How to identify multiple entities in RASA
I want to extract multiple entities from a user input.
Example- "Service httpd is not responding because of high CPU usage and DNS Error"
So here I want to identify below:
Httpd
High CPU usage
DNS Error
And I will be using this keywords to get a response from a Database.
rasa-nlu rasa-core
asked Nov 21 '18 at 1:53
SUBHOJEETSUBHOJEET
394
add a comment |
I want to extract multiple entities from a user input.
Example- "Service httpd is not responding because of high CPU usage and DNS Error"
So here I want to identify below:
Httpd
High CPU usage
DNS Error
And I will be using this keywords to get a response from a Database.
rasa-nlu rasa-core
asked Nov 21 '18 at 1:53
SUBHOJEETSUBHOJEET
394
add a comment |
I want to extract multiple entities from a user input.
Example- "Service httpd is not responding because of high CPU usage and DNS Error"
So here I want to identify below:
Httpd
High CPU usage
DNS Error
And I will be using this keywords to get a response from a Database.
rasa-nlu rasa-core
asked Nov 21 '18 at 1:53
SUBHOJEETSUBHOJEET
394
I want to extract multiple entities from a user input.
Example- "Service httpd is not responding because of high CPU usage and DNS Error"
So here I want to identify below:
Httpd
High CPU usage
DNS Error
And I will be using this keywords to get a response from a Database.
rasa-nlu rasa-core
rasa-nlu rasa-core
asked Nov 21 '18 at 1:53
SUBHOJEETSUBHOJEET
394
asked Nov 21 '18 at 1:53
SUBHOJEETSUBHOJEET
394
asked Nov 21 '18 at 1:53
SUBHOJEETSUBHOJEET
394
asked Nov 21 '18 at 1:53
SUBHOJEETSUBHOJEET
394
asked Nov 21 '18 at 1:53
SUBHOJEETSUBHOJEET
394
394
add a comment |
add a comment |
1 Answer
1
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oldest
votes
Just annotate them accordingly, e.g.
## intent: query_error
- Service [httpd](keyword) is not responding because of [high CPU usage](keyword) and [DNS Error](keyword)
Having the sentence from above, Rasa NLU would extract 3 entities of type keyword. You can then access these entities in a custom action and query your database.
Regarding the number of examples which are required: this depends on
- the NLU pipeline which you are using. Typically
tensorflow_embeddingrequires more training examples thanspacy_sklearnsince it does not use pretrained language models. - the number of different values your entities can have. If it is only
httpd,high CPU usage, andDNS errorthen you don't need a lot of examples. However, if you have a thousand different values for your entity, then you need more training examples
One intent is enough if you always want to trigger the same custom action. However, if you want to classify different type of problems, e.g. server problems and client problems, and trigger different databases depending on the type of problems, you might consider having multiple intents.
Sorry for the vague answers, but in machine learning most things are highly dependent on the use case and the dataset.
answered Nov 21 '18 at 13:20
TobiasTobias
570311
how many such examples do I need to train to extract the entities correctly. also is it necessary that all training example should consists of three similar entities.
– SUBHOJEET
Nov 21 '18 at 13:28
added it to my answer
– Tobias
Nov 21 '18 at 14:00
How to fill the slot in stories.md with the list of keywords, so it can be used in a custom action?
– asmaier
Nov 27 '18 at 12:26
add a comment |
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1 Answer
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oldest
votes
1 Answer
1
active
oldest
votes
active
oldest
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active
oldest
votes
Just annotate them accordingly, e.g.
## intent: query_error
- Service [httpd](keyword) is not responding because of [high CPU usage](keyword) and [DNS Error](keyword)
Having the sentence from above, Rasa NLU would extract 3 entities of type keyword. You can then access these entities in a custom action and query your database.
Regarding the number of examples which are required: this depends on
- the NLU pipeline which you are using. Typically
tensorflow_embeddingrequires more training examples thanspacy_sklearnsince it does not use pretrained language models. - the number of different values your entities can have. If it is only
httpd,high CPU usage, andDNS errorthen you don't need a lot of examples. However, if you have a thousand different values for your entity, then you need more training examples
One intent is enough if you always want to trigger the same custom action. However, if you want to classify different type of problems, e.g. server problems and client problems, and trigger different databases depending on the type of problems, you might consider having multiple intents.
Sorry for the vague answers, but in machine learning most things are highly dependent on the use case and the dataset.
answered Nov 21 '18 at 13:20
TobiasTobias
570311
how many such examples do I need to train to extract the entities correctly. also is it necessary that all training example should consists of three similar entities.
– SUBHOJEET
Nov 21 '18 at 13:28
added it to my answer
– Tobias
Nov 21 '18 at 14:00
How to fill the slot in stories.md with the list of keywords, so it can be used in a custom action?
– asmaier
Nov 27 '18 at 12:26
add a comment |
Just annotate them accordingly, e.g.
