Documentation for the classic Watson Assistant experience has moved. For the most up-to-date version, see Defining information to look for in customer input.

Defining information to look for in customer input

Entities represent information in the user input that is relevant to the user's purpose.

If intents represent verbs (the action a user wants to do), entities represent nouns (the object of, or the context for, that action). For example, when the intent is to get a weather forecast, the relevant location and date entities are required before the application can return an accurate forecast.

Recognizing entities in the user's input helps you to craft more useful, targeted responses. For example, you might have a #buy_something intent. When a user makes a request that triggers the #buy_something intent, the assistant's response should reflect an understanding of what the something is that the customer wants to buy. You can add a @product entity, and then use it to extract information from the user input about the product that the customer is interested in. (The @ prepended to the entity name helps to clearly identify it as an entity.)

Finally, you can add multiple responses to your dialog tree with wording that differs based on the @product value that is detected in the user's request.

Entity evaluation overview

Your assistant detects entities in the user input by using one of the following evaluation methods:

Dictionary-based method

Your assistant looks for terms in the user input that match the values, synonyms, or patterns you define for the entity.

Synonym entity: You define a category of terms as an entity (color), and then one or more values in that category (blue). For each value you specify a bunch of synonyms (aqua, navy).

At run time, your assistant recognizes terms in the user input that exactly match the values or synonyms that you defined for the entity as mentions of that entity.
Pattern entity: You define a category of terms as an entity (contact_info), and then one or more values in that category (email). For each value, you specify a regular expression that defines the textual pattern of mentions of that value type. For an email entity value, you might want to specify a regular expression that defines a text@text.com pattern.

At run time, your assistant looks for patterns matching your regular expression in the user input, and identifies any matches as mentions of that entity.
System entity: Synonym entities that are prebuilt for you by IBM. They cover commonly used categories, such as numbers, dates, and times. You simply enable a system entity to start using it.

Annotation-based method

When you define an annotation-based entity, which is also referred to as a contextual entity, a model is trained on both the annotated term and the context in which the term is used in the sentence you annotate. This new contextual entity model enables your assistant to calculate a confidence score that identifies how likely a word or phrase is to be an instance of an entity, based on how it is used in the user input.

Contextual entity: First, you define a category of terms as an entity (product). Next, you go to the Intents page and mine your existing intent user examples to find any mentions of the entity, and label them as such. For example, you might go to the #buy_something intent, and find a user example that says, I want to buy a Coach bag. You can label Coach bag as a mention of the @product entity.

For training purposes, the term you annotated, Coach bag, is added as a value of the @product entity.

At run time, your assistant evaluates terms based on the context in which they are used in the sentence only. If the structure of a user request that mentions the term matches the structure of an intent user example in which a mention is labeled, then your assistant interprets the term to be a mention of that entity type. For example, the user input might include the utterance, I want to buy a Gucci bag. Due to the similarity of the structure of this sentence to the user example that you annotated (I want to buy a Coach bag), your assistant recognizes Gucci bag as a @product entity mention.

When a contextual entity model is used for an entity, your assistant does not look for exact text or pattern matches for the entity in the user input, but focuses instead on the context of the sentence in which the entity is mentioned.

If you choose to define entity values by using annotations, add at least 10 annotations per entity to give the contextual entity model enough data to be reliable.

To learn more about contextual entities, read this blog post.

To learn about how to use contextual entites to identify names and locations, see the Detecting Names And Locations With Watson Assistant blog post on Medium.

Creating entities

Open your dialog skill and then click the Entities tab.
Click Create entity.

You can also click System entities to select from a list of common entities, provided by IBM, that can be applied to any use case. See Enabling system entities for more detail.
In the Entity name field, type a descriptive name for the entity.

The entity name can contain letters (in Unicode), numbers, underscores, and hyphens. For example:
- @location
- @menu_item
- @product
Do not include spaces in the name. The name cannot be longer than 64 characters. Do not begin the name with the string sys- because it is reserved for system entities.

