Elasticsearch modify asciifolding - elasticsearch

ASCII Folding Token Filter folds "Ə"/"ə"(U+018F / U+0259) characters to "A"/"a". I need to modify or add fold to "E"/"e". char_filter doesn't help and doesn't preserve original
Add analyzer:
curl -XPUT 'localshot:9200/myix/_settings?pretty' -H 'Content-Type: application/json' -d'
{
"analysis" : {
"analyzer" : {
"default" : {
"tokenizer" : "standard",
"filter" : ["standard", "my_ascii_folding"]
}
},
"filter" : {
"my_ascii_folding" : {
"type" : "asciifolding",
"preserve_original" : true
}
}
}
}
'
Test result:
http://localhost:9200/myix/_analyze?text=üöğıəçşi_ÜÖĞIƏÇŞİ&filter=my_ascii_folding
{
"tokens": [
{
"token": "uogiacsi_UOGIACSI",
"start_offset": 0,
"end_offset": 17,
"type": "<ALPHANUM>",
"position": 0
},
{
"token": "üöğıəçşi_ÜÖĞIƏÇŞİ",
"start_offset": 0,
"end_offset": 17,
"type": "<ALPHANUM>",
"position": 0
}
]
}

When looking at Lucene's ASCIIFoldingFilter.java source file, it doesn indeed seem like Ə gets folded into an E and not a A. Even the ICU folding filter which is asciifolding on steroids, does the same folding.
However, there's an interesting discussion on the subject and it seems that given the pronunciation it should be folded into an a and not a e:
A quick search on English or French Wikipedia, where it currently gets folded, shows that it gets folded to an a! I would have expected an e based on orthography, but a makes sense in terms of pronunciation (in English, at least).
Someone else even thinks that neither a nor e makes sense:
That seems like a really bad decision. I don't think ə should fold to either of a or e.
Anyway, I don't think there is a way except using a char_filter or extending the ASCIIFoldingFilter and bundling it into an ES analysis plugin yourself.

Related

Elasticsearch's minimumShouldMatch for each member of an array

Consider an Elasticsearch entity:
{
"id": 123456,
"keywords": ["apples", "bananas"]
}
Now, imagine I would like to find this entity by searching for apple.
{
"match" : {
"keywords" : {
"query" : "apple",
"operator" : "AND",
"minimum_should_match" : "75%"
}
}
}
The problem is that the 75% minimum for matching would be required for both of the strings of the array – so nothing will be found. Is there a way to say something like minimumSouldMatch: "75% of any array fields"?
Note that I need to use AND as each item of keywords may be composed of longer text.
EDIT:
I tried the proposed solutions, but none of them was giving expected results. I guess the problem is that the text might be quite long, eg.:
["national gallery in prague", "narodni galerie v praze"]
I guess the fuzzy expansion is just not able to expand such long strings if you just start searching by "national g".
Would this may be be possible somehow via Nested objects?
{ keywords: [{keyword: "apples"}, {keyword: "babanas"}}
and then have minimumShouldMatch=1 on keywords and then 75% on each keyword?
As per doc
The match query is of type boolean. It means that the text provided is analyzed and the analysis process constructs a boolean query from the provided text. The operator parameter can be set to or or and to control the boolean clauses (defaults to or). The minimum number of optional should clauses to match can be set using the minimum_should_match parameter.
If you are searching for multiple tokens example "apples mangoes" and set minimum as 100%. It will mean both tokens should be present in document. If you set it at 50% , it means at least one of these should be present.
If you want to match tokens partially
You can use fuzziness parameter
Using fuzziness you can set maximum edit distance allowed for matching
{
"query": {
"match": {
"keywords": {
"query": "apple",
"fuzziness": "auto"
}
}
}
}
If you are trying to match word to its root form you can use "stemming" token filter
PUT index-name
{
"settings": {
"analysis": {
"analyzer": {
"my_analyzer": {
"tokenizer": "standard",
"filter": [ "stemmer" ]
}
}
}
},
"mappings": {
"properties": {
"keywords":{
"type": "text",
"analyzer": "my_analyzer"
}
}
}
}
Tokens generated
GET index-name/_analyze
{
"text": ["apples", "bananas"],
"analyzer": "my_analyzer"
}
"tokens" : [
{
"token" : "appl",
"start_offset" : 0,
"end_offset" : 6,
"type" : "<ALPHANUM>",
"position" : 0
},
{
"token" : "banana",
"start_offset" : 7,
"end_offset" : 14,
"type" : "<ALPHANUM>",
"position" : 101
}
]
stemming breaks words to their root form.
You can also explore n-grams, edge grams for partial matching

