We are now using Azure search Microsoft language analyzers on some of language specific fields. In most of cases, it has better relevance than standard Lucene language analyzers. But we found an issue when verifying en.microsoft analyzer.
The problem is, if the field value contains digits. The analyzer is smart to allow redundant “0” in front the digit.
For example:
POST /analyze?api-version=2017-11-11
{
"text": "1",
"analyzer": "en.microsoft"
}
The response is:
"tokens": [
{
"token": "1",
"startOffset": 0,
"endOffset": 2,
"position": 0
},
{
"token": "nn1",
"startOffset": 0,
"endOffset": 2,
"position": 0
}
]
The problem is, that if the field value is “01”, then all text like “01”, “001”, “0001”, … will match that field.
We have a field to save the product attribute name/value pairs, for example, “brand:Contoso|size:1”. Then even searching “0001” can return the document with this field value. This is not what we want.
So, my question is, is there any way to customize the en.microsoft analyzer so that, we can take advantage of the powerful stemmer of the analyzer but avoid the auto “0” padding in front of the digit?
Unfortunately, you can't change how the Microsoft tokenizers normalize numbers. To workaround this limitation you could choose a different analyzer for product attributes or add a character filter to your analyzer configuration that encodes the numeric characters so the tokenizer ignores them, for example, map each digit into a character from outside your expected character set using the MappingCharFilter.You can find examples here, use MicrosoftLanguageStemmingTokenizer as your tokenizer.
Related
I have created search index with ja.microsoft analyzer and it works fine as far as I am searching for characters in katkana. But when Hiragana character is used search does not work and no search results are returned.
Ex:
Searching names in Hiragana can not hit names in Katakana, and vice versa:
To hit "姥谷 キツ"(the name is in Katakana), searching "きつ" (in Hiragana) though. it fails to get it.
To hit "元廣 あえか" (phonetic chars in Katakana), searching "アエカ" though, it fails to get it.
Does Azure search support any analyzer which supports all writing schemes (Hiragana, Kankana, Kanji etc) or I need to use some other technique for this?
Neither the Lucene nor the Microsoft Japanese analyzers generate katakana tokens for hiragana or vice-versa. The analyzer will create tokens for the given syllabary.
You can use the analyze API to see what tokens get generated for a particular string.
For instance, calling the analyze API with this input:
{
"text": "元廣 あえか",
"analyzer": "ja.microsoft"
}
returns:
{
"#odata.context": "https://service-name.search.windows.net/$metadata#Microsoft.Azure.Search.V2019_05_06.AnalyzeResult",
"tokens": [
{
"token": "元廣",
"startOffset": 0,
"endOffset": 2,
"position": 0
},
{
"token": "あえか",
"startOffset": 3,
"endOffset": 6,
"position": 1
}
]
}
The Fuzzy matching feature of Ibm watson conversation since its latest update is matching words incorrectly. Eg. "what" is getting picked up as entity "chatbot" whereas there is no synonym in chatbot entity that is even close to what.
My question is that is there a way to exclude words from fuzzy matching yet keeping it ON for the entity. Or any other solution to tackle this problem.
Thanks
I assume you have an entity in chatbot for 'chat bot', and its getting a partial match on chat, and then doing fuzzy match from 'chat' to 'what' because its only one character difference and could be a spelling error.
You can turn fuzzy matching off, but you cannot currently blacklist any specific words. You can also try to protect yourself by your dialog design in that youre only looking for #chatbot at certain points, so it shouldn't interrupt very often
I know what you mean, we need to use fuzzy matching, but it sometimes creates more trouble. We have had a number of words picked up and reported as something different. The method we use to remove some of the issues, is to view the confidence value that's given for the incorrect spelling "what" .. and then using this as an additional condition.
i.e. if "what" reports a confidence value of 0.6 then set your condition to be 0.7 .. entities['chatbot']?.confidence > 0.7
Fuzzy logic can be switched on or off for each individual "class" of entities, i.e. 'chatbot' in the example above or 'city' in many of the doc examples.
I don't believe you can set a one global condition that checks all entities for there confidence value, so you do need to check the confidence at the class level. As shown above.
Also at present you cannot blacklist individual words to stop the fuzzy logic from checking them, like 'what' in your example.
Yes, you can definitely examine the confidence value. One concern I have about that is that you have no idea how many entities you are receiving, so you will have to write some fairly complex logic, but if you only have one entity, its pretty simple. When we detect entities, we return this:
"entities": [
{
"entity": "appliance",
"location": [
23,
29
],
"value": "wipers",
"confidence": 1
},
{
"entity": "appliance",
"location": [
11,
18
],
"value": "lights",
"confidence": 0.87
}
]
So to access the confidence of an entity you would do entity[0].confidence > 0.x in your dialog trigger.