The at sign @ is prepended to the entity name automatically to identify the term as an entity. You do not need to add it.
Click Create entity.
For this entity, choose whether you want your assistant to use a dictionary-based or annotation-based approach to find mentions of it, and then follow the appropriate procedure.

For each entity that you create, choose one entity type to use only. As soon as you add an annotation for an entity, the contextual model is initialized and becomes the primary approach for analyzing user input to find mentions of that entity. The context in which the mention is used in the user input takes precedence over any exact matches that might be present. See Entity evaluation overview for more information about how each type is evaluated.
- Dictionary-based entities
- Annotation-based entities

Adding dictionary-based entities

Dictionary-based entites are those for which you define specific terms, synonyms, or patterns. At run time, your assistant finds entity mentions only when a term in the user input exactly matches (or closely matches if fuzzy matching is enabled) the value or one of its synonyms.

In the Value name field, type a value. For example, for the @city entity, you might type New York City.

An entity value can be any string up to 64 characters in length.

Important: Don't include sensitive or personal information in entity names or values. The names and values can be exposed in URLs in an app.
Add synonyms for the value. For example, you might add NYC and The Big Apple as synonyms for New York City.

A synonym can be any string up to 64 characters in length.

If you want to define a pattern for your assistant to look for in user input, such as a product order number or email address, define a pattern value instead. See Adding entities that recognize patterns for more details.

Note: You can add either synonyms or patterns for a single entity value, not both.
If you want your assistant to recognize terms with syntax that is similar to the entity value and synonyms you specify, but without requiring an exact match, set the Fuzzy Matching switch to On.

For example, if you add apple as a value for a @fruit entity, and a user enters apples or appel, if fuzzy matching is enabled, your assistant will recognize the word as a @fruit mention. For more information, see How fuzzy matching works.
Click Add value and repeat the process to add more entity values.

If you are adding many values, one after another, press Shift+Enter to finish adding the current value, and keep focus in the value field so you can add the next value.
After you add the entity values, click to finish creating the entity.

The entity you created is added, and the system begins to train itself on the new data.

Adding entities that recognize patterns

You can create an entity that looks for patterns in user input. For example, you can look for mentions of an email address by looking for occurrences of the pattern {word}+@+{word}+.com. Or, you might have product order numbers that follow a very specific format, such as TWEX3433JKL. You can create a pattern to look for strings with that syntax in the user utterance.

To add an entity that recognizes a pattern:

Follow the standard procedure to create a dictionary-based entity, but select Patterns from the Type drop-down menu instead of Synonyms.
Add a regular expression that defines the pattern you want to look for.
- For each entity value, there can be a maximum of up to 5 patterns.
- Each pattern (regular expression) is limited to 512 characters.
Follow these syntax rules:
- Entity patterns may not contain:
  - Positive repetitions (for example x*+)
  - Backreferences (for example \g1)
  - Conditional branches (for example (?(cond)true))
- When a pattern entity starts or ends with a Unicode character, and includes word boundaries, for example \bš\b, the pattern match does not match the word boundary correctly. In this example, for input š zkouška, the match returns Group 0: 6-7 š (š zkouška), instead of the correct Group 0: 0-1 š (š zkouška).
  
  The regular expression engine is loosely based on the Java regular expression engine. You will see an error if you try to upload an unsupported pattern, either by using the API or from within the Watson Assistant user interface.
For example, for entity ContactInfo, the patterns for phone, email, and website values can be defined as follows:
- Phone
  - localPhone: (\d{3})-(\d{4}), e.g. 426-4968
  - fullUSphone: (\d{3})-(\d{3})-(\d{4}), e.g. 800-426-4968
  - internationalPhone: ^($?\+?[0-9]*$?)?[0-9_\- ]*$, e.g., +44 1962 815000
- email: \b[A-Za-z0-9._%+-]+@([A-Za-z0-9-]+\.)+[A-Za-z]{2,}\b, e.g. name@ibm.com
- website: (https?:\/\/)?([\da-z\.-]+)\.([a-z\.]{2,6})([\/\w \.-]*)*\/?$, e.g. https://www.ibm.com
Click Add value and repeat the process to add more entity values.

