I'm trying to setup LUIS to connect to my Azure WebApp Bot, I've been asked by my IT Director to test the bot on a "Simple" Staff Directory Lookup (hosted in Azure SQL VM's).
I was trying to configure LUIS to understand intents such as 'Who is in Hospitality', or 'Who is Joe Bloggs', but I'm struggling with how to do this.
Do I use entities for departments and people? Are there Pre-Built Intents for 'Greetings' and other commonly used intents?
Any help would be appreciated.
You have several questions so I splitted my answer in 2 parts.
Information detection (departement, names)
[I want to] understand intents such as 'Who is in Hospitality', or 'Who is Joe
Bloggs', but I'm struggling with how to do this.
Do I use entities for departments and people?
Department:
If you have a limited and known list of departments, you can create an Entity which type will be List. It will process an exact text match on the items of this list (see doc here).
If you don't have this list, use an Entity of type Simple (see doc here) and label this entity in several (various) examples utterances that you provide. You can improve the detection by also adding a Phrase list in that case: it will help and is not processing an exact match in the list. And you should improve it over the time.
People:
For the people name detection, it will be a little bit more tricky. You can have a look to Communication.ContactName pre-built entity. If it's not working, create your own simple entity but I'm not sure that the results will be relevant.
"Small talk" part
Are there Pre-Built Intents for 'Greetings' and other commonly used
intents?
There is no pre-built intents but there is a Lab Project called Personality Chat that is designed to manage such cases (in English only for the moment): https://labs.cognitive.microsoft.com/en-us/project-personality-chat
It is still in a lab version, so you should not use in production, but it is mostly open-source so you can give it a try and see if it fits your needs.
Related
I've been trying to replicate some of my previous LUIS knowledge into the new Microsoft Language portal (Conversational Language Understanding) and I'm getting stuck into one thing that we used to do so frequently that we have no longer been able to do: nested entities.
To clarify what my doubt is, pretend that we have just asked the user to provide a desired price range for a product. Then the user says: 'price range from $233 to $400'. With Luis, we could create a machine learning entity where we could have two other sub-entities: minimumValue, maximumValue like this:
nested entity in LUIS
and when we train and test, we get a result like this:
training and results in LUIS
My question is: how can we implement something similar in CLU?
I have already tried Quantity.NumberRange prebuilt entity and it does not cover all the possible scenarios! I tested it with many different ways of mentioning a range and it failed in many of them. Also, I tried to merge the prebuilt training with manual labeling training (learned) and the thing is that when the prebuilt did not find the minimum and maximum (meaning that it did not work) the manual labeling worked (but I couldn't specify the minimum and maximum because there are no nested entities in CLU). I would really appreciate any help
In our company, we are building a chatbot using Microsoft Luis Bot Framework. For two of the intent's, I don't know how I should annotate the examples.
I have a product called ABC and I need two different intent's: Access ABC and Use ABC. That is, for each of these two intents, we have different answers.
Now, my questions is how can I define synonyms for verbs. Should I define verbs as entities?
Our utterances for the intent Access ABC would be like:
1) How can I access ABC?
2) How can I reach ABC?
Our utterance's for the intent Use ABC would be like:
3) How can I use ABC?
4) Is there any introduction to ABC?
5) I am new to ABC. Is there any usage guideline?
The problem that I see is that if I don't tag the verbs for the first intent, how can the system distinguish between 1 (or 2) and 4?
This is a big problem. In your utterences 1 and 2 for example, all you are teaching a model is that the verb access all the verb reach have no importance. this is because you are telling the models look at those two sentences both works regardless of what is a verb. This mean it will create quite a lot of confusions with your sentence number three. Training this model is still very empirical. We use QBox (disclaimer, I work for them). Having a couple of verbs uterences for each use case might work but you are going to have to try it. But be careful at your training examples. Try to vary as much as you can the word that play less importance.
1- how can I access ABC
2- tell me how to reach ABC
3- I can't access ABC
4- I'm struggling to reach ABC
remember that you will need 10 to 15 examples minimum in order to reach a good level of performance. This depends of the number of intents you have in your model of course
What is the best practice approach to handle typos / misspelling on LUIS List Entities?
