317 lines
12 KiB
Plaintext
317 lines
12 KiB
Plaintext
[[search-suggesters-phrase]]
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=== Phrase Suggester
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NOTE: In order to understand the format of suggestions, please
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read the <<search-suggesters>> page first.
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The `term` suggester provides a very convenient API to access word
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alternatives on token basis within a certain string distance. The API
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allows accessing each token in the stream individually while
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suggest-selection is left to the API consumer. Yet, often pre-selected
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suggestions are required in order to present to the end-user. The
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`phrase` suggester adds additional logic on top of the `term` suggester
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to select entire corrected phrases instead of individual tokens weighted
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based on `ngram-langugage` models. In practice it this suggester will be
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able to make better decision about which tokens to pick based on
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co-occurence and frequencies.
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==== API Example
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The `phrase` request is defined along side the query part in the json
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request:
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[source,js]
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--------------------------------------------------
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curl -XPOST 'localhost:9200/_search' -d {
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"suggest" : {
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"text" : "Xor the Got-Jewel",
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"simple_phrase" : {
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"phrase" : {
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"analyzer" : "body",
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"field" : "bigram",
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"size" : 1,
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"real_word_error_likelihood" : 0.95,
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"max_errors" : 0.5,
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"gram_size" : 2,
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"direct_generator" : [ {
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"field" : "body",
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"suggest_mode" : "always",
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"min_word_len" : 1
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} ],
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"highlight": {
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"pre_tag": "<em>",
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"post_tag": "</em>"
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}
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}
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}
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}
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}
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--------------------------------------------------
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The response contains suggested scored by the most likely spell
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correction first. In this case we got the expected correction
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`xorr the god jewel` first while the second correction is less
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conservative where only one of the errors is corrected. Note, the
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request is executed with `max_errors` set to `0.5` so 50% of the terms
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can contain misspellings (See parameter descriptions below).
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[source,js]
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--------------------------------------------------
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{
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"took" : 5,
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"timed_out" : false,
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"_shards" : {
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"total" : 5,
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"successful" : 5,
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"failed" : 0
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},
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"hits" : {
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"total" : 2938,
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"max_score" : 0.0,
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"hits" : [ ]
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},
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"suggest" : {
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"simple_phrase" : [ {
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"text" : "Xor the Got-Jewel",
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"offset" : 0,
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"length" : 17,
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"options" : [ {
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"text" : "xorr the god jewel",
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"highlighted": "<em>xorr</em> the <em>god</em> jewel",
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"score" : 0.17877324
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}, {
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"text" : "xor the god jewel",
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"highlighted": "xor the <em>god</em> jewel",
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"score" : 0.14231323
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} ]
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} ]
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}
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}
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--------------------------------------------------
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==== Basic Phrase suggest API parameters
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[horizontal]
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`field`::
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the name of the field used to do n-gram lookups for the
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language model, the suggester will use this field to gain statistics to
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score corrections. This field is mandatory.
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`gram_size`::
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sets max size of the n-grams (shingles) in the `field`.
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If the field doesn't contain n-grams (shingles) this should be omitted
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or set to `1`. Note that Elasticsearch tries to detect the gram size
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based on the specified `field`. If the field uses a `shingle` filter the
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`gram_size` is set to the `max_shingle_size` if not explicitly set.
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`real_word_error_likelihood`::
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the likelihood of a term being a
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misspelled even if the term exists in the dictionary. The default it
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`0.95` corresponding to 5% or the real words are misspelled.
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`confidence`::
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The confidence level defines a factor applied to the
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input phrases score which is used as a threshold for other suggest
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candidates. Only candidates that score higher than the threshold will be
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included in the result. For instance a confidence level of `1.0` will
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only return suggestions that score higher than the input phrase. If set
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to `0.0` the top N candidates are returned. The default is `1.0`.
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`max_errors`::
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the maximum percentage of the terms that at most
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considered to be misspellings in order to form a correction. This method
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accepts a float value in the range `[0..1)` as a fraction of the actual
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query terms a number `>=1` as an absolute number of query terms. The
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default is set to `1.0` which corresponds to that only corrections with
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at most 1 misspelled term are returned.
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`separator`::
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the separator that is used to separate terms in the
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bigram field. If not set the whitespace character is used as a
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separator.
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`size`::
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the number of candidates that are generated for each
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individual query term Low numbers like `3` or `5` typically produce good
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results. Raising this can bring up terms with higher edit distances. The
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default is `5`.
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`analyzer`::
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Sets the analyzer to analyse to suggest text with.
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Defaults to the search analyzer of the suggest field passed via `field`.
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`shard_size`::
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Sets the maximum number of suggested term to be
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retrieved from each individual shard. During the reduce phase, only the
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top N suggestions are returned based on the `size` option. Defaults to
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`5`.
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`text`::
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Sets the text / query to provide suggestions for.
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`highlight`::
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Sets up suggestion highlighting. If not provided then
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no `highlighted` field is returned. If provided must
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contain exactly `pre_tag` and `post_tag` which are
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wrapped around the changed tokens. If multiple tokens
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in a row are changed the entire phrase of changed tokens
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is wrapped rather than each token.
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==== Smoothing Models
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The `phrase` suggester supports multiple smoothing models to balance
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weight between infrequent grams (grams (shingles) are not existing in
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the index) and frequent grams (appear at least once in the index).
