Conversational AI with Rasa: Entities
Summary
TLDRIn this video from the Conversational AI with Rasa series, Yusta explains the importance of entities in enhancing your assistant's understanding and response accuracy. The video covers different methods for entity extraction, including pre-built models like Duckling and SpaCy, regex patterns, and machine learning. It also delves into advanced features like synonyms, lookup tables, and entity roles and groups, providing practical insights on how to implement and utilize these techniques in your Rasa assistant for improved performance.
Takeaways
- 🧠 Entities are critical for AI assistants to better understand user inputs and respond appropriately, often extracted from user messages.
- 📝 Common entities include numbers, dates, country names, and other relevant information that can be contextualized in responses or actions.
- 📚 The training data for entity extraction is stored in the 'nlu.yaml' file, with entities labeled using a simple square bracket and label convention.
- 🚀 Rasa offers pre-built models like Duckling and Spacey for extracting entities without extensive labeled data, useful for dates, numbers, and more.
- 🔍 Regex is a method in Rasa for extracting entities that follow a deterministic pattern, such as user IDs, enhancing the assistant's ability to identify specific details.
- 🤖 Machine learning is employed for custom entities that don't have pre-built models or don't follow a regex pattern, requiring more training data for better results.
- 📊 Rasa's DIET classifier is a powerful machine learning model for entity extraction, part of the NLU model pipeline configurations.
- 📈 The output of entity extraction by Rasa is in JSON format, providing detailed information about the entity, its value, location in the sentence, and the extraction method used.
- 🔄 Synonyms in Rasa allow normalization of entity values, useful for mapping different user terms to a single value for consistent data usage.
- 📋 Lookup tables in Rasa are used to enhance extraction for entities with known possible values, like country names, by generating case-sensitive regex patterns.
- 🎯 Entity roles and groups in Rasa add additional context to entities, helping distinguish between different types of information, such as origin and destination in flight booking.
- 📘 Entity roles can influence the conversation flow and are configured in training data stories and referenced in the domain file for more natural and context-aware responses.
Q & A
What are entities in the context of Rasa?
-Entities are pieces of information that your assistant can extract from user inputs to better understand what is being asked and use those details in a specific context.
What are some common examples of entities?
-Common examples of entities include numbers, dates, country names, and any kind of relevant information, such as destinations in a flight booking assistant.
Where should the training data for entity extraction be stored in Rasa?
-The training data for entity extraction should be stored inside the nlu.yaml file.
How should entities be labeled in the training data?
-Entities should be surrounded by square brackets and the label should be included inside parentheses next to the word.
What are the different methods for entity extraction in Rasa?
-The different methods for entity extraction in Rasa include using pre-built models, regex, and machine learning.
What is Duckling and how is it used in Rasa?
-Duckling is a tool for extracting entities like numbers, dates, URLs, email addresses, etc. It requires no training data for your assistant to extract these details.
What is SpaCy and how does it enhance entity extraction in Rasa?
-SpaCy is a powerful library that enables the use of pre-built models to enhance entity extraction for details like person names and locations.
How does regex help in entity extraction in Rasa?
-Regex allows defining a specific pattern that the entities should follow, making it suitable for extracting entities that follow a deterministic pattern, such as user IDs.
When should machine learning be used for entity extraction in Rasa?
-Machine learning should be used for extracting custom entities that don't have pre-built models or don't follow specific patterns.
What is the Rasa DIET classifier?
-The Rasa DIET classifier is a powerful machine learning model used for entity extraction that requires a lot of good quality training data to achieve the best results.
What additional features does Rasa offer for enhancing entity extraction?
-Rasa offers features such as synonyms, lookup tables, and entity roles and groups to enhance entity extraction.
How do synonyms work in Rasa?
-Synonyms allow mapping extracted entity values to a different value to normalize the data, which is useful when users refer to the same thing using different terms.
What are lookup tables in Rasa and how are they used?
-Lookup tables are lists of words used to generate case-sensitive regex patterns, enhancing entity extraction for details with a set of known possible values.
What are entity roles and groups in Rasa?
-Entity roles and groups allow assigning additional details to entities, such as distinguishing between origin and destination in a flight booking assistant.