We are using Solr to store documents with keywords; each keyword is associated with a span within the document.
The keywords were produced by some fancy analytics and/or manual work prior to loading them into Solr. A keyword can be repeated multiple times in a document. On the other hand, different instances of the same string in a single document can be connected with different keywords.
For example, this document
Bill studied The Bill of Rights last summer.
could be accompanied by the following keywords (with offsets in parentheses):
William Brown (0:4)
legal term (13:31)
summer 2011 (32:43)
(Obviously in other documents, Bill could refer to Bill Clinton or Bill Gates. Similarly, last summer will refer to different years in different documents. We do have all this information for all the documents.)
I know the document can have a field, say KEYWORD, which will store William Brown. Then when I search for William Brown I will get the above document. That part is easy.
But I have no idea how to store the info that William Brown corresponds to the text span 0:4 so I can highlight the first Bill, but not the second.
I thought I could use TermVectors, but I am not sure if/how I can store custom offsets. I would think this is a fairly common scenario ...
EDIT: edited to make clear that Bill can refer to different people/things in different documents.
EDIT2: edited to make clear that a document can contain homonyms (identical strings with different meanings).
Two Q Monte
Solution Pros:
Annotations logically stored with source docs
No knowledge of highlighter implementation or custom Java highlighter development required
Since all customization happens outside of Solr, this solution should be forward-compatible to future Solr versions.
Solution Cons:
Requires two queries to be run
Requires code in your search client to merge results from one query into the other.
With Solr 4.8+ you can nest child documents (annotations) underneath each primary document (text)...
curl http://localhost:8983/solr/update/json?softCommit=true -H 'Content-type:application/json' -d '
[
{
"id": "123",
"text" : "Bill studied The Bill of Rights last summer.",
"content_type": "source",
"_childDocuments_": [
{
"id": "123-1",
"content_type": "source_annotation",
"annotation": "William Brown",
"start_offset": 0,
"end_offset": 4
},
{
"id": "123-2",
"content_type": "source_annotation",
"annotation": "legal term",
"start_offset": 13,
"end_offset": 31
},
{
"id": "123-3",
"content_type": "source_annotation",
"annotation": "summer 2011",
"start_offset": 32,
"end_offset": 43
}
]
}
]
... using block join to query the annotations.
1) Annotation Query: http://localhost:8983/solr/query?fl=id,start_offset,end_offset&q={!child of=content_type:source}annotation:"William Brown"
"response":{"numFound":1,"start":0,
"docs":[
{
"id": "123-1",
"content_type": "source_annotation",
"annotation": "William Brown",
"start_offset": 0,
"end_offset": 4
}
]
}
Store these results in your code so that you can fold in the annotation offsets after the next query returns.
2) Source Query + Highlighting: http://localhost:8983/solr/query?hl=true&hl.fl=text&fq=content_type:source&q=text:"William Brown" OR id:123
(id:123 discovered in Annotation Query gets ORed into second query)
"response":{"numFound":1,"start":0,
"docs":[
{
"id": "123",
"content_type": "source",
"text": "Bill studied The Bill of Rights last summer."
}
],
"highlighting":{}
}
Note: In this example there is no highlighting information returned because the search terms didn't match any content_type:source documents. However we have the explicit annotations and offsets from the first query!
Your client code then needs to take the content_type:source_annotation results from the first query and manually insert highlighting markers into the content_type:source results from the second query.
More block join info on Yonik's blog here.
By default Solr stores the start/end position of each token once is tokenized, for instance using the StandardTokenizer. This info is encoded on the underline index. The use case that you described here sounds a lot like the SynonymFilterFactory.
When you define a synonym using the SynonymFilterFactory stating for instance that: foo => baz foo is equivalent to bar, the bar term is added to the token stream generated when the text is tokenized, and it will have the same offset information than the original token. So for instance if your text is: "foo is awesome", the term foo will have the following offset information (start=0,end=3) a new token bar(start=0,end=3) will be added to your index (assuming that you're using the SynonymFilterFactory at index time):
text: foo is awesome
start: 0 4 7
end: 3 6 13
Once the SynonymFilterFactory is applied:
bar
text: foo is awesome
start: 0 4 7
end: 3 6 13
So if you fire a query using foo, the document will match, but if you use bar as your query the document will also match since a bar token is added by the SynonymFilterFactory
In your particular case, you're trying to accomplish multi-term synonyms, which is kind of a difficult problem, you may need something more than the default synonym filter of Solr. Check this post from the guys at OpenSourceConnections and this other post from Lucidworks (the company behind Solr/Lucene). This two posts should provide additional information and the caveats of each approach.
