verma
Feb 20 at 6:34










3 Answers
3






active

oldest

votes


















0












$begingroup$

What you need to look for is called "Named Entity recognition". From Wikipedia




Named-entity recognition (NER) (also known as entity identification,
entity chunking and entity extraction) is a subtask of information
extraction that seeks to locate and classify named entity mentions in
unstructured text into pre-defined categories such as the person
names, organizations, locations, medical codes, time expressions,
quantities, monetary values, percentages, etc.




As it is mentioned in the comments section, Stanford has a great NER Tagger and you could use that together with Python (even if the StanfordNLP is implemented in Java)



Download the jar file from the official url. It has this format stanford-ner-xxxx-xx-xx.zip



You need to put the following two files in the same application folder as your Python script



  1. ner-tagger.jar

  2. ner-model-english.ser.gz (choose another one if you don't want English)


import nltk

from nltk.tag.stanford import StanfordNERTagger

yourText = this_is_your_text

words = nltk.word_tokenize(yourText)
jar = './stanford-ner.jar'
model = './ner-model-english.ser.gz'

tagger = StanfordNERTagger(model, jar, encoding='utf8')

print(ner_tagger.tag(words))


Then you can grab from the above, anything that is tagged as DATE






share|improve this answer









$endgroup$












  • $begingroup$
    ner-model-english.ser.gz is not present in that zip and i am trying to use other file from classifier (english.all.3class.distsim.crf.ser.gz) but i always stuck with following error: TypeError: Can't instantiate abstract class StanfordTagger with abstract methods _cmd
    $endgroup$
    – bipul kumar
    Feb 21 at 7:46



















0












$begingroup$

Sounds like you need a temporal tagger. This is a good rule-based one https://github.com/HeidelTime/heideltime



Stanford CoreNLP also has one https://nlp.stanford.edu/software/sutime.html



It seems like generally rule-based approaches work well for this task.






share|improve this answer









$endgroup$




















    0












    $begingroup$

    I got my answer , NLTK is good to go for this problem.
    You may use sutime with python wrapper :



    Python wrapper for Stanford CoreNLP's SUTime



    The usual approach in NLP is to collect a dataset required for training. Process that dataset so that the words in the dataset are converted into numbers.



    One simple example of converting it into numbers is to make a large dictionary of words from the dataset and use the index of each word in the dictionary as the representing number






    share|improve this answer









    $endgroup$












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      3 Answers
      3






      active

      oldest

      votes








      3 Answers
      3






      active

      oldest

      votes









      active

      oldest

      votes






      active

      oldest

      votes









      0












      $begingroup$

      What you need to look for is called "Named Entity recognition". From Wikipedia




      Named-entity recognition (NER) (also known as entity identification,
      entity chunking and entity extraction) is a subtask of information
      extraction that seeks to locate and classify named entity mentions in
      unstructured text into pre-defined categories such as the person
      names, organizations, locations, medical codes, time expressions,
      quantities, monetary values, percentages, etc.




      As it is mentioned in the comments section, Stanford has a great NER Tagger and you could use that together with Python (even if the StanfordNLP is implemented in Java)



      Download the jar file from the official url. It has this format stanford-ner-xxxx-xx-xx.zip



      You need to put the following two files in the same application folder as your Python script



      1. ner-tagger.jar

      2. ner-model-english.ser.gz (choose another one if you don't want English)


      import nltk

      from nltk.tag.stanford import StanfordNERTagger

      yourText = this_is_your_text

      words = nltk.word_tokenize(yourText)
      jar = './stanford-ner.jar'
      model = './ner-model-english.ser.gz'

      tagger = StanfordNERTagger(model, jar, encoding='utf8')

      print(ner_tagger.tag(words))


      Then you can grab from the above, anything that is tagged as DATE






      share|improve this answer









      $endgroup$












      • $begingroup$
        ner-model-english.ser.gz is not present in that zip and i am trying to use other file from classifier (english.all.3class.distsim.crf.ser.gz) but i always stuck with following error: TypeError: Can't instantiate abstract class StanfordTagger with abstract methods _cmd
        $endgroup$
        – bipul kumar
        Feb 21 at 7:46
















      0












      $begingroup$

      What you need to look for is called "Named Entity recognition". From Wikipedia




      Named-entity recognition (NER) (also known as entity identification,
      entity chunking and entity extraction) is a subtask of information
      extraction that seeks to locate and classify named entity mentions in
      unstructured text into pre-defined categories such as the person
      names, organizations, locations, medical codes, time expressions,
      quantities, monetary values, percentages, etc.




