[[char-filters]]
=== Tidying Up Input Text

Tokenizers produce the best results when the input text is clean, valid
text, where valid means that it follows the punctuation rules that the
Unicode algorithm expects.(((“text”, “tidying up text input for tokenizers”)))(((“words”, “identifying”, “tidying up text input”))) Quite often, though, the text we need to process
is anything but clean. Cleaning it up before tokenization improves the quality
of the output.

==== Tokenizing HTML

Passing HTML through the standard tokenizer or the icu_tokenizer produces
poor results.(((“HTML, tokenizing”))) These tokenizers just don’t know what to do with the HTML tags.
For example:

[source,js]

GET /_analyzer?tokenizer=standard

Some déjà vu website

The standard tokenizer(((“standard tokenizer”, “tokenizing HTML”))) confuses HTML tags and entities, and emits the
following tokens: p, Some, d, eacute, j, agrave, vu, a,
href, http, somedomain.com, website, a. Clearly not what was
intended!

Character filters can be added to an analyzer to (((“character filters”)))preprocess the text
before it is passed to the tokenizer. In this case, we can use the
html_strip character filter(((“analyzers”, “adding character filters to”)))(((“html_strip character filter”))) to remove HTML tags and to decode HTML entities
such as é into the corresponding Unicode characters.

Character filters can be tested out via the analyze API by specifying them
in the query string:

[source,js]

GET /_analyzer?tokenizer=standard&char_filters=html_strip

Some déjà vu website

To use them as part of the analyzer, they should be added to a custom
analyzer definition:

[source,js]

PUT /my_index
{
“settings”: {
“analysis”: {
“analyzer”: {
“my_html_analyzer”: {
“tokenizer”: “standard”,
“char_filter”: [ “html_strip” ]
}
}
}
}

}

Once created, our new my_html_analyzer can be tested with the analyze API:

[source,js]

GET /my_index/_analyzer?analyzer=my_html_analyzer

Some déjà vu website

This emits the tokens that we expect: Some, ++déjà++, vu, website.

==== Tidying Up Punctuation

The standard tokenizer and icu_tokenizer both understand that an
apostrophe within a word should be treated as part of the word, while single
quotes that surround a word should not.(((“standard tokenizer”, “handling of punctuation”)))(((“icu_tokenizer”, “handling of punctuation”)))(((“punctuation”, “tokenizers' handling of”))) Tokenizing the text You're my 'favorite'. would correctly emit the tokens You're, my, favorite.

Unfortunately,(((“apostrophes”))) Unicode lists a few characters that are sometimes used
as apostrophes:

U+0027::
Apostrophe (')—the original ASCII character

U+2018::
Left single-quotation mark (‘)—opening quote when single-quoting

U+2019::
Right single-quotation mark (’)—closing quote when single-quoting, but also the preferred character to use as an apostrophe

Both tokenizers treat these three characters as an apostrophe (and thus as
part of the word) when they appear within a word. Then there are another three
apostrophe-like characters:

U+201B::
Single high-reversed-9 quotation mark (‛)—same as U+2018 but differs in appearance

U+0091::
Left single-quotation mark in ISO-8859-1—should not be used in Unicode

U+0092::
Right single-quotation mark in ISO-8859-1—should not be used in Unicode

Both tokenizers treat these three characters as word boundaries—a place to
break text into tokens.(((“quotation marks”))) Unfortunately, some publishers use U+201B as a
stylized way to write names like M‛coy, and the second two characters may well
be produced by your word processor, depending on its age.

Even when using the `acceptable'' quotation marks, a word written with a single right quotation mark—You’re—is not the same as the word written with an apostrophe—You’re`—which means that a query for one variant
will not find the other.

Fortunately, it is possible to sort out this mess with the mapping character
filter,(((“character filters”, “mapping character filter”)))(((“mapping character filter”))) which allows us to replace all instances of one character with
another. In this case, we will replace all apostrophe variants with the
simple U+0027 apostrophe:

[source,js]

PUT /my_index
{
“settings”: {
“analysis”: {
“char_filter”: { <1>
“quotes”: {
“type”: “mapping”,
“mappings”: [ <2>
“\u0091=>\u0027”,
“\u0092=>\u0027”,
“\u2018=>\u0027”,
“\u2019=>\u0027”,
“\u201B=>\u0027”
]
}
},
“analyzer”: {
“quotes_analyzer”: {
“tokenizer”: “standard”,
“char_filter”: [ “quotes” ] <3>
}
}
}
}

}

<1> We define a custom char_filter called quotes that
maps all apostrophe variants to a simple apostrophe.

<2> For clarity, we have used the JSON Unicode escape syntax
for each character, but we could just have used the
characters themselves: "‘=>'".

<3> We use our custom quotes character filter to create
a new analyzer called quotes_analyzer.

As always, we test the analyzer after creating it:

[source,js]

GET /my_index/_analyze?analyzer=quotes_analyzer

You’re my ‘favorite’ M‛Coy

This example returns the following tokens, with all of the in-word
quotation marks replaced by apostrophes: You're, my, favorite, M'Coy.

The more effort that you put into ensuring that the tokenizer receives good-quality input, the better your search results will be.