ElasticSearch custom analyzer breaks words containing special characters

If user searches for foo(bar), elasticsearch breaks it into foo and bar.
What I'm trying to achieve, is when a user types in say, i want a foo(bar), I match exactly an item named foo(bar), the name is fixed, and it will be used by a filter, so it is set to a keyword type.
The approximate steps I did,
define a custom analyzer
define a dictionary containing foo(bar)
define a synonym mapping containing abc => foo(bar)
Now, when I search for abc, elasticsearch translates it to foo(bar), but then it breaks it into foo and bar.
The question, as you may have guessed, is how to preserve special characters in elasticsearch analyzer?
I tried to use quotes(") in the dictionary file, like "foo(bar)", it didn't work.
Or is there maybe another way to work around this problem?
By the way, I'm using foo(bar) here just for simplicity, the actual case is much more complicated.
Thanks in advance.
---- edit ----
Thanks to #star67, now I realized that it is the issue of the tokenizer.
I am using the plugin medcl/elasticsearch-analysis-ik, which provides an ik_smart tokenizer, which is designed for the Chinese language.
Although I realized what the real problem is, I still don't know how to solve it. I mean, I have to use the ik_smart tokenizer, but how do I modify it to exclude some special characters?
I know that I can define a custom pattern tokenizer like this as #star67 provided:
{
"tokenizer": {
"my_tokenizer": {
"type": "pattern",
"pattern": "[^\\w\\(\\)]+"
}
}
}
But I also want to use the ik_smart tokenizer, because in Chinese, words and characters are not separated by space, for example 弹性搜索很厉害 should be tokenized as ['弹性', '搜索', '很', '厉害'], words can only be split based on a dictionary, so the default behavior is not desirable. What I want is maybe something like this:
{
"tokenizer": {
"my_tokenizer": {
"tokenizer": "ik_smart",
"ignores": "[\\w\\(\\)]+"
}
}
}
And I couldn't find an equivalent setting in elasticsearch.
Is it that I have to build my own plugin to achieve this?
You might want to use another tokenizer in your custom analyzer for your index.
For example, the standard tokenizer (used via analyzer for short) splits by all non-word characters (\W+):
POST _analyze
{
"analyzer": "standard",
"text": "foo(bar)"
}
==>
{
"tokens" : [
{
"token" : "foo",
"start_offset" : 0,
"end_offset" : 3,
"type" : "<ALPHANUM>",
"position" : 0
},
{
"token" : "bar",
"start_offset" : 4,
"end_offset" : 7,
"type" : "<ALPHANUM>",
"position" : 1
}
]
}
Compare to a custom tokenizer, that splits by all non-word characters except the ( and ) ( which is [^\w\(\)]+):
PUT my-index-000001
{
"settings": {
"analysis": {
"analyzer": {
"my_analyzer": {
"tokenizer": "my_tokenizer"
}
},
"tokenizer": {
"my_tokenizer": {
"type": "pattern",
"pattern": "[^\\w\\(\\)]+"
}
}
}
}
}
POST my-index-000001/_analyze
{
"analyzer": "my_analyzer",
"text": "foo(bar)"
}
===>
{
"tokens" : [
{
"token" : "foo(bar)",
"start_offset" : 0,
"end_offset" : 8,
"type" : "word",
"position" : 0
}
]
}
I used a Pattern Tokenier as an example to exclude certain symbols (( and ) in your case) from being used in tokenization.

Elasticsearch : Problem with querying document where "." is included in field

I have an index where some entries are like
{
"name" : " Stefan Drumm"
}
...
{
"name" : "Dr. med. Elisabeth Bauer"
}
The mapping of the name field is
{
"name": {
"type": "text",
"analyzer": "index_name_analyzer",
"search_analyzer": "search_cross_fields_analyzer"
}
}
When I use the below query
GET my_index/_search
{"size":10,"query":
{"bool":
{"must":
[{"match":{"name":{"query":"Stefan Drumm","operator":"AND"}}}]
,"boost":1.0}},
"min_score":0.0}
It returns the first document.
But when I try to get the second document using the query below
GET my_index/_search
{"size":10,"query":
{"bool":
{"must":
[{"match":{"name":{"query":"Dr. med. Elisabeth Bauer","operator":"AND"}}}]
,"boost":1.0}},
"min_score":0.0}
it is not returning anything.
Things I can't do
can't change the index
can't use the term query.
change the operator to 'OR', because in that case it will return multiple entries, which I don't want.
What I am doing wrong and how can I achieve this by modifying the query?
You have configured different analyzers for indexing and searching (index_name_analyzer and search_cross_fields_analyzer). If these analyzers process the input Dr. med. Elisabeth Bauer in an incompatible way, the search isn't going to match. This is described in more detail in Index and search analysis, as well as in Controlling Analysis.
You don't provide the definition of these two analyzers, so it's hard to guess from your question what they are doing. Depending on the analyzers, it may be possible to preprocess your query string (e.g. by removing .) before executing the search so that the search will match.
You can investigate how analysis affects your search by using the _analyze API, as described in Testing analyzers. For your example, the commands
GET my_index/_analyze
{
"analyzer": "index_name_analyzer",
"text": "Dr. med. Elisabeth Bauer"
}
and
GET my_index/_analyze
{
"analyzer": "search_cross_fields_analyzer",
"text": "Dr. med. Elisabeth Bauer"
}
should show you how the two analyzers configured for your index treats the target string, which might provide you with a clue about what's wrong. The response will be something like
{
"tokens": [
{
"token": "dr",
"start_offset": 0,
"end_offset": 2,
"type": "<ALPHANUM>",
"position": 0
},
{
"token": "med",
"start_offset": 4,
"end_offset": 7,
"type": "<ALPHANUM>",
"position": 1
},
{
"token": "elisabeth",
"start_offset": 9,
"end_offset": 18,
"type": "<ALPHANUM>",
"position": 2
},
{
"token": "bauer",
"start_offset": 19,
"end_offset": 24,
"type": "<ALPHANUM>",
"position": 3
}
]
}
For the example output above, the analyzer has split the input into one token per word, lowercased each word, and discarded all punctuation.
My guess would be that index_name_analyzer preserves punctuation, while search_cross_fields_analyzer discards it, so that the tokens won't match. If this is the case, and you can't change the index configuration (as you state in your question), one other option would be to specify a different analyzer when running the query:
GET my_index/_search
{
"query": {
"bool": {
"must": [
{
"match": {
"name": {
"query": "Dr. med. Elisabeth Bauer",
"operator": "AND",
"analyzer": "index_name_analyzer"
}
}
}
],
"boost": 1
}
},
"min_score": 0
}
In the query above, the analyzer parameter has been set to override the search analysis to use the same analyzer (index_name_analyzer) as the one used when indexing. What analyzer might make sense to use depends on your setup. Ideally, you should configure the analyzers to align so that you don't have to override at search time, but it sounds like you are not living in an ideal world.