      As it is mentioned in the comments section, Stanford has a great NER Tagger and you could use that together with Python (even if the StanfordNLP is implemented in Java)



      Download the jar file from the official url. It has this format stanford-ner-xxxx-xx-xx.zip



      You need to put the following two files in the same application folder as your Python script



      1. ner-tagger.jar

      2. ner-model-english.ser.gz (choose another one if you don't want English)


      import nltk

      from nltk.tag.stanford import StanfordNERTagger

      yourText = this_is_your_text

      words = nltk.word_tokenize(yourText)
      jar = './stanford-ner.jar'
      model = './ner-model-english.ser.gz'

      tagger = StanfordNERTagger(model, jar, encoding='utf8')

      print(ner_tagger.tag(words))


      Then you can grab from the above, anything that is tagged as DATE






      share|improve this answer









      $endgroup$












      • $begingroup$
        ner-model-english.ser.gz is not present in that zip and i am trying to use other file from classifier (english.all.3class.distsim.crf.ser.gz) but i always stuck with following error: TypeError: Can't instantiate abstract class StanfordTagger with abstract methods _cmd
        $endgroup$
        – bipul kumar
        Feb 21 at 7:46














      0












      0








      0





      $begingroup$

      What you need to look for is called "Named Entity recognition". From Wikipedia




      Named-entity recognition (NER) (also known as entity identification,
      entity chunking and entity extraction) is a subtask of information
      extraction that seeks to locate and classify named entity mentions in
      unstructured text into pre-defined categories such as the person
      names, organizations, locations, medical codes, time expressions,
      quantities, monetary values, percentages, etc.




      As it is mentioned in the comments section, Stanford has a great NER Tagger and you could use that together with Python (even if the StanfordNLP is implemented in Java)



      Download the jar file from the official url. It has this format stanford-ner-xxxx-xx-xx.zip



      You need to put the following two files in the same application folder as your Python script



      1. ner-tagger.jar

      2. ner-model-english.ser.gz (choose another one if you don't want English)


      import nltk

      from nltk.tag.stanford import StanfordNERTagger

      yourText = this_is_your_text

      words = nltk.word_tokenize(yourText)
      jar = './stanford-ner.jar'
      model = './ner-model-english.ser.gz'

      tagger = StanfordNERTagger(model, jar, encoding='utf8')

      print(ner_tagger.tag(words))


      Then you can grab from the above, anything that is tagged as DATE






      share|improve this answer









      $endgroup$



      What you need to look for is called "Named Entity recognition". From Wikipedia




      Named-entity recognition (NER) (also known as entity identification,
      entity chunking and entity extraction) is a subtask of information
      extraction that seeks to locate and classify named entity mentions in
      unstructured text into pre-defined categories such as the person
      names, organizations, locations, medical codes, time expressions,
      quantities, monetary values, percentages, etc.




      As it is mentioned in the comments section, Stanford has a great NER Tagger and you could use that together with Python (even if the StanfordNLP is implemented in Java)



      Download the jar file from the official url. It has this format stanford-ner-xxxx-xx-xx.zip



      You need to put the following two files in the same application folder as your Python script



      1. ner-tagger.jar

      2. ner-model-english.ser.gz (choose another one if you don't want English)


      import nltk

      from nltk.tag.stanford import StanfordNERTagger

      yourText = this_is_your_text

      words = nltk.word_tokenize(yourText)
      jar = './stanford-ner.jar'
      model = './ner-model-english.ser.gz'

      tagger = StanfordNERTagger(model, jar, encoding='utf8')

      print(ner_tagger.tag(words))


      Then you can grab from the above, anything that is tagged as DATE







      share|improve this answer












      share|improve this answer



      share|improve this answer










      answered Feb 20 at 10:43









      TasosTasos

      1,005631




      1,005631











      • $begingroup$
        ner-model-english.ser.gz is not present in that zip and i am trying to use other file from classifier (english.all.3class.distsim.crf.ser.gz) but i always stuck with following error: TypeError: Can't instantiate abstract class StanfordTagger with abstract methods _cmd
        $endgroup$
        – bipul kumar
        Feb 21 at 7:46

















      • $begingroup$
        ner-model-english.ser.gz is not present in that zip and i am trying to use other file from classifier (english.all.3class.distsim.crf.ser.gz) but i always stuck with following error: TypeError: Can't instantiate abstract class StanfordTagger with abstract methods _cmd
        $endgroup$
        – bipul kumar
        Feb 21 at 7:46
















      $begingroup$
      ner-model-english.ser.gz is not present in that zip and i am trying to use other file from classifier (english.all.3class.distsim.crf.ser.gz) but i always stuck with following error: TypeError: Can't instantiate abstract class StanfordTagger with abstract methods _cmd
      $endgroup$
      – bipul kumar
      Feb 21 at 7:46





