OpenSearch/docs/plugins/mapper-annotated-text.asciidoc

[[mapper-annotated-text]]
=== Mapper Annotated Text Plugin

experimental[]

The mapper-annotated-text plugin provides the ability to index text that is a
combination of free-text and special markup that is typically used to identify
items of interest such as people or organisations (see NER or Named Entity Recognition
tools). 


The elasticsearch markup allows one or more additional tokens to be injected, unchanged, into the token
stream at the same position as the underlying text it annotates.

:plugin_name: mapper-annotated-text
include::install_remove.asciidoc[]

[[mapper-annotated-text-usage]]
==== Using the `annotated-text` field

The `annotated-text` tokenizes text content as per the more common `text` field (see 
"limitations" below) but also injects any marked-up annotation tokens directly into
the search index:

[source,js]
--------------------------
PUT my_index?include_type_name=true
{
  "mappings": {
    "_doc": {
      "properties": {
        "my_field": {
          "type": "annotated_text"
        }
      }
    }
  }
}
--------------------------
// CONSOLE

Such a mapping would allow marked-up text eg wikipedia articles to be indexed as both text
and structured tokens. The annotations use a markdown-like syntax using URL encoding of
one or more values separated by the `&` symbol.


We can use the "_analyze" api to test how an example annotation would be stored as tokens
in the search index:


[source,js]
--------------------------
GET my_index/_analyze
{
  "field": "my_field", 
  "text":"Investors in [Apple](Apple+Inc.) rejoiced."
}
--------------------------
// NOTCONSOLE

Response:

[source,js]
--------------------------------------------------
{
  "tokens": [
    {
      "token": "investors",
      "start_offset": 0,
      "end_offset": 9,
      "type": "<ALPHANUM>",
      "position": 0
    },
    {
      "token": "in",
      "start_offset": 10,
      "end_offset": 12,
      "type": "<ALPHANUM>",
      "position": 1
    },
    {
      "token": "Apple Inc.", <1> 
      "start_offset": 13,
      "end_offset": 18,
      "type": "annotation",
      "position": 2
    },
    {
      "token": "apple",
      "start_offset": 13,
      "end_offset": 18,
      "type": "<ALPHANUM>",
      "position": 2
    },
    {
      "token": "rejoiced",
      "start_offset": 19,
      "end_offset": 27,
      "type": "<ALPHANUM>",
      "position": 3
    }
  ]
}
--------------------------------------------------
// NOTCONSOLE

<1> Note the whole annotation token `Apple Inc.` is placed, unchanged as a single token in
the token stream and at the same position (position 2) as the text token (`apple`) it annotates.


We can now perform searches for annotations using regular `term` queries that don't tokenize
the provided search values. Annotations are a more precise way of matching as can be seen 
in this example where a search for `Beck` will not match `Jeff Beck` :

[source,js]
--------------------------
# Example documents
PUT my_index/_doc/1
{
  "my_field": "[Beck](Beck) announced a new tour"<2>
}

PUT my_index/_doc/2
{
  "my_field": "[Jeff Beck](Jeff+Beck&Guitarist) plays a strat"<1>
}

# Example search
GET my_index/_search
{
  "query": {
    "term": {
        "my_field": "Beck" <3>
    }
  }
}
--------------------------
// CONSOLE

<1> As well as tokenising the plain text into single words e.g. `beck`, here we 
inject the single token value `Beck` at the same position as `beck` in the token stream.
<2> Note annotations can inject multiple tokens at the same position - here we inject both
the very specific value `Jeff Beck` and the broader term `Guitarist`. This enables
broader positional queries e.g. finding mentions of a `Guitarist` near to `strat`.
<3> A benefit of searching with these carefully defined annotation tokens is that a query for 
`Beck` will not match document 2 that contains the tokens `jeff`, `beck` and `Jeff Beck`

WARNING: Any use of `=` signs in annotation values eg `[Prince](person=Prince)` will 
cause the document to be rejected with a parse failure. In future we hope to have a use for
the equals signs so wil actively reject documents that contain this today.


[[mapper-annotated-text-tips]]
==== Data modelling tips
===== Use structured and unstructured fields

Annotations are normally a way of weaving structured information into unstructured text for
higher-precision search.

`Entity resolution` is a form of document enrichment undertaken by specialist software or people 
where references to entities in a document are disambiguated by attaching a canonical ID.
The ID is used to resolve any number of aliases or distinguish between people with the
same name. The hyperlinks connecting Wikipedia's articles are a good example of resolved 
entity IDs woven into text. 

