RASA NLU: Can't extract entity











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I've trained my rasa nlu model in a way that It recognizes the content in between square brackets as pst entity. For the training part, I had covered both the scenarios with more than 50 examples.





There are two scenarios(only space difference):




  1. When I pass http://www.google.comm, 1283923, [9283911,9309212,9283238], it is considering only [ bracket as the pst entity.


  2. When I pass http://www.google.comm, 1283923, [9283911, 9309212, 9283238], it is working fine and recognizing [9283911, 9309212, 9283238] as the pst entity as expected.





For the scenario 1, I've tried all the possible pipelines, but it only recognizes the first square bracket [ as the pst entity



In the response, I am getting this output:



{
'intent': {
'name': None,
'confidence': 0.0
},
'entities': [
{
'start': 0,
'end': 22,
'value': 'http://www.google.comm',
'entity': 'url',
'confidence': 0.8052099168500071,
'extractor': 'ner_crf'
},
{
'start': 24,
'end': 31,
'value': '1283923',
'entity': 'defect_id',
'confidence': 0.8334249141074151,
'extractor': 'ner_crf'
},
{
'start': 33,
'end': 34,
'value': '[',
'entity': 'pst',
'confidence': 0.5615805162522188,
'extractor': 'ner_crf'
}
],
'intent_ranking': ,
'text': 'http://www.google.comm, 1283923, [9283911,9309212,9283238]'
}




So, Can anyone tell me what I am missing in the configuration? The problem is happening because of spacing only, and my model should have the knowledge of spacing as I am providing the training data with both scenarios.










share|improve this question






















  • What pipeline, specifically, what tokenizer generated the above? What makes you think you need NLP rather than just a regex pattern matcher?
    – Caleb Keller
    Nov 10 at 16:25










  • I am running nlu for different intents and entities too, so I want to use only rasa nlu for this project.
    – abhishake
    Nov 12 at 13:11










  • I am using spacy_sklearn pipeline only. should I use any other piepline for extraction?
    – abhishake
    Nov 12 at 13:13















up vote
0
down vote

favorite












I've trained my rasa nlu model in a way that It recognizes the content in between square brackets as pst entity. For the training part, I had covered both the scenarios with more than 50 examples.





There are two scenarios(only space difference):




  1. When I pass http://www.google.comm, 1283923, [9283911,9309212,9283238], it is considering only [ bracket as the pst entity.


  2. When I pass http://www.google.comm, 1283923, [9283911, 9309212, 9283238], it is working fine and recognizing [9283911, 9309212, 9283238] as the pst entity as expected.





For the scenario 1, I've tried all the possible pipelines, but it only recognizes the first square bracket [ as the pst entity



In the response, I am getting this output:



{
'intent': {
'name': None,
'confidence': 0.0
},
'entities': [
{
'start': 0,
'end': 22,
'value': 'http://www.google.comm',
'entity': 'url',
'confidence': 0.8052099168500071,
'extractor': 'ner_crf'
},
{
'start': 24,
'end': 31,
'value': '1283923',
'entity': 'defect_id',
'confidence': 0.8334249141074151,
'extractor': 'ner_crf'
},
{
'start': 33,
'end': 34,
'value': '[',
'entity': 'pst',
'confidence': 0.5615805162522188,
'extractor': 'ner_crf'
}
],
'intent_ranking': ,
'text': 'http://www.google.comm, 1283923, [9283911,9309212,9283238]'
}




So, Can anyone tell me what I am missing in the configuration? The problem is happening because of spacing only, and my model should have the knowledge of spacing as I am providing the training data with both scenarios.










share|improve this question






















  • What pipeline, specifically, what tokenizer generated the above? What makes you think you need NLP rather than just a regex pattern matcher?
    – Caleb Keller
    Nov 10 at 16:25










  • I am running nlu for different intents and entities too, so I want to use only rasa nlu for this project.
    – abhishake
    Nov 12 at 13:11










  • I am using spacy_sklearn pipeline only. should I use any other piepline for extraction?
    – abhishake
    Nov 12 at 13:13













up vote
0
down vote

favorite









up vote
0
down vote

favorite











I've trained my rasa nlu model in a way that It recognizes the content in between square brackets as pst entity. For the training part, I had covered both the scenarios with more than 50 examples.





