I have 2 documents, and am searching for the keyword "Twitter". Suppose both documents are blog posts with a "tags" field.
Document A has ONLY 1 term in the "tags" field, and it's "Twitter".
Document B has 100 terms in the "tags" field, but 3 of them is "Twitter".
Elastic Search gives the higher score to Document A even though Document B has a higher frequency. But the score is "diluted" because it has more terms. How do I give Document B a higher score, since it has a higher frequency of the search term?
I know ElasticSearch/Lucene performs some normalization based on the number of terms in the document. How can I disable this normalization, so that Document B gets a higher score above?
As the other answer says it would be interesting to see whether you have the same result on a single shard. I think you would and that depends on the norms for the tags field, which is taken into account when computing the score using the tf/idf similarity (default).
In fact, lucene does take into account the term frequency, in other words the number of times the term appears within the field (1 or 3 in your case), and the inverted document frequency, in other words how the term is frequent in the index, in order to compare it with other terms in the query (in your case it doesn't make any difference if you are searching for a single term).
But there's another factor called norms, that rewards shorter fields and take into account eventual index time boosting, which can be per field (in the mapping) or even per document. You can verify that norms are the reason of your result enabling the explain option in your search request and looking at the explain output.
I guess the fact that the first document contains only that tag makes it more important that the other ones that contains that tag multiple times but a lot of ther tags as well. If you don't like this behaviour you can just disable norms in your mapping for the tags field. It should be enabled by default if the field is "index":"analyzed" (default). You can either switch to "index":"not_analyzed" if you don't want your tags field to be analyzed (it usually makes sense but depends on your data and domain) or add the "omit_norms": true option in the mapping for your tags field.
Are the documents found on different shards? From Elastic search documentation:
"When a query is executed on a specific shard, it does not take into account term frequencies and other search engine information from the other shards. If we want to support accurate ranking, we would need to first execute the query against all shards and gather the relevant term frequencies, and then, based on it, execute the query."
The solution is to specify the search type. Use dfs_query_and_fetch search type to execute an initial scatter phase which goes and computes the distributed term frequencies for more accurate scoring.
You can read more here.
Related
Context:
We are using Azure Cognitive Services in a mobile app to search patient diagnostic codes (ICD10 codes).
The ICD10 code list is approximately 94,000 items. For anyone interested here is a list.
We currently have set-up a standard Lucene analyser on the diagnostic description field
Requirement:
We want to provide a really good search as you type experience, which provides the most relevant suggestions
Using the Suggest method with the fuzzy parameter set to true works reasonably well for a single search term:
As you can see it does well in finding partial matches and is resilient to typos.
The issue comes in when I add a second search term. E.g. I want to search for asthma that is moderate:
In both these examples, there is no match.
So when searching for more than one term, requiring the user to express this in the sequence that this is in the data is not a good user experience.
Using the Search method instead, we can overcome the problem of finding matches where 2 search terms are supplied that do not appear consecutively in the data:
And this is resilient to typos
However, this is not good at finding partial matches (like the Suggest does).
E.g. in this search, we would still want the term moderate to be picked up:
Seemingly if we could combine a wild card search with a fuzzy search we could solve this problem. e.g. supplying the following search phrase: ashtma~* AND moder~*.
But from what we have seen this syntax is not supported.
Any suggestions on how to overcome this limitation so we can get the best of both worlds, i.e:
For 2 or more search terms, it will work on partial matches
And the search terms are treated independently and do not need to appear consecutively in the data
Many thanks in advance,
Andreas.
I recommend using (or at least experimenting with) Lucene ngrams.
An example custom analyzer can use the NGramTokenFilter.
This filter splits each source token into one or more indexed tokens by chopping up the source into substrings of different lengths.
An example from the above link:
"abc" will give "a", "ab", "abc", "b", "bc", "c"
You can, as an example, set each token to be from 3 to 5 characters long (but this is one of the areas where you can experiment with different settings).
When you use this analyzer for indexing, it's going to create many more tokens (larger index) but that gives you more searching flexibility.
Use the same analyzer for searching.
If the user enters the following two words as their search values:
ashtma moder
You would convert that into the following Lucene search phrase:
ashtma~ AND moder~
This will find the following hits:
doc id = 12877
field = Moderate persistent asthma with status asthmaticus
doc id = 12874
field = Moderate persistent asthma
doc id = 12875
field = Moderate persistent asthma, uncomplicated
doc id = 12876
field = Moderate persistent asthma with (acute) exacerbation
doc id = 94210
field = Family history of asthma and oth chronic lower resp diseases
doc id = 6970
field = Xanthelasma of right lower eyelid
doc id = 6973
field = Xanthelasma of left lower eyelid
doc id = 6979
field = Chloasma of right lower eyelid and periocular area
doc id = 6982
field = Chloasma of left lower eyelid and periocular area
As you can see it does find some false positives, but the first four hits (the highest scored) are the ones you want.
You can see how this approach performs in terms of index size and search speed.