Simple Elasticsearch PDF Text Search using german language

I can handle/extract the text from my PDF-Files, I don't know quite know if I am going the right way about how to store my content in Elasticsearch.
My PDF-Texts are mostly German - with letters like "ö", "ä", etc.
In order to store EVERY character of the content, I "escape" necessary characters and encode them properly to JSON so I can store them.
For example:
I want to store the following (PDF) text:
Öffentliche Verkehrsmittel. TestPath: C:\Windows\explorer.exe
I convert and upload it to Elasticsearch like this:
{"text":"\\u00D6ffentliche Verkehrsmittel. TestPath: C:\\\\Windows\\\\explorer.exe"}
My question is: Is this the right way to store documents like this?
Elasticsearch comes up with a wide range of inbuilt language-specific analyzer and if you are creating the text field and storing your data, by default standard analyzer is used. which you change like below:
{
"mappings": {
"properties": {
"title.german" :{
"type" :"text",
"analyzer" : "german"
}
}
}
}
You can also check the tokens generated by language analyzer in your case german using analyze API
{
"text" : "Öffentliche",
"analyzer" : "german"
}
And generated token
{
"tokens": [
{
"token": "offentlich",
"start_offset": 0,
"end_offset": 11,
"type": "<ALPHANUM>",
"position": 0
}
]
}
Tokens for Ö
{
"text" : "Ö",
"analyzer" : "german"
}
{
"tokens": [
{
"token": "o",
"start_offset": 0,
"end_offset": 1,
"type": "<ALPHANUM>",
"position": 0
}
]
}
Note:- it converted it to plain text, so now whether you search for Ö or ö it will come in the search result, as the same analyzer is applied at query time if you use the match query.

Elasticsearch wildcard character is not matching numbers

I am searching elasticsearch index by using following query string:
curl -XGET 'http://localhost:9200/index/type/_search' -d '{
"query": {
"query_string" : {
"default_field" : "keyword",
"query" : "file*.tif"
}
}
}'
Schema for keyword field is as follows:
"keyword" : {"type" : "string", "store" : "yes", "index" : "analyzed" }
The problem with above query is it doesn't retrieve results for keyword like file001.tif while file001_copy.tif is retrieved. Match query is retrieving results correctly. Is this a limitation of Query_String or am I missing something?
You can see your problem by analyzing the string that you're indexing
curl "localhost:9200/_analyze" -d "file001.tif" | python -mjson.tool
{
"tokens": [
{
"end_offset": 7,
"position": 1,
"start_offset": 0,
"token": "file001",
"type": "<ALPHANUM>"
},
{
"end_offset": 11,
"position": 2,
"start_offset": 8,
"token": "tif",
"type": "<ALPHANUM>"
}
]
}
curl "localhost:9200/_analyze" -d "file001_copy.tif" | python -mjson.tool
{
"tokens": [
{
"end_offset": 16,
"position": 1,
"start_offset": 0,
"token": "file001_copy.tif",
"type": "<ALPHANUM>"
}
]
}
The standard analyzer file001.tif is splitting the tokens up to file001 and tif
but file001_copy.tif is not. so when you go search for file its only hitting file001_copy.tif because its the only thing that fits your criteria (has to have a token that has 'file' + 0 or more characters AND 'tif' in it)
You probably want to use a whitespace or keyword analyzer in tandem with a lowercase filter, to make it work the way you want to.

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