There are two scenarios(only space difference):




  1. When I pass http://www.google.comm, 1283923, [9283911,9309212,9283238], it is considering only [ bracket as the pst entity.


  2. When I pass http://www.google.comm, 1283923, [9283911, 9309212, 9283238], it is working fine and recognizing [9283911, 9309212, 9283238] as the pst entity as expected.





For the scenario 1, I've tried all the possible pipelines, but it only recognizes the first square bracket [ as the pst entity



In the response, I am getting this output:



{
'intent': {
'name': None,
'confidence': 0.0
},
'entities': [
{
'start': 0,
'end': 22,
'value': 'http://www.google.comm',
'entity': 'url',
'confidence': 0.8052099168500071,
'extractor': 'ner_crf'
},
{
'start': 24,
'end': 31,
'value': '1283923',
'entity': 'defect_id',
'confidence': 0.8334249141074151,
'extractor': 'ner_crf'
},
{
'start': 33,
'end': 34,
'value': '[',
'entity': 'pst',
'confidence': 0.5615805162522188,
'extractor': 'ner_crf'
}
],
'intent_ranking': ,
'text': 'http://www.google.comm, 1283923, [9283911,9309212,9283238]'
}




So, Can anyone tell me what I am missing in the configuration? The problem is happening because of spacing only, and my model should have the knowledge of spacing as I am providing the training data with both scenarios.










share|improve this question













I've trained my rasa nlu model in a way that It recognizes the content in between square brackets as pst entity. For the training part, I had covered both the scenarios with more than 50 examples.





There are two scenarios(only space difference):




  1. When I pass http://www.google.comm, 1283923, [9283911,9309212,9283238], it is considering only [ bracket as the pst entity.


  2. When I pass http://www.google.comm, 1283923, [9283911, 9309212, 9283238], it is working fine and recognizing [9283911, 9309212, 9283238] as the pst entity as expected.





For the scenario 1, I've tried all the possible pipelines, but it only recognizes the first square bracket [ as the pst entity



In the response, I am getting this output:



{
'intent': {
'name': None,
'confidence': 0.0
},
'entities': [
{
'start': 0,
'end': 22,
'value': 'http://www.google.comm',
'entity': 'url',
'confidence': 0.8052099168500071,
'extractor': 'ner_crf'
},
{
'start': 24,
'end': 31,
'value': '1283923',
'entity': 'defect_id',
'confidence': 0.8334249141074151,
'extractor': 'ner_crf'
},
{
'start': 33,
'end': 34,
'value': '[',
'entity': 'pst',
'confidence': 0.5615805162522188,
'extractor': 'ner_crf'
}
],
'intent_ranking': ,
'text': 'http://www.google.comm, 1283923, [9283911,9309212,9283238]'
}




So, Can anyone tell me what I am missing in the configuration? The problem is happening because of spacing only, and my model should have the knowledge of spacing as I am providing the training data with both scenarios.







rasa-nlu






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share|improve this question










asked Nov 10 at 15:06









abhishake

14519




14519












  • What pipeline, specifically, what tokenizer generated the above? What makes you think you need NLP rather than just a regex pattern matcher?
    – Caleb Keller
    Nov 10 at 16:25










  • I am running nlu for different intents and entities too, so I want to use only rasa nlu for this project.
    – abhishake
    Nov 12 at 13:11










  • I am using spacy_sklearn pipeline only. should I use any other piepline for extraction?
    – abhishake
    Nov 12 at 13:13


















  • What pipeline, specifically, what tokenizer generated the above? What makes you think you need NLP rather than just a regex pattern matcher?
    – Caleb Keller
    Nov 10 at 16:25










  • I am running nlu for different intents and entities too, so I want to use only rasa nlu for this project.
    – abhishake
    Nov 12 at 13:11










  • I am using spacy_sklearn pipeline only. should I use any other piepline for extraction?
    – abhishake
    Nov 12 at 13:13
















What pipeline, specifically, what tokenizer generated the above? What makes you think you need NLP rather than just a regex pattern matcher?
– Caleb Keller
Nov 10 at 16:25




What pipeline, specifically, what tokenizer generated the above? What makes you think you need NLP rather than just a regex pattern matcher?
– Caleb Keller
Nov 10 at 16:25












I am running nlu for different intents and entities too, so I want to use only rasa nlu for this project.
– abhishake
Nov 12 at 13:11