I have intents on LUIS which use a list entity (specifically Company Department - HR, Finance, etc). It is common for users to misspell this when putting forward their utterance. LUIS expects an exact match, it doesn't do a "smart" match, and therefore doesn't pick up the misspelled entity.
a) Using bing spell check is not necessarily a good solution. e.g. Certain departments are acronyms such as VRPA - and bing wont correct a typo there.
b) When I used LUIS a year ago, I would pre-process the utterance and use a Levenshtein distance algorithm to fix typos on list entities before feeding them to LUIS.
I would imagine that by now LUIS has some better out of the box way of handling this very common use case.
I'd appreciate input on what the best practice approach is to handle this.
#acambitsis and I exchanged messages via his UserVoice ticket, but I'm going to post the answer here for others.
A combination of Bing and Simple Entities might be what you're looking for, then (they're machine-learned).
I was able to accomplish something close and attached images.
In entities, I created a Simple entity with the role, VRPA. In intents, I created the Show Me intent and added sample utterances "Show me the VRPA" and "Show me the VPRA". I clicked on V**A and selected the Simple Entity:VRPA role. After training, I tried "show me the varp" and it correctly guessed "varp" was the "Simple:VRPA" entity.
You may also find RegEx entities useful. For acronyms, you could do something like: /[vrpa]/i and then any combination of VRPA/VPRA/VARP/ARVP would match.
I highly recommend reading through the Entity Types and Improve App Performance to see if anything jumps out to solve your particular issues.
This may not do exactly what you're looking for. If not, I'd recommend implementing a fuzzy-matching algo of your choice.
entities
intents
I am using luis.ai which is offered as a part of Microsoft Cognitive Services, in my project. I have a requirement of detecting names using LUIS. For the same, I have been using the phrase list feature. I have added some names in the list. But as we all know, the names list is never exhaustive. So, no matter how many names I add, since they don't have a specific pattern, when I test with some new names, the entity detection fails. I want to know if there's any other way in which we can have LUIS detect names of people.
Please let me know if you have a solution to this problem.
LUIS could be used to recognize and extract intents and entities from utterances, but based on my experience, it might not be 100% intelligent to identify person’s name, because person’s name could be anything.
As you did, adding not well-recognized names in phrase list could be as a solution. Besides, this github issue:Identifying the Names from the sentence using LUIS discussed a similar question, and as cahann mentioned, you can add and label more example utterances that contain not well-recognized name to make your LUIS app recognize Names better.
I trained my luis model to recognize an intent called "getDefinition" with example utterances such as: "What does BLANK mean" or "Can you explain BLANK to me?". It recognizes the intent correctly. I also added an entity called "topic" and trained it to recognize what topic the user is asking about. The problem is that luis only recognizes the exact topic the user is asking about if I used that specific term in one of the utterances before.
Does this mean I have to train it with all the possible terms a user can ask about or is there some way to have it recognize it anyway?
For example when I ask "What does blockchain mean" it correctly identifies the entity (topic) as blockchain because the word blockchain is in the utterance. But if I ask the same version of the question about another topic such as "what does mining mean", it doesn't recognize that as the entity.
Using a list or phrase list doesn't seem to be solving the problem. I want to eventually have thousands of topics the bot responds to, entering each topic in a list is tedious and inconvenient. Is there a way LUIS can recognize that its a topic just from the context?
What is the best way to go about this?
Same Doubt, Bit Modified. Sorry for Reposting this here.
At the moment LUIS cannot extract an entity just based on the the intent. Phrase lists will help LUIS extract tokens that don't have explicit training data. For example training LUIS with the utterance "What does blockchain mean?" does not mean that it will extract "mining" from "What does mining mean?" unless "mining" was either included in a phrase list, or a list entity. In addition to what Nicolas R said about tagging different values, another thing to consider is that using words not commonly found (or found at all) in the corpuses that LUIS uses for each culture will likely result in LUIS not extracting the words without assistance (either via Phrase list or list entity).
For example, if you created a LUIS application that dealt with units of measurement, while you might not be required to train it with units such as inch, meter, kilometer or ounce; you would probably have to train it with words like milliradian, parsec, and even other cultural spellings like kilometre. Otherwise these words would most likely not be extracted by LUIS. If a user provided the tokens "Planck unit", LUIS might provide a faulty extraction where it returns "unit" as the measurement entity instead of "Planck unit".