These IDs can be embedded as annotations in an annotated_text field but it often makes 
sense to include them in dedicated structured fields to support discovery via aggregations:

[source,js]
--------------------------
PUT my_index?include_type_name=true
{
  "mappings": {
    "_doc": {
      "properties": {
        "my_unstructured_text_field": {
          "type": "annotated_text"
        },
        "my_structured_people_field": {
          "type": "text",
          "fields": {
          	"keyword" :{
          	  "type": "keyword"
          	}
          }
        }
      }
    }
  }
}
--------------------------
// CONSOLE

Applications would then typically provide content and discover it as follows:

[source,js]
--------------------------
# Example documents
PUT my_index/_doc/1
{
  "my_unstructured_text_field": "[Shay](%40kimchy) created elasticsearch",
  "my_twitter_handles": ["@kimchy"] <1>
}

GET my_index/_search
{
  "query": {
    "query_string": {
        "query": "elasticsearch OR logstash OR kibana",<2>
        "default_field": "my_unstructured_text_field"
    }
  },
  "aggregations": {
  	"top_people" :{
  	    "significant_terms" : { <3>
	       "field" : "my_twitter_handles.keyword"
  	    }
  	}
  }
}
--------------------------
// CONSOLE

<1> Note the `my_twitter_handles` contains a list of the annotation values
also used in the unstructured text. (Note the annotated_text syntax requires escaping). 
By repeating the annotation values in a structured field this application has ensured that 
the tokens discovered in the structured field can be used for search and highlighting 
in the unstructured field.  
<2> In this example we search for documents that talk about components of the elastic stack
<3> We use the `my_twitter_handles` field here to discover people who are significantly
associated with the elastic stack.

===== Avoiding over-matching annotations
By design, the regular text tokens and the annotation tokens co-exist in the same indexed 
field but in rare cases this can lead to some over-matching.

The value of an annotation often denotes a _named entity_ (a person, place or company).
The tokens for these named entities are inserted untokenized, and differ from typical text 
tokens because they are normally:

* Mixed case e.g. `Madonna`
* Multiple words e.g. `Jeff Beck`
* Can have punctuation or numbers e.g. `Apple Inc.` or `@kimchy`

This means, for the most part, a search for a named entity in the annotated text field will
not have any false positives e.g. when selecting `Apple Inc.` from an aggregation result 
you can drill down to highlight uses in the text without "over matching" on any text tokens 
like the word `apple` in this context:

    the apple was very juicy
    
However, a problem arises if your named entity happens to be a single term and lower-case e.g. the 
company `elastic`. In this case, a search on the annotated text field for the token `elastic`
may match a text document such as this:

    he fired an elastic band

To avoid such false matches users should consider prefixing annotation values to ensure 
they don't name clash with text tokens e.g.

    [elastic](Company_elastic) released version 7.0 of the elastic stack today




[[mapper-annotated-text-highlighter]]
==== Using the `annotated` highlighter

The `annotated-text` plugin includes a custom highlighter designed to mark up search hits
in a way which is respectful of the original markup:

[source,js]
--------------------------
# Example documents
PUT my_index/_doc/1
{
  "my_field": "The cat sat on the [mat](sku3578)"
}

GET my_index/_search
{
  "query": {
    "query_string": {
        "query": "cats" 
    }
  },
  "highlight": {
    "fields": {
      "my_field": {
        "type": "annotated", <1>
        "require_field_match": false
      }
    }
  }
}
--------------------------
// CONSOLE
<1> The `annotated` highlighter type is designed for use with annotated_text fields

The annotated highlighter is based on the `unified` highlighter and supports the same
settings but does not use the `pre_tags` or `post_tags` parameters. Rather than using
html-like markup such as `<em>cat</em>` the annotated highlighter uses the same 
markdown-like syntax used for annotations and injects a key=value annotation where `_hit_term`
is the  key and the matched search term is the value e.g. 

    The [cat](_hit_term=cat) sat on the [mat](sku3578)

The annotated highlighter tries to be respectful of any existing markup in the original 
text:

* If the search term matches exactly the location of an existing annotation then the 
`_hit_term` key is merged into the url-like syntax used in the `(...)` part of the
existing annotation. 
* However, if the search term overlaps the span of an existing annotation it would break
the markup formatting so the original annotation is removed in favour of a new annotation
with just the search hit information in the results. 
* Any non-overlapping annotations in the original text are preserved in highlighter
selections


[[mapper-annotated-text-limitations]]
==== Limitations

The annotated_text field type supports the same mapping settings as the `text` field type
but with the following exceptions:

* No support for `fielddata` or `fielddata_frequency_filter`
* No support for `index_prefixes` or `index_phrases` indexing