I am running nlu for different intents and entities too, so I want to use only rasa nlu for this project.
– abhishake
Nov 12 at 13:11












I am using spacy_sklearn pipeline only. should I use any other piepline for extraction?
– abhishake
Nov 12 at 13:13




I am using spacy_sklearn pipeline only. should I use any other piepline for extraction?
– abhishake
Nov 12 at 13:13












1 Answer
1






active

oldest

votes

















up vote
0
down vote













It is good idea to use Regex for your purpose. Rasa NLU supports extraction of Entities by Regex. Normal NLU training data will have something like below



{
"rasa_nlu_data": {
"common_examples": [
{
"text": "Hi",
"intent": "greet",
"entities":
}]
}
}


You can provide Regex data for training as below in the NLU json file.



{
"rasa_nlu_data": {
"regex_features": [
{
"name": "pst",
"pattern": "[..*]"
},
]
}
}


Reference: Regular Expression in Rasal NLU






share|improve this answer





















  • I've tried this solution, but unfortunately, it doesn't make any difference in the output.
    – abhishake
    Nov 12 at 16:26











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1 Answer
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oldest

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1 Answer
1






active

oldest

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active

oldest

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active

oldest

votes








up vote
0
down vote













It is good idea to use Regex for your purpose. Rasa NLU supports extraction of Entities by Regex. Normal NLU training data will have something like below



{
"rasa_nlu_data": {
"common_examples": [
{
"text": "Hi",
"intent": "greet",
"entities":
}]
}
}


You can provide Regex data for training as below in the NLU json file.



{
"rasa_nlu_data": {
"regex_features": [
{
"name": "pst",
"pattern": "[..*]"
},
]
}
}


Reference: Regular Expression in Rasal NLU






share|improve this answer





















  • I've tried this solution, but unfortunately, it doesn't make any difference in the output.
    – abhishake
    Nov 12 at 16:26















up vote
0
down vote













It is good idea to use Regex for your purpose. Rasa NLU supports extraction of Entities by Regex. Normal NLU training data will have something like below



{
"rasa_nlu_data": {
"common_examples": [
{
"text": "Hi",
"intent": "greet",
"entities":
}]
}
}


You can provide Regex data for training as below in the NLU json file.



{
"rasa_nlu_data": {
"regex_features": [
{
"name": "pst",
"pattern": "[..*]"
},
]
}
}


Reference: Regular Expression in Rasal NLU






share|improve this answer





















  • I've tried this solution, but unfortunately, it doesn't make any difference in the output.
    – abhishake
    Nov 12 at 16:26













up vote
0
down vote










up vote
0
down vote









It is good idea to use Regex for your purpose. Rasa NLU supports extraction of Entities by Regex. Normal NLU training data will have something like below



{
"rasa_nlu_data": {
"common_examples": [
{
"text": "Hi",
"intent": "greet",
"entities":
}]
}
}


You can provide Regex data for training as below in the NLU json file.



{
"rasa_nlu_data": {
"regex_features": [
{
"name": "pst",
"pattern": "[..*]"
},
]
}
}


Reference: Regular Expression in Rasal NLU






share|improve this answer












It is good idea to use Regex for your purpose. Rasa NLU supports extraction of Entities by Regex. Normal NLU training data will have something like below



{
"rasa_nlu_data": {
"common_examples": [
{
"text": "Hi",
"intent": "greet",
"entities":
}]
}
}


You can provide Regex data for training as below in the NLU json file.



{
"rasa_nlu_data": {
"regex_features": [
{
"name": "pst",
"pattern": "[..*]"
},
]
}
}


Reference: Regular Expression in Rasal NLU







share|improve this answer












share|improve this answer



share|improve this answer










answered Nov 12 at 15:48









Karthik Sunil

315




315












  • I've tried this solution, but unfortunately, it doesn't make any difference in the output.
    – abhishake
    Nov 12 at 16:26


















  • I've tried this solution, but unfortunately, it doesn't make any difference in the output.
    – abhishake
    Nov 12 at 16:26
















I've tried this solution, but unfortunately, it doesn't make any difference in the output.
– abhishake
Nov 12 at 16:26




I've tried this solution, but unfortunately, it doesn't make any difference in the output.
– abhishake
Nov 12 at 16:26


















 

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