When you use pattern entities to find patterns in user input, you often need a way to store the part of the user input text that matches the pattern. To do so, you can use a context variable. For more information, see Defining a context variable.

For example, your dialog might ask users for their email addresses. The dialog node condition will contain a condition similar to @contactInfo:email. You can use the following syntax in the dialog node's response section to define a context variable that captures and stores the user's email address text:

Saving a pattern
Variable	Value
email	`<? @contactInfo.literal ?>`

This syntax indicates that you want to find the part of the user input that matches the email pattern and save that subset of text into a context variable named email.

Capture groups

For regular expressions, any part of a pattern inside a pair of normal parentheses will be captured as a group. For example, the entity @ContactInfo has a pattern value named fullUSphone that contains three captured groups:

(\d{3}) - US area code
(\d{3}) - Prefix
(\d{4}) - Line number

Grouping can be helpful if, for example, you want your assistant to ask a user for a phone number, and then use only the area code of the provided number in a response.

To assign the user-entered area code as a context variable, use the following syntax in the dialog node's response section to capture the group match:

Saving a capture group
Variable	Value
area_code	`<? @ContactInfo.groups[1] ?>`

For more information about using capture groups in your dialog, see Storing and recognizing entity pattern groups in input.

How fuzzy matching works

Fuzzy matching is available for languages noted in the Supported languages topic.

Fuzzy matching has these components:

Stemming - The feature recognizes the stem form of entity values that have several grammatical forms. For example, the stem of 'bananas' would be 'banana', while the stem of 'running' would be 'run'.
Misspelling - The feature is able to map user input to the appropriate corresponding entity despite the presence of misspellings or slight syntactical differences. For example, if you define giraffe as a synonym for an animal entity, and the user input contains the terms giraffes or girafe, the fuzzy match is able to map the term to the animal entity correctly.
Partial match - With partial matching, the feature automatically suggests substring-based synonyms present in the user-defined entities, and assigns a lower confidence score as compared to the exact entity match.

The partial match component is supported only in English-language dialog skills.

For English, fuzzy matching prevents the capturing of some common, valid English words as fuzzy matches for a given entity. This feature uses standard English dictionary words. You can also define an English entity value/synonym, and fuzzy matching will match only your defined entity value/synonym. For example, fuzzy matching may match the term unsure with insurance; but if you have unsure defined as a value/synonym for an entity like @option, then unsure will always be matched to @option, and not to insurance.

Interactions between the stemming and misspelling fuzzy matching features are not allowed. Specifically, if either an entity or the input is stemmed, misspelling fuzzy matching does not work. For example, assume that the entity is @lending and the input word is pending. During entity stemming, @lending produces lend. During input stemming, pending produces pend. In this case, lend does not match to pend because the entity and input were stemmed. This change applies to only the English language.

Adding contextual entities

Annotation-based entites are those for which you annotate occurrences of the entity in sample sentences to teach your assistant about the context in which the entity is typically used.

In order to train a contextual entity model, you can take advantage of your intent examples, which provide readily-available sentences to annotate.

This feature is generally available in English-language dialog skills and is available as a beta feature in French-langage dialog skills. For more information about language support, see Supported languages.

Using an intent's user examples to define contextual entities does not affect the classification of that intent. However, entity mentions that you label are also added to that entity as synonyms. And intent classification does use synonym mentions in intent user examples to establish a weak reference between an intent and an entity.

From your dialog skill, click the Intents tab.
Click an intent to open it.

For this example, the intent #buy_supplies defines the order function for an online retailer.
Click Annotate entities, and then review the intent examples for potential entity mentions.
Click any word, words, or punctuation that is part of a single entity mention from the intent examples.

In this example, mobile phones is the entity mention.

A Search box opens that you can use to search for the entity that the highlighted word or phrase is a mention of.
Enter the entity name to search for. You do not need to include the starting @ symbol.

Do one of the following things:
- If the entity has any existing entity values, they are displayed for informational purposes only. You are adding the annotation to the entity, not to any specific entity value.
- If you want to teach the model that the mention is synonymous with an existing entity value, add a colon (:) after the entity name to show a list of entity values. Choose an entity value from the list that is displayed. For example, @product:device.
Select the entity or entity and value to which you want to add the annotation.