Outlines
🔍 Understanding Entities in Rasa AI
This paragraph introduces the concept of entities in the context of conversational AI with Rasa. Entities are vital pieces of information extracted from user inputs that assist the AI in comprehending the query and responding appropriately. The paragraph explains that entities can range from simple details like numbers and dates to more complex and context-specific information. It also touches on the training data requirements for entity extraction, which should be formatted within the 'nlu.yaml' file using a specific labeling convention. The paragraph further discusses various methods for entity extraction in Rasa, including the use of pre-built models like Duckling for common entities and Spacey for names and locations, regex for deterministic patterns, and machine learning for custom entities. The importance of quality training data for machine learning models is emphasized, along with the inner workings and considerations when building these models.
🛠️ Enhancing Entity Extraction with Rasa Features
The second paragraph delves into additional features of Rasa that can enhance entity extraction. It begins by discussing synonyms, which help in mapping different user expressions to a single normalized value, facilitating consistent data usage across the system. The paragraph explains how synonyms can be added either in the 'nlu.yaml' file or inline with training examples. It also introduces lookup tables, which are used to create regular expression patterns for known sets of values, such as country names. Furthermore, the paragraph covers entity roles and groups, which allow for the differentiation of entities within a context, such as distinguishing between an origin and a destination in a flight booking scenario. The importance of providing varied examples for roles and groups is highlighted to enable the AI to learn effectively. The paragraph concludes by noting that entity roles can influence conversation flow and must be integrated into training data stories and the domain file.
Mindmap
Keywords
💡Entities
💡Conversational AI
💡Rasa
💡NLU (Natural Language Understanding)
💡Duckling
💡Regex
💡Machine Learning
💡DIET Classifier
💡Synonyms
💡Lookup Tables
💡Entity Roles and Groups
Highlights
Entities are vital for assistants to understand user inputs and respond contextually.
Entities can be any relevant piece of information, such as numbers, dates, or country names.
Entity extraction training data should be stored in the nlu.yaml file.
Entities are labeled in training data by surrounding the word with square brackets and adding the label in parentheses.
Rasa offers pre-built models like Duckling for extracting numbers, dates, URLs, and email addresses without extensive training data.
Spacey is another library for enhancing entity extraction with pre-built models for names and locations.
To use pre-built models, reference them in the NLU model configuration file.
Regex is useful for extracting entities that follow a deterministic pattern, like user IDs.
Machine learning is employed for custom entities without pre-built models or specific patterns.
Rasa's diet classifier is a powerful model for entity extraction using machine learning.
High-quality training data is crucial for better machine learning model results.
Rasa's output for entity extraction is in JSON format, detailing the entity, value, location, and extraction method.
Synonyms help normalize extracted entity values for consistent database querying or API calls.
Synonyms can be added via the nlu.yaml file or inline with NLU training examples.
Lookup tables enhance extraction for entities with known possible values, like country names.
Entity roles and groups allow distinguishing between different types of entities, such as origins and destinations in flight booking.
Entity roles can influence the conversation flow and must be included in training data stories and the domain file.
Entities are essential for assistants to comprehend user requests and utilize them contextually in later interactions.
Transcripts
[Music]
hi everyone and welcome back to the
conversational ai with rasa series my
name is yusta and i'm extremely excited
to continue your developer education
journey with you
in this video we will talk about
entities
entities are the pieces of information
that your assistant can extract from
user inputs those details can help your
assistant to better understand what is
being asked and later on use those
details in a specific context when
responding back to the user or running
specific actions
the most common examples of entities are
details like numbers dates country names
and similar but an entity can really be
any kind of piece of information that is
relevant and important to your assistant
for example for a flight booking
assistant it would be really useful to
know which detail in users input is a
destination
that's why in this example where the
user says that they would like to book a
ticket to sydney
sydney is being extract as an entity of
a type destination
now a quick note on how the training
data for entity extraction should look
like and where should it be stored
the training data for entity extraction
should be stored inside of your nlu.yaml
file now how this data should be labeled
it's a very simple convention
the word that should be extracted as an
entity should be surrounded by square
brackets and then next to it you should
include the label to this entity inside
of the parentheses
there are a few ways of how entities can
be extracted using rasa
different methods work best for specific
entity types but the most common
approach that we see being used by
developers is using a mix of different
methods to achieve the best results
let's talk about those methods one by
one
the first approach is using pre-built
models
rasa enables you to use pre-built models
to enhance the entity extraction results
without requiring you to have loads of
labeled data for example
a tool called duckling is an extremely
powerful approach for extracting
entities like numbers dates urls email
addresses and similar
the best part about duckling is that it
requires no training data for your
assistant to extract those details
another really powerful library spacey
enables you to use pre-built models to
enhance the entity extraction for
details like person names locations and
similar to enable your assa assistant to