Do you need to fetch the stored offsets for some later processing?
I am implementing Solr search, the search order is not displaying on the basis of score. Lets say if use the search keywords as .net ios it's returning the results based on score. I have a field title which holds the following data
KeySkills:Android, ios, Phonegap, ios
KeySkills:.net, .net, .net, MVC, HTML, CSS
Here when i search .net ios as search keyword net, .net, .net, MVC, HTML, CSS should come first in the results and the score should be higher because it contains .net 3 times, but i am getting reverse result.
Is there any setting needs to be done in solr config file or in schema.xml file to achieve this or how can i sort the results based on max no of occurrence of the the search string. please help me to solve this.
Following is the result i get
{
"responseHeader": {
"status": 0,
"QTime": 0,
"params": {
"indent": "true",
"q": ".net ios",
"_": "1434345788751",
"wt": "json"
}
},
"response": {
"numFound": 2,
"start": 0,
"docs": [
{
"KeySkills": "Android, ios, Phonegap, ios",
"_version_": 1504020323727573000,
"score": 0.47567564
},
{
"KeySkills": "net, net, net, MVC, HTML, CSS",
"_version_": 1504020323675144200,
"score": 0.4726259
}
]
}
}
As you can see in Lucene's doc, score is not only estimated with the number of matching term:
score(q,d) = coord(q,d) · queryNorm(q) · ∑( tf(t in d)·
idf(t)²·t.getBoost()·norm(t,d) )
Where
tf(t in d) correlates to the term's frequency, defined as the number
of times term t appears in the currently scored document d.
idf(t) stands for Inverse Document Frequency. This value correlates
to the inverse of docFreq (the number of documents in which the term t
appears). This means rarer terms give higher contribution to the total
score.
coord(q,d) is a score factor based on how many of the query terms
are found in the specified document.
t.getBoost() is a search time boost of term t in the query q as
specified in the query text.
norm(t,d) encapsulates a few
(indexing time) boost and length factors:
Field boost
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.
When a document is added to the index, all the above factors are
multiplied. If the document has multiple fields with the same name,
all their boosts are multiplied together:
norm(t,d) = lengthNorm · ∏ f.boost()
So, here I guess that "KeySkills": "Android, ios, Phonegap, ios" is before your other document because it contains less words than the other one.
To check that, you can use this awesome tool, which is explain.solr.pl.
I have a non trivial SOLR query, which already involves a filter query and facet calculations over multiple fields. One of the facet fields is a a multi value integer field, that is used to store categories. There are many possible categories and new ones are created dynamically, so using multiple fields is not an option.
What I want to do, is to restrict facet calculation over this field to a certain set of integers (= categories). So for example I want to calculate facets of this field, but only taking categories 3,7,9 and 15 into account. All other values in that field should be ignored.
How do I do that? Is there some build in functionality which can be used to solve this? Or do I have to write a custom search component?
The parameter can be defined for each field specified by the facet.field parameter – you can do it, by adding a parameter like this: facet.field_name.prefix.
I don't know about any way to define the facet base that should be different from the result, but one can use the facet.query to explicitly define each facet filter, e.g.:
facet.query={!key=3}category:3&facet.query={!key=7}category:7&facet.query={!key=9}category:9&facet.query={!key=15}category:15
Given the solr schema/data from this gist, the results will have something like this:
"facet_counts": {
"facet_queries": {
"3": 1,
"7": 1,
"9": 0,
"15": 0
},
"facet_fields": {
"category": [
"2",
2,
"1",
1,
"3",
1,
"7",
1,
"8",
1
]
},
"facet_dates": {},
"facet_ranges": {}
}
Thus giving the needed facet result.
I have some doubts about performance here(especially when there will be more than 4 categories and if the initial query is returning a lot of results), so it is better to do some benchmarking, before using this in production.
Not exactly the answer to my own question, but the solution we are using now: The numbers I want to filter on, build distinct groups. So we can prefix the id with a group id like this:
1.3
1.8
1.9
2.4
2.5
2.11
...
Having the data like this in SOLR, we can use facted prefixes to facet only over a single group: http://wiki.apache.org/solr/SimpleFacetParameters#facet.prefix