      $begingroup$
      ner-model-english.ser.gz is not present in that zip and i am trying to use other file from classifier (english.all.3class.distsim.crf.ser.gz) but i always stuck with following error: TypeError: Can't instantiate abstract class StanfordTagger with abstract methods _cmd
      $endgroup$
      – bipul kumar
      Feb 21 at 7:46












      0












      $begingroup$

      Sounds like you need a temporal tagger. This is a good rule-based one https://github.com/HeidelTime/heideltime



      Stanford CoreNLP also has one https://nlp.stanford.edu/software/sutime.html



      It seems like generally rule-based approaches work well for this task.






      share|improve this answer









      $endgroup$

















        0












        $begingroup$

        Sounds like you need a temporal tagger. This is a good rule-based one https://github.com/HeidelTime/heideltime



        Stanford CoreNLP also has one https://nlp.stanford.edu/software/sutime.html



        It seems like generally rule-based approaches work well for this task.






        share|improve this answer









        $endgroup$















          0












          0








          0





          $begingroup$

          Sounds like you need a temporal tagger. This is a good rule-based one https://github.com/HeidelTime/heideltime



          Stanford CoreNLP also has one https://nlp.stanford.edu/software/sutime.html



          It seems like generally rule-based approaches work well for this task.






          share|improve this answer









          $endgroup$



          Sounds like you need a temporal tagger. This is a good rule-based one https://github.com/HeidelTime/heideltime



          Stanford CoreNLP also has one https://nlp.stanford.edu/software/sutime.html



          It seems like generally rule-based approaches work well for this task.







          share|improve this answer












          share|improve this answer



          share|improve this answer










          answered Feb 20 at 14:36









          Igor BrigadirIgor Brigadir

          263




          263





















              0












              $begingroup$

              I got my answer , NLTK is good to go for this problem.
              You may use sutime with python wrapper :



              Python wrapper for Stanford CoreNLP's SUTime



              The usual approach in NLP is to collect a dataset required for training. Process that dataset so that the words in the dataset are converted into numbers.



              One simple example of converting it into numbers is to make a large dictionary of words from the dataset and use the index of each word in the dictionary as the representing number






              share|improve this answer









              $endgroup$

















                0












                $begingroup$

                I got my answer , NLTK is good to go for this problem.
                You may use sutime with python wrapper :



                Python wrapper for Stanford CoreNLP's SUTime



                The usual approach in NLP is to collect a dataset required for training. Process that dataset so that the words in the dataset are converted into numbers.



                One simple example of converting it into numbers is to make a large dictionary of words from the dataset and use the index of each word in the dictionary as the representing number






                share|improve this answer









                $endgroup$















                  0












                  0








                  0





                  $begingroup$

                  I got my answer , NLTK is good to go for this problem.
                  You may use sutime with python wrapper :



                  Python wrapper for Stanford CoreNLP's SUTime



                  The usual approach in NLP is to collect a dataset required for training. Process that dataset so that the words in the dataset are converted into numbers.



                  One simple example of converting it into numbers is to make a large dictionary of words from the dataset and use the index of each word in the dictionary as the representing number






                  share|improve this answer









                  $endgroup$



                  I got my answer , NLTK is good to go for this problem.
                  You may use sutime with python wrapper :



                  Python wrapper for Stanford CoreNLP's SUTime



                  The usual approach in NLP is to collect a dataset required for training. Process that dataset so that the words in the dataset are converted into numbers.



                  One simple example of converting it into numbers is to make a large dictionary of words from the dataset and use the index of each word in the dictionary as the representing number







                  share|improve this answer












                  share|improve this answer



                  share|improve this answer










                  answered Feb 22 at 3:05









                  bipul kumarbipul kumar

                  214




                  214



























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                      ValueError: Expected n_neighbors <= n_samples, but n_samples = 1, n_neighbors = 6 (SMOTE) The 2019 Stack Overflow Developer Survey Results Are InCan SMOTE be applied over sequence of words (sentences)?ValueError when doing validation with random forestsSMOTE and multi class oversamplingLogic behind SMOTE-NC?ValueError: Error when checking target: expected dense_1 to have shape (7,) but got array with shape (1,)SmoteBoost: Should SMOTE be ran individually for each iteration/tree in the boosting?solving multi-class imbalance classification using smote and OSSUsing SMOTE for Synthetic Data generation to improve performance on unbalanced dataproblem of entry format for a simple model in KerasSVM SMOTE fit_resample() function runs forever with no result