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[horizontal]
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`stupid_backoff`::
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a simple backoff model that backs off to lower
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order n-gram models if the higher order count is `0` and discounts the
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lower order n-gram model by a constant factor. The default `discount` is
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`0.4`. Stupid Backoff is the default model.
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`laplace`::
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a smoothing model that uses an additive smoothing where a
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constant (typically `1.0` or smaller) is added to all counts to balance
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weights, The default `alpha` is `0.5`.
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`linear_interpolation`::
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a smoothing model that takes the weighted
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mean of the unigrams, bigrams and trigrams based on user supplied
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weights (lambdas). Linear Interpolation doesn't have any default values.
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All parameters (`trigram_lambda`, `bigram_lambda`, `unigram_lambda`)
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must be supplied.
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==== Candidate Generators
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The `phrase` suggester uses candidate generators to produce a list of
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possible terms per term in the given text. A single candidate generator
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is similar to a `term` suggester called for each individual term in the
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text. The output of the generators is subsequently scored in combination
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with the candidates from the other terms to for suggestion candidates.
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Currently only one type of candidate generator is supported, the
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`direct_generator`. The Phrase suggest API accepts a list of generators
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under the key `direct_generator` each of the generators in the list are
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called per term in the original text.
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==== Direct Generators
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The direct generators support the following parameters:
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[horizontal]
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`field`::
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The field to fetch the candidate suggestions from. This is
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an required option that either needs to be set globally or per
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suggestion.
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`size`::
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The maximum corrections to be returned per suggest text token.
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`suggest_mode`::
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The suggest mode controls what suggestions are
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included or controls for what suggest text terms, suggestions should be
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suggested. Three possible values can be specified:
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** `missing`: Only suggest terms in the suggest text that aren't in the
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index. This is the default.
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** `popular`: Only suggest suggestions that occur in more docs then the
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original suggest text term.
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** `always`: Suggest any matching suggestions based on terms in the
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suggest text.
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`max_edits`::
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The maximum edit distance candidate suggestions can have
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in order to be considered as a suggestion. Can only be a value between 1
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and 2. Any other value result in an bad request error being thrown.
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Defaults to 2.
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`prefix_len`::
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The number of minimal prefix characters that must
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match in order be a candidate suggestions. Defaults to 1. Increasing
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this number improves spellcheck performance. Usually misspellings don't
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occur in the beginning of terms.
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`min_word_len`::
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The minimum length a suggest text term must have in
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order to be included. Defaults to 4.
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`max_inspections`::
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A factor that is used to multiply with the
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`shards_size` in order to inspect more candidate spell corrections on
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the shard level. Can improve accuracy at the cost of performance.
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Defaults to 5.
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`min_doc_freq`::
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The minimal threshold in number of documents a
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suggestion should appear in. This can be specified as an absolute number
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or as a relative percentage of number of documents. This can improve
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quality by only suggesting high frequency terms. Defaults to 0f and is
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not enabled. If a value higher than 1 is specified then the number
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cannot be fractional. The shard level document frequencies are used for
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this option.
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`max_term_freq`::
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The maximum threshold in number of documents a
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suggest text token can exist in order to be included. Can be a relative
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percentage number (e.g 0.4) or an absolute number to represent document
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frequencies. If an value higher than 1 is specified then fractional can
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not be specified. Defaults to 0.01f. This can be used to exclude high
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frequency terms from being spellchecked. High frequency terms are
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usually spelled correctly on top of this also improves the spellcheck
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performance. The shard level document frequencies are used for this
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option.
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`pre_filter`::
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a filter (analyzer) that is applied to each of the
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tokens passed to this candidate generator. This filter is applied to the
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original token before candidates are generated.
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`post_filter`::
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a filter (analyzer) that is applied to each of the
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generated tokens before they are passed to the actual phrase scorer.
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The following example shows a `phrase` suggest call with two generators,
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the first one is using a field containing ordinary indexed terms and the
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second one uses a field that uses terms indexed with a `reverse` filter
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(tokens are index in reverse order). This is used to overcome the limitation
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of the direct generators to require a constant prefix to provide
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high-performance suggestions. The `pre_filter` and `post_filter` options
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accept ordinary analyzer names.
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[source,js]
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--------------------------------------------------
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curl -s -XPOST 'localhost:9200/_search' -d {
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"suggest" : {
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"text" : "Xor the Got-Jewel",
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"simple_phrase" : {
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"phrase" : {
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"analyzer" : "body",
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"field" : "bigram",
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"size" : 4,
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"real_word_error_likelihood" : 0.95,
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"confidence" : 2.0,
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"gram_size" : 2,
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"direct_generator" : [ {
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"field" : "body",
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"suggest_mode" : "always",
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"min_word_len" : 1
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}, {
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"field" : "reverse",
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"suggest_mode" : "always",
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"min_word_len" : 1,
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"pre_filter" : "reverse",
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"post_filter" : "reverse"
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} ]
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}
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}
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}
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}
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--------------------------------------------------
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`pre_filter` and `post_filter` can also be used to inject synonyms after
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candidates are generated. For instance for the query `captain usq` we
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might generate a candidate `usa` for term `usq` which is a synonym for
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`america` which allows to present `captain america` to the user if this
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phrase scores high enough.
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