## intent: query_error
- Service [httpd](keyword) is not responding because of [high CPU usage](keyword) and [DNS Error](keyword)
Having the sentence from above, Rasa NLU would extract 3 entities of type keyword. You can then access these entities in a custom action and query your database.
Regarding the number of examples which are required: this depends on
- the NLU pipeline which you are using. Typically
tensorflow_embeddingrequires more training examples thanspacy_sklearnsince it does not use pretrained language models. - the number of different values your entities can have. If it is only
httpd,high CPU usage, andDNS errorthen you don't need a lot of examples. However, if you have a thousand different values for your entity, then you need more training examples
One intent is enough if you always want to trigger the same custom action. However, if you want to classify different type of problems, e.g. server problems and client problems, and trigger different databases depending on the type of problems, you might consider having multiple intents.
Sorry for the vague answers, but in machine learning most things are highly dependent on the use case and the dataset.
answered Nov 21 '18 at 13:20
TobiasTobias
570311
how many such examples do I need to train to extract the entities correctly. also is it necessary that all training example should consists of three similar entities.
– SUBHOJEET
Nov 21 '18 at 13:28
added it to my answer
– Tobias
Nov 21 '18 at 14:00
How to fill the slot in stories.md with the list of keywords, so it can be used in a custom action?
– asmaier
Nov 27 '18 at 12:26
add a comment |
Just annotate them accordingly, e.g.
## intent: query_error
- Service [httpd](keyword) is not responding because of [high CPU usage](keyword) and [DNS Error](keyword)
Having the sentence from above, Rasa NLU would extract 3 entities of type keyword. You can then access these entities in a custom action and query your database.
Regarding the number of examples which are required: this depends on
- the NLU pipeline which you are using. Typically
tensorflow_embeddingrequires more training examples thanspacy_sklearnsince it does not use pretrained language models. - the number of different values your entities can have. If it is only
httpd,high CPU usage, andDNS errorthen you don't need a lot of examples. However, if you have a thousand different values for your entity, then you need more training examples
One intent is enough if you always want to trigger the same custom action. However, if you want to classify different type of problems, e.g. server problems and client problems, and trigger different databases depending on the type of problems, you might consider having multiple intents.
Sorry for the vague answers, but in machine learning most things are highly dependent on the use case and the dataset.
answered Nov 21 '18 at 13:20
TobiasTobias
570311
Just annotate them accordingly, e.g.
## intent: query_error
- Service [httpd](keyword) is not responding because of [high CPU usage](keyword) and [DNS Error](keyword)
Having the sentence from above, Rasa NLU would extract 3 entities of type keyword. You can then access these entities in a custom action and query your database.
Regarding the number of examples which are required: this depends on
- the NLU pipeline which you are using. Typically
tensorflow_embeddingrequires more training examples thanspacy_sklearnsince it does not use pretrained language models. - the number of different values your entities can have. If it is only
httpd,high CPU usage, andDNS errorthen you don't need a lot of examples. However, if you have a thousand different values for your entity, then you need more training examples
One intent is enough if you always want to trigger the same custom action. However, if you want to classify different type of problems, e.g. server problems and client problems, and trigger different databases depending on the type of problems, you might consider having multiple intents.
Sorry for the vague answers, but in machine learning most things are highly dependent on the use case and the dataset.
answered Nov 21 '18 at 13:20
TobiasTobias
570311
edited Nov 21 '18 at 14:00
answered Nov 21 '18 at 13:20
TobiasTobias
570311
answered Nov 21 '18 at 13:20
TobiasTobias
570311
answered Nov 21 '18 at 13:20
TobiasTobias
570311
570311
how many such examples do I need to train to extract the entities correctly. also is it necessary that all training example should consists of three similar entities.
– SUBHOJEET
Nov 21 '18 at 13:28
added it to my answer
– Tobias
Nov 21 '18 at 14:00
How to fill the slot in stories.md with the list of keywords, so it can be used in a custom action?
– asmaier
Nov 27 '18 at 12:26
add a comment |
how many such examples do I need to train to extract the entities correctly. also is it necessary that all training example should consists of three similar entities.
– SUBHOJEET
Nov 21 '18 at 13:28
added it to my answer
– Tobias
Nov 21 '18 at 14:00
How to fill the slot in stories.md with the list of keywords, so it can be used in a custom action?
– asmaier
Nov 27 '18 at 12:26
how many such examples do I need to train to extract the entities correctly. also is it necessary that all training example should consists of three similar entities.
– SUBHOJEET
Nov 21 '18 at 13:28
how many such examples do I need to train to extract the entities correctly. also is it necessary that all training example should consists of three similar entities.
– SUBHOJEET
Nov 21 '18 at 13:28
added it to my answer
– Tobias
Nov 21 '18 at 14:00
added it to my answer
– Tobias
Nov 21 '18 at 14:00
How to fill the slot in stories.md with the list of keywords, so it can be used in a custom action?
– asmaier
Nov 27 '18 at 12:26
How to fill the slot in stories.md with the list of keywords, so it can be used in a custom action?
– asmaier
Nov 27 '18 at 12:26
add a comment |
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