One reason for suggesting ngrams is your point about wanting to handle mis-spellings: ngrams may help to isolate spelling mistakes into smaller tokens,since the ~ fuzzy search operator is fairly limited in what it can handle. But, definitely experiment with different ngram lengths - and maybe also without using ngrams at all.
Using SOLR version 4.3, it appears that SOLR is valuing the percentage of matching terms more than the number of matching terms.
For example, we do a search for Dog and a document with just the word dog and a three other words returns. We have another article with hundreds of words, that has the word dog in it 27 times.
I would expect the second article to return first. However, the one with one word out of three returns first. I was hoping to find out what in SOLR controls this so I can make the appropriate modifications. I have looked the SOLR documentation and have seen COORD mentioned, but it seems to indicate that the article with 27 references should return first. Any help would be appreciated.
For 4.x Solr still used regular TF/IDF as its scoring formula, and you can see the Lucene implementation detailed in the documentation for TFIDFSimilarity.
For your question, the two factors that affect the score is:
The length of the field, as given in norm():
norm(t,d) encapsulates a few (indexing time) boost and length factors:
Field boost - set by calling field.setBoost() before adding the field to a document.
lengthNorm - computed when the document is added to the index in accordance with the number of tokens of this field in the document, so that shorter fields contribute more to the score. LengthNorm is computed by the Similarity class in effect at indexing.
.. while the number of terms matching (not their frequency), is given by coord():
coord(q,d) is a score factor based on how many of the query terms are found in the specified document. Typically, a document that contains more of the query's terms will receive a higher score than another document with fewer query terms. This is a search time factor computed in coord(q,d) by the Similarity in effect at search time.
There are a few settings in your schema that can affect how Solr scores the documents in your example:
omitNorms
If true, omits the norms associated with this field (this disables length normalization for the field, and saves some memory)
.. this will remove the norm() part of the score.
omitTermFreqAndPositions
If true, omits term frequency, positions, and payloads from postings for this field.
.. and this will remove the boost from multiple occurrences of the same term. Be aware that this will remove positions as well, making phrase queries impossible.
But you should also consider upgrading Solr, as the BM25 similarity that's the default from 6.x usually performs better. I can't remember if a version is available for 4.3.
I have a Lucene indexed corpus of more than 1 million documents.
I am searching for named entities such as "Susan Witting" by using the the Lucene java API for queries.
I would like to expand my queries by also searching for "Sue Witting" for example but would like that term to have a lower weight than the main query term.
How can I go about doing that?
I found infos about the boosting option in the Lucene Manual. But it seems to be set at indexing and it needs fields.
You can boost each query clause independently. See the Query Javadoc.
If you want to give different weight to the words of a term. Then
Query#setBoost(float)
is not useful. A better way is:
Term term = new Term("some_key", "stand^3 firm^2 always");
This allows to give different weight to words in the same term query. Here, the word stand boosted by three but always is has the default boost value.
Is it possible to determine that specific terms are more important then other when creating the index (not when querying it) ?
Consider for example a synonym filter:
doc 1: "this is a nice car"
doc 2: "this is a nice vehicle"
I want to add the term vehicle to the first doc and the term car to the second doc,
but I want that if later the index is queried with the word car then the first document will be scored higher then the second one and if queried for vehicle it will be the other way around.
Will calling setBoost on the fields before adding them to their respective documents do the trick?
Or maybe I should add the synonyms to a different field name?
Or am I looking at this from a wrong point of view ?
Thanks
Setting boost on a filed affects all terms in that field so this wouldn't work in your case.
But it should be posible using Lucene payloads (a byte array that can be set for every term). You would use them to set term specific boosts (vehicle to 0.5 for doc 1, for example). Then you'll implement your own Similarity and override scorePayload() method to decode that boost and then use PayloadTermQuery which allows you to contribute to the score based on the boots you have in the payload for that term.
We have an application where every term position in a document is associated with an "engine score".
A term query should then be scored according to the sum of "engine scores" of the term in a document, rather than on the term frequency.
For example, term frequency of 5 with an average engine score of 100 should be equivalent to term frequency of 1 with engine score 500.
I understood that if I keep the engine score per position in the payload, I will be able to use scorePayload in combination of a summary version of PayloadFunction to get the sum of engine scores of a term in a document, and so will be able to achieve my goal.
There are two issues with this solution:
Even the simplest term query should scan the positions file in order to get the payloads, which could be a performance issue.
We would prefer to index the sum of engine scores in advance per document, in addition to the term frequency. This is some sort of payload in the document level. Does Lucene support that or have any other solution for this issue ?
The "engine score" of a phrase occurrence is defined as the multiplication of engine scores of the terms that compose the phrase.
So in scorePayload I need the payloads of all the terms in the phrase in order to be able to appropriately score the phrase occurrence.
As much as I understand, the current interface of scorePayload does not provide this information.
Is there another way this can be achieved in Lucene ?
One workaround for a document-level payload is to create a single Lucene document / your document that just contains the engine score for your whole document as a specially-named field (different from all other Lucene document field names). You can then combine / extract that document during your searches. Not much of a workaround, but there it is.