In this example, mobile phones is being added as an annotation for the @product entity value and as a synonym for the @product:device entity value.

Create at least 10 annotations for each contextual entity; more annotations are recommended for production use.
If none of the entities are appropriate, you can create a new entity by adding its name. Then choose the {entity_name}(create new entity) option from the list.
Repeat this process for each entity mention that you want to annotate.

Be sure to annotate every mention of an entity type that occurs in any user examples that you edit. See What you don't annotate matters for more details.
Now, click the annotation you just created. A box is displayed that says, Go to: {entity-name}. Clicking that link takes you directly to the entity.

The annotation is added to the entity you associated it with, and the system begins to train itself on the new data.

The term you annotated is added to the entity as a new dictionary value. If you associated the annotated term with an existing entity value, then the term is added as a synonym of that entity value instead of as an independent entity value.
To see all of the mentions you annotated for a particular entity, from the entity's configuration page, click the Annotation tab.

Contextual entities understand values that you have not explicitly defined. The system makes predictions about additional entity values based on how your user examples are annotated, and uses those values to train other entities. Any similar user examples are added to the Annotation view, so you can see how this option impacts training.

If you do not want your contextual entities to use this expanded understanding of entity values, select all the user examples in the Annotation view for that entity, and then click Delete.

Tutorial: To walk through a tutorial that shows you how to define contextual entities before you add your own, go to Tutorial: Defining contextual entities.

The contextual entities tutorial shows a slightly older version of the user interface. But the concepts it covers are the same, so it is still a worthwhile exercise.

What you don't annotate matters

If you have an intent example with an annotation, and another word in that example matches the value or a synonym of the same entity, but the value is not annotated, that omission has impact. The model also learns from the context of the term you did not annotate. Therefore, if you label one term as a mention of an entity in a user example, be sure to label any other applicable mentions also.

The #Customer_Care_Appointments intent includes two intent examples with the word visit.
In the second occurrence of the word, you want to annotate the word visit as an entity value of the @meeting entity. This makes visit equivalent to other @meeting entity values such as appointment, as in I'd like to make an appointment or I'd like to schedule a visit.
In the first occurrence, the word visit is being used as a verb. It has a different meaning from a meeting. In this case, you can select the word appointment from the intent example, and annotate it as an entity value of the @meeting entity. The model learns from the fact that the word visit in the same example is not annotated.

Enabling system entities

Watson Assistant provides a number of system entities, which are common entities that you can use for any application. Enabling a system entity makes it possible to quickly populate your skill with training data that is common to many use cases.

System entities can be used to recognize a broad range of values for the object types they represent. For example, the @sys-number system entity matches any numerical value, including whole numbers, decimal fractions, or even numbers written out as words.

System entities are centrally maintained, so any updates are available automatically. You cannot modify system entities.

On the Entities page, click System entities.
Browse through the list of system entities to choose the ones that are useful for your application.
- To see more information about a system entity, including examples of matching input, click the entity in the list.
- For details about the available system entities, see System entities.
Set the switch for each system entity that you want to use to On.

After you enable system entities, Watson Assistant begins to retrain. After training is complete, you can use the entities.

Entity limits

The number of entities, entity values, and synonyms that you can create depends on your Watson Assistant service plan:

Plan details
Plan	Entities per skill	Entity values per skill	Entity synonyms per skill
Enterprise	1,000	100,000	100,000
Premium (legacy)	1,000	100,000	100,000
Plus	1,000	100,000	100,000
Lite, Trial	100	100,000	100,000

System entities that you enable for use count toward your plan usage totals.

Plan details continued
Plan	Contextual entities and annotations
Enterprise	150 contextual entities with 3000 annotations
Premium (legacy)	150 contextual entities with 3000 annotations
Plus	100 contextual entities with 2000 annotations
Lite, Trial	10 contextual entities with 1000 annotations

Editing entities

You can click any entity in the list to open it for editing. You can rename or delete entities, and you can add, edit, or delete values, synonyms, or patterns.

