Convention for creating good data set for RASA NER_CRF





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I am trying to create a dataset for training RASA ner_crf for one type of entity. Please let me know the minimum number of sentences/variation_in_sentence_formation for good result. When I have one type of each of the possible sentence NER_CRF is not giving good result.










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    I am trying to create a dataset for training RASA ner_crf for one type of entity. Please let me know the minimum number of sentences/variation_in_sentence_formation for good result. When I have one type of each of the possible sentence NER_CRF is not giving good result.










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      I am trying to create a dataset for training RASA ner_crf for one type of entity. Please let me know the minimum number of sentences/variation_in_sentence_formation for good result. When I have one type of each of the possible sentence NER_CRF is not giving good result.










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      I am trying to create a dataset for training RASA ner_crf for one type of entity. Please let me know the minimum number of sentences/variation_in_sentence_formation for good result. When I have one type of each of the possible sentence NER_CRF is not giving good result.







      rasa-nlu crf ner






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      asked Nov 23 '18 at 13:58









      Surabhi MundraSurabhi Mundra

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          Rasa entity extraction depends heavily on the pipeline you have defined. Also depends on language model and tokenizers. So make sure you use good tokenizer. If it is normal English utterances try using tokenizer_ spacy before ner_crf. Also try with ner_spacy



          As per my experience, 5 to 10 variations of utterances for each case gave a decent result to start with






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            1 Answer
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            Rasa entity extraction depends heavily on the pipeline you have defined. Also depends on language model and tokenizers. So make sure you use good tokenizer. If it is normal English utterances try using tokenizer_ spacy before ner_crf. Also try with ner_spacy



            As per my experience, 5 to 10 variations of utterances for each case gave a decent result to start with






            share|improve this answer




























              2














              Rasa entity extraction depends heavily on the pipeline you have defined. Also depends on language model and tokenizers. So make sure you use good tokenizer. If it is normal English utterances try using tokenizer_ spacy before ner_crf. Also try with ner_spacy



              As per my experience, 5 to 10 variations of utterances for each case gave a decent result to start with






              share|improve this answer


























                2












                2








                2







                Rasa entity extraction depends heavily on the pipeline you have defined. Also depends on language model and tokenizers. So make sure you use good tokenizer. If it is normal English utterances try using tokenizer_ spacy before ner_crf. Also try with ner_spacy



                As per my experience, 5 to 10 variations of utterances for each case gave a decent result to start with






                share|improve this answer













                Rasa entity extraction depends heavily on the pipeline you have defined. Also depends on language model and tokenizers. So make sure you use good tokenizer. If it is normal English utterances try using tokenizer_ spacy before ner_crf. Also try with ner_spacy



                As per my experience, 5 to 10 variations of utterances for each case gave a decent result to start with







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                answered Nov 24 '18 at 2:07









                Karthik SunilKarthik Sunil

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