If the term "H5N1" only exists in 5 documents in a 10 million document index and yet is found in 4 of the 100 documents that make up a user's search results
When querying an index of all crimes from all police forces, what these results show is that the British Transport Police force
stand out as a force dealing with a disproportionately large number of bicycle thefts. Ordinarily, bicycle thefts represent only 1% of crimes (66799/5064554)
The numbers returned for scores are primarily intended for ranking different suggestions sensibly rather than something easily understood by end users. The scores are derived from the doc frequencies in _foreground_ and _background_ sets. In brief, a term is considered significant if there is a noticeable difference in the frequency in which a term appears in the subset and in the background. The way the terms are ranked can be configured, see "Parameters" section.
free-text "movie_description" field. Take the suggested words (I'll leave them to your imagination) and then search for all movies NOT marked as category:adultMovie but containing these keywords.
You now have a ranked list of badly-categorized movies that you should reclassify or at least remove from the "familyFriendly" category.
The significance score from each term can also provide a useful `boost` setting to sort matches.
Using the `minimum_should_match` setting of the `terms` query with the keywords will help control the balance of precision/recall in the result set i.e
a high setting would have a small number of relevant results packed full of keywords and a setting of "1" would produce a more exhaustive results set with all documents containing _any_ keyword.
free-text field and use them in a `terms` query on the same field with a `highlight` clause to present users with example snippets of documents. When the terms
are presented unstemmed, highlighted, with the right case, in the right order and with some context, their significance/meaning is more readily apparent.
Ordinarily, the foreground set of documents is "diffed" against a background set of all the documents in your index.
However, sometimes it may prove useful to use a narrower background set as the basis for comparisons.
For example, a query on documents relating to "Madrid" in an index with content from all over the world might reveal that "Spanish"
was a significant term. This may be true but if you want some more focused terms you could use a `background_filter`
on the term 'spain' to establish a narrower set of documents as context. With this as a background "Spanish" would now
be seen as commonplace and therefore not as significant as words like "capital" that relate more strongly with Madrid.
Note that using a background filter will slow things down - each term's background frequency must now be derived on-the-fly from filtering posting lists rather than reading the index's pre-computed count for a term.
If there is the equivalent of a `match_all` query or no query criteria providing a subset of the index the significant_terms aggregation should not be used as the
aggregation that later discards many candidate terms. It is advisable in these cases to perform two searches - the first to provide a rationalized list of
The counts of how many documents contain a term provided in results are based on summing the samples returned from each shard and
as such may be:
* low if certain shards did not provide figures for a given term in their top sample
* high when considering the background frequency as it may count occurrences found in deleted documents
Like most design decisions, this is the basis of a trade-off in which we have chosen to provide fast performance at the cost of some (typically small) inaccuracies.
The scores are derived from the doc frequencies in _foreground_ and _background_ sets. The _absolute_ change in popularity (foregroundPercent - backgroundPercent) would favor common terms whereas the _relative_ change in popularity (foregroundPercent/ backgroundPercent) would favor rare terms. Rare vs common is essentially a precision vs recall balance and so the absolute and relative changes are multiplied to provide a sweet spot between precision and recall.
===== mutual information
Mutual information as described in "Information Retrieval", Manning et al., Chapter 13.5.1 can be used as significance score by adding the parameter
Mutual information does not differentiate between terms that are descriptive for the subset or for documents outside the subset. The significant terms therefore can contain terms that appear more or less frequent in the subset than outside the subset. To filter out the terms that appear less often in the subset than in documents outside the subset, `include_negatives` can be set to `false`.
Per default, the assumption is that the documents in the bucket are also contained in the background. If instead you defined a custom background filter that represents a different set of documents that you want to compare to, set
Chi square behaves like mutual information and can be configured with the same parameters `include_negatives` and `background_is_superset`.
===== google normalized distance
Google normalized distance as described in "The Google Similarity Distance", Cilibrasi and Vitanyi, 2007 (http://arxiv.org/pdf/cs/0412098v3.pdf) can be used as significance score by adding the parameter
A simple calculation of the number of documents in the foreground sample with a term divided by the number of documents in the background with the term.
By default this produces a score greater than zero and less than one.
The benefit of this heuristic is that the scoring logic is simple to explain to anyone familiar with a "per capita" statistic. However, for fields with high cardinality there is a tendency for this heuristic to select the rarest terms such as typos that occur only once because they score 1/1 = 100%.
It would be hard for a seasoned boxer to win a championship if the prize was awarded purely on the basis of percentage of fights won - by these rules a newcomer with only one fight under his belt would be impossible to beat.
Multiple observations are typically required to reinforce a view so it is recommended in these cases to set both `min_doc_count` and `shard_min_doc_count` to a higher value such as 10 in order to filter out the low-frequency terms that otherwise take precedence.
Roughly, `mutual_information` prefers high frequent terms even if they occur also frequently in the background. For example, in an analysis of natural language text this might lead to selection of stop words. `mutual_information` is unlikely to select very rare terms like misspellings. `gnd` prefers terms with a high co-occurrence and avoids selection of stopwords. It might be better suited for synonym detection. However, `gnd` has a tendency to select very rare terms that are, for example, a result of misspelling. `chi_square` and `jlh` are somewhat in-between.
It is hard to say which one of the different heuristics will be the best choice as it depends on what the significant terms are used for (see for example [Yang and Pedersen, "A Comparative Study on Feature Selection in Text Categorization", 1997](http://courses.ischool.berkeley.edu/i256/f06/papers/yang97comparative.pdf) for a study on using significant terms for feature selection for text classification).
will cause extra network traffic and RAM usage so this is quality/cost trade off that needs to be balanced. If `shard_size` is set to -1 (the default) then `shard_size` will be automatically estimated based on the number of shards and the `size` parameter.
Terms that score highly will be collected on a shard level and merged with the terms collected from other shards in a second step. However, the shard does not have the information about the global term frequencies available. The decision if a term is added to a candidate list depends only on the score computed on the shard using local shard frequencies, not the global frequencies of the word. The `min_doc_count` criterion is only applied after merging local terms statistics of all shards. In a way the decision to add the term as a candidate is made without being very _certain_ about if the term will actually reach the required `min_doc_count`. This might cause many (globally) high frequent terms to be missing in the final result if low frequent but high scoring terms populated the candidate lists. To avoid this, the `shard_size` parameter can be increased to allow more candidate terms on the shards. However, this increases memory consumption and network traffic.
The parameter `shard_min_doc_count` regulates the _certainty_ a shard has if the term should actually be added to the candidate list or not with respect to the `min_doc_count`. Terms will only be considered if their local shard frequency within the set is higher than the `shard_min_doc_count`. If your dictionary contains many low frequent words and you are not interested in these (for example misspellings), then you can set the `shard_min_doc_count` parameter to filter out candidate terms on a shard level that will with a reasonable certainty not reach the required `min_doc_count` even after merging the local frequencies. `shard_min_doc_count` is set to `1` per default and has no effect unless you explicitly set it.
Setting `shard_min_doc_count` too high will cause significant candidate terms to be filtered out on a shard level. This value should be set much lower than `min_doc_count/#shards`.