If you change the entity type from synonym to pattern, or vice versa, the existing values are converted, but might not be useful as-is.

Searching entities

Use the Search feature to find entity names, values, and synonyms.

From the Entities page, click the Search icon.

System entities are not searchable.
Enter a search term or phrase.

The first time you search for something, you might get a message that says the skill is being indexed. If so, wait a minute, and then resubmit the search term.

Entities containing your search term, with corresponding examples, are shown.

Downloading entities

You can download a number of entities to a CSV file, so you can then upload and reuse them in another Watson Assistant application.

Pattern information is included in the downloaded CSV file. Any string wrapped with / will be considered a pattern (as opposed to a synonym).
Annotations associated with contexual entities are not downloaded. You must download the entire dialog skill to capture both the entity value and any associated annotations.

Go to the Entities page
- To download all entities, meaning the entities that are listed on this and any additional pages, do not select any individual entities. Instead, click the Download icon .
- To download the entities that are listed on the current page only, select the checkbox in the header. This action selects all of the entities on the current page. Click the Download icon .
- To download one or more specific entities, select the entities that you want to download, and then click the Download icon .
Specify the name and location in which to store the CSV file that is generated, and then click Save.

Uploading entities

If you have a large number of entities, you might find it easier to upload them from a comma-separated value (CSV) file than to define them one by one.

Entity annotations are not included in the upload of an entity CSV file. You must upload the entire dialog skill to retain the associated annotations for a contextual entity in that skill. If you download and upload entities only, then any contextual entities that you downloaded are treated as dictionary-based entities after you upload them.

Collect the entities into a CSV file, or export them from a spreadsheet to a CSV file. The required format for each line in the file is as follows:
```
<entity>,<value>,<synonyms>
```
where <entity> is the name of an entity, <value> is a value for the entity, and <synonyms> is a comma-separated list of synonyms for that value.
```
weekday,Monday,Mon
weekday,Tuesday,Tue,Tues
weekday,Wednesday,Wed
weekday,Thursday,Thur,Thu,Thurs
weekday,Friday,Fri
weekday,Saturday,Sat
weekday,Sunday,Sun
month,January,Jan
month,February,Feb
month,March,Mar
month,April,Apr
month,May
```
Uploading a CSV file also supports patterns. Any string wrapped with / will be considered a pattern (as opposed to a synonym).
```
ContactInfo,localPhone,/(\d{3})-(\d{4})/
ContactInfo,fullUSphone,/(\d{3})-(\d{3})-(\d{4})/
ContactInfo,internationalPhone,/^($?\+?[0-9]*$?)?[0-9_\- ]*$/
ContactInfo,email,/\b[A-Za-z0-9._%+-]+@[A-Za-z0-9.-]+\.[A-Za-z]{2,}\b/
ContactInfo,website,/(https?:\/\/)?([\da-z\.-]+)\.([a-z\.]{2,6})([\/\w \.-]*)*\/?$/
```
Save the CSV file with UTF-8 encoding and no byte order mark (BOM). The maximum CSV file size is 10MB. If your CSV file is larger, consider splitting it into multiple files and uploading them separately. Open your dialog skill and then click the Entities tab.
Click the upload icon .
Drag a file, or browse to select a file from your computer, and then click Upload.

The file is validated and uploaded, and the system begins to train itself on the new data.

You can view the uploaded entities on the Entities tab. You might need to refresh the page to see the new entities.

Deleting entities

You can select a number of entities for deletion.

When you delete an entity, you remove any values, synonyms, patterns, or annotations that are associated with the entity. This data cannot be retrieved later. All dialog nodes that reference these entities or values must be updated manually to no longer reference the deleted content.

Go to the Entities page.
- To delete all entities, meaning the entities listed on this and any additional pages, do not select any individual entities. Instead, click the Delete all entities icon.
- To delete the entities that are listed on the current page only, select the checkbox in the header. This action selects all of the entities that are listed on the current page. Click Delete.
- To delete one or more specific entities, select the entities that you want to delete, and then click Delete.