use pre-built models all you have to do
is reference those models in your nlu
model configuration file we will talk
about those details in the later
episodes of the series
another approach for extracting entities
with fasa is using regex
regex allows you to define a specific
pattern that the entities you would like
to extract should follow
it means that regex is the best approach
for extracting entities that follow a
specific deterministic pattern for
example user ids and similar details
to enable your rasa assistant to use
regex you will have to define the regex
pattern and include it in your nlu.yaml
file in addition to a few training
examples for that specific entity
don't forget to name the entity you
would like your assistant to extract the
same way as you named your regex pattern
and last but not least the third
approach for extracting entities with
rasa is using machine learning
if some of the entities that you would
like your assistant to extract don't
have pre-built models or if they don't
follow a specific pattern so using regex
is not really an option
you should use machine learning to
extract those
entities machine learning models are
very powerful for extracting what we
call custom entities
to extract entities using machine
learning models you will need some
training data in fact you will need a
lot more training data than for previous
approaches
but the more good quality training data
you have the better results you will
achieve
raza comes with a few machine learning
models that you can use and train on
your own training data
for example one of the most powerful
models for entity extraction is rasa's
diet classifier when extracting entities
using machine learning models there are
lots of things that are happening under
the hood and there are quite a few
things you have to take into account
when building those models
we will talk about this topic a lot more
in the later episodes of the series when
we will dive deeper into the nlu model
pipeline configurations
when entities are extracted under the
hood rasa produces an output in a json
format
that output is rich in detail about what
kind of entity has been extracted what
is the value of that entity and where in
a sentence that detail was found and
also what method or a model has been
used for extracting this detail so for
example here you can see an entity city
being extract with a value new york city
and the method that has been used for
extracting this entity is diet
classifier
in addition to just extracting entities
rasa comes with a few additional
features that can help you enhance the
entity extraction even more let's talk
about them
the first one is called synonyms
synonyms allow you to map extracted
entity value to a value different than
the one extracted
now where this is useful in some cases
users will refer to the same thing using
lots of different terms
but you as a developer you will want to
use the values of the entities for let's
say querying the database or making api
calls or using those details for
something else
this means that you will need the
extracted entity values to be normalized
and may be mapped under one specific
value and this exactly what you can
achieve with synonyms
there are two ways of how synonyms can
be added to your rasa assistant
one of them is by adding a new section
to your nlu.yaml file called synonym you
have to define the actual value that
extracted values will have to be mapped
to and then you have to provide examples
of how users might refer to that
specific synonym
the second approach of adding synonyms
to your aza assistant is by adding them
in line with your nlu training examples
all you have to do is add another
parameter called value which will
reference the value that extracted
entities will have to be mapped to
a very important note about synonyms
synonym mapping happens after entities
are extracted which means that you will
need some training data to enable your
assistant to extract entities first
another very powerful feature of rasa is
lookup tables
lookup tables are lists of words that
can be used to generate case sensitive
regular expression patterns
with lookup tables you can enhance
entity extraction for details that have
a set of known possible values
for example you can use lookup table to
enhance the entity extraction for
country names to achieve that include
the list of all countries in the world
in your nlu.yaml file under the section
lookup
and the last very powerful feature of
rasa for entity extraction
is entity roles and groups
if you are building an assistant for
flight booking or something similar you
will quickly realize that you will want
to add additional details for specific
entities for example
if the user says that they would like to
book a ticket from new york to boston
in a very simple scenario your assistant
will extract new york and boston as an
entity location but for your assistant
to be natural in how it responds and in
general to be able to run necessary
actions to book a ticket your assistant
will have to distinguish which detail is
the origin which one is the destination
you can enable your assistant to do that
by defining entity roles
to do that you will have to include
additional parameter called role to your
entity labeled data and define which
role a specific entity should correspond
to
entity groups allow you to group
specific entities under a specific
category
so for example in a pizza booking
assistant you can enable your assistant
to group specific topics corresponding
to a specific order
an important thing about entity roles
and groups is that you have to include
quite a few different examples for your
assistant to really learn so make sure
to include different examples of
different variations of roles and groups
entity roles can also be configured to
influence the flow of the conversation
to do that you will have to include
roles in your training data stories just
like you see in the example here and
also reference them in your domain file
entities are extremely important pieces
of information that can enable your
assistant to better understand what is
being asked and enable your assistant to
use them later on in a specific context
we will dive even deeper into the topic
of collecting the information and using
it in a specific context in the later
episodes of the conversational ai with
rasa series i hope i'll see you there
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