In summary, why does Luis not label the preBuilt entity personName in some cases. Often the second name is not labeled for no discernible reason.
This behaviour does not exist for say geography preBuilt entity with the same kinds of utterances.
If anyone can explain why this happens and how best to address it I'd greatly appreciate it.
This simply dose not make sense to me. I would love to understand more.
personName Image here.
Luis not labelling All personName correctly
geography example here (without the same issues as above)
geography example
Thanks. K.
The scenario you described is currently the expected behavior (it might extract some or all names). However, we are working on improving the built-in personName entity (currently on our road-map). However, we recommend using a machine learned entity where you label the instances of names in your dataset and using the personName entity as a feature to help create your own name entity. Sorry for the inconvenience, but hope this helps!
Related
In the v3 api for building LUIS apps I notice an emphasis on Machined learned entities. When working with them I notice something that concerns me and I was hoping to get more insight into the matter.
The idea is that when using a machined learned entity you can bind it to descriptors of phrase lists or other entities or list entities as a constraint on that machined learned entity. Why not just aim to extract the list entity by itself? What is the purpose of wrapping it in a machined learnt object?
I ask this because I have always had great success with lists. It very controllable albeit you need to watch for spelling mistakes and variations to assure accuracy. However, when I use machined learnt entities I notice you have to be more careful with word order. If there is a variation it could not pick up that machined learnt entity.
Now training would fix this but in reality if I know I have the intent I want and I just need entities from that what really does the machine learnt entity provide?
It seems you need to be more careful with it.
Now I say this with this suspicion. Would the answer lie in the fact that a machine learnt entity would increase intent detection where a list entity would only serve to increase entity detection. If that is the answer that most fits I think I can see the solution to what it is I am looking for.
EDITED:
I haven't been keeping up with LUIS ever since I went on maternity leave, and lo and behold, it's moving from V2 to V3!
The following shows an email conversation from a writer of the LUIS team's documentation.
LUIS is moving away from different types of entities toward a single ML entity to encapsulate a concept. The ML entity can have children which are ML entities themselves. An ML entity can have a feature directly connected to it, instead of acting as a global feature.
This feature can be a phrase list, or it can be another model such as a prebuilt entity, reg ex entity, or list entity.
So a year ago a customer might have built a composite entity and thrown features into the app. Now they should create an ML entity with children, and these children should have features.
Now (before //MS Build Conference) any non-phrase-list feature can be a constraint (required) so a child entity with a constrained regex entity won’t fire until the regex matches.
At/after //Build, this concept has been reworked in the UI to be a required feature – same idea but different terminology.
...
This is about understanding a whole concept that has parts, so an address is a typical example. An address has street number, street name, street type (street/court/blvd), city, state/province, country, postal code.
Each of the subparts is a feature (strong indicator) that an address is in the utterance.
If you used a list entity but not as a required feature to the address, yes it would trigger, but that wouldn’t help the address entity which is what you are really trying to get.
If however, you really just want to match a list, go head. But when the customer says the app isn’t predicting as well as they thought, the team will come back to this concept of the ML entity parent and its parts and suggest changes to the entities.
I'm looking to share an EF Context between "data manager" objects to ensure change tracking occurs under one context as opposed to handling multiple contexts...so I'm looking into named context... but in review of this documentation, I felt it wasn't clear, and I want to ensure my assumption is correct, and if so, update the documentation: here PRISM documents the resolution of named instances via a constructor:
My assumption is that the named typed "carservice" is matched to the named parameter in the constructor to select which concrete class should be reference to that parameter based on the IVehicleService.
Is my assumption correct - either way I'll create a pull request to clarify the documentation..Either way, answering the question here will add reference to clarification/handling of named instances in PRISM.
It seems this is NOT correct assumption, and it's not possible in Unity - I refactored to avoid.
One solution that I didn't try was to simply another interface (came to mind much later), was inheriting from an existing interface, and registers the "new" interface to use a specific constructor. As I'm new to Unity, I thought I would mention. I'm answering my own question, as I can't don't think I can give credit to a comment, and I HATE leaving questions unanswered on this site!
Suppose I have a intent "findStuff" that takes things of the form
find xxx
list xxx where something = somevalue
find xxx where something = somevalue
Getting the LUIS to understand that "xxx" is any word seems hard. I defined a "plainWord" entity, and defined a pattern feature with the same name & value "\w+". I thought that that used to work, but doesn't seem to be doing it any more. Some words that it has seen it recognizes, but it can never seem to deal with "find junk" -- "junk" is never recognized as any entity.
The system for which this is intended is open-ended. Users can add there own types of things that we may "find."...
How extensively trained is your model? You should update your model by labeling users' utterances. I recommend against using generalized entities like a "plainWord" entity, from your description it sounds like this entity is supposed to just be applied to words that occur after "find" and "list". If an entity has not been labeled/applied to many utterances, your model will not catch the words you want it to catch.
If you post your LUIS model I might be able to better help you. You can export the JSON model or provide your application ID to share it.
If I have a prebuilt entity temperature.
And how can I match it to my intent AskTemperature
Because In the AskTemperature.
It can't add the prebuilt entity.
So how do I implement to make entity temperature belongs to the intent AskTemperature?
Thank you!!!
If you provide an example of the labeled utterances, the community may be better equipped to answer your question. That said... Once you train your model, the model should automatically assign the prebuilt entity to the recognized tokens (words, numbers, etc).
After training your model, if this recognition doesn't occur, you may need to train with more straight forward utterances (e.g. "Is the temperature in Redmond right now 16 celsius?").
If all of this doesn't work, then posting relevant portions of your LUIS model may aid in getting help from the SO community.
After you trained your model/intents, the prebuilt entities should be assigned to that utterances.
I have this utterance: are there room available on 12/21/2016?
When I tried to assign the 12/21/2016 to "datetimeV2" entity directly, I cannot find that prebuilt entity; but after I trained it, it shows automatically.
Click to see this image (cannot post image since reputation not reaching 10)
Hope this helps!
i'm working on new entities discovery from text and was wondering if stanford nlp can be used for this purpose ?
Actually what i know is that stanford requires trained classifiers to recognize entities but if i'm not wrong it will only detect already known entities for example if your models contains "stanford is a good university" and stanford is already a know entity, if i try "fooo is a good university" it won't recognize it as a new entity
This project should be of interest to you:
http://nlp.stanford.edu/software/patternslearning.shtml
OK - if javascript is fine for you (node.js/browser) please see : http://github.com/redaktor/nlp_compromise/
This is a "No training" solution. I worked especially on NER (named entity extraction) the last days - just described it here Named entity recognition with a small data set (corpus)
Feel free to ask me about it in the github issues because I did not document the new methods (no time and still working on it)