Convention for creating good data set for RASA NER_CRF
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
asked Nov 23 '18 at 13:58
Surabhi MundraSurabhi Mundra
1468
add a comment |
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
asked Nov 23 '18 at 13:58
Surabhi MundraSurabhi Mundra
1468
add a comment |
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
asked Nov 23 '18 at 13:58
Surabhi MundraSurabhi Mundra
1468
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
rasa-nlu crf ner
asked Nov 23 '18 at 13:58
Surabhi MundraSurabhi Mundra
1468
asked Nov 23 '18 at 13:58
Surabhi MundraSurabhi Mundra
1468
asked Nov 23 '18 at 13:58
Surabhi MundraSurabhi Mundra
1468
asked Nov 23 '18 at 13:58
Surabhi MundraSurabhi Mundra
1468
asked Nov 23 '18 at 13:58
Surabhi MundraSurabhi Mundra
1468
1468
add a comment |
add a comment |
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
answered Nov 24 '18 at 2:07
Karthik SunilKarthik Sunil
10117
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1 Answer
1
active
oldest
votes
1 Answer
1
active
oldest
votes
active
oldest
votes
active
oldest
votes
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
answered Nov 24 '18 at 2:07
Karthik SunilKarthik Sunil
10117
add a comment |
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
answered Nov 24 '18 at 2:07
Karthik SunilKarthik Sunil
10117
add a comment |
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
answered Nov 24 '18 at 2:07
Karthik SunilKarthik Sunil
10117
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
answered Nov 24 '18 at 2:07
Karthik SunilKarthik Sunil
10117
answered Nov 24 '18 at 2:07
Karthik SunilKarthik Sunil
10117
answered Nov 24 '18 at 2:07
Karthik SunilKarthik Sunil
10117
answered Nov 24 '18 at 2:07
Karthik SunilKarthik Sunil
10117
10117
add a comment |
add a comment |
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