Re: MoreLikeThis can't identify that 2 documents with exactly same attachments are duplicates
Thank you Alex. At the moment it works fine even with large documents, but I'll test if I can reach similar results with interesting terms. Best, Zoran On Thursday, 8 May 2014 02:02:24 UTC-7, Alex Ksikes wrote: > > On May 8, 2014 8:09 AM, "Zoran Jeremic" > > wrote: > > > > Hi Alex, > > > > Thank you for this explanation. This really helped me to understand how > it works, and now I managed to get results I was expecting just after > setting max_query_terms value to be 0 or some very high value. With these > results in my tests I was able to identify duplicates. I noticed couple of > things though. > > > > - I got much better results with web pages when I indexed attachment as > html source and use text extracted by Jsoup in query, then when I indexed > text extracted from web page as attachment and used text in query. I > suppose that difference is related to the fact that Jsoup did not extract > text in the same way as Tika parser used by ES did. > > - There was significant improvement in the results in the second test > when I have indexed 50 web pages, then in first test when I indexed 10 web > pages. I deleted index before each test. I suppose that this is related to > the tf*idf. > > If so, does it make sense to provide some training set for elasticsearch > that will be used to populate index before system is started to be used? > > Perhaps you are asking for a background dataset to bias the selection of > interesting terms. This could make sense depending on your application. > > > Could you please define "relevant" in your setting? In a corpus of very > similar documents, is your goal to find the ones which are oddly different? > Have you looked into ES significant terms? > > I have the service that recommends documents to the students based on > their current learning context. It creates tokenized string from titles, > descriptions and keywords of the course lessons student is working at the > moment. I'm using this string as input to the mlt_like_text to find some > interesting resources that could help them. > > I want to avoid having duplicates (or very similar documents) among top > documents that are recommended. > > My idea was that during the documents uploading (before I index it with > elasticsearch) I find if there already exists it's duplicate, and store > this information as ES document field. Later, in query I can specify that > duplicates are not recommended. > > > > Here you should probably strip the html tags, and solely index the text > in its own field. > > As I already mentioned this didn't give me good results for some reason. > > > > Do you think this approach would work fine with large textual documents, > e.g. pdf documents having couple of hundred of pages? My main concern is > related to performances of these queries using like_text, so that's why I > was trying to avoid this approach and use mlt with document id as input. > > I don't think this approach would work well in this case, but you should > try. I think what you are after is to either extract good features for your > PDF documents and search on that, or finger printing. This could be > achieved by playing with analyzers. > > > Thanks, > > Zoran > > > > > > > > On Wednesday, 7 May 2014 06:14:56 UTC-7, Alex Ksikes wrote: > >> > >> Hi Zoran, > >> > >> In a nutshell 'more like this' creates a large boolean disjunctive > query of 'max_query_terms' number of interesting terms from a text > specified in 'like_text'. The interesting terms are picked up with respect > to the their tf-idf scores in the whole corpus. These later parameters > could be tuned with 'min_term_freq', 'min_doc_freq', and 'min_doc_freq' > parameters. The number of boolean clauses that must match is controlled by > 'percent_terms_to_match'. In the case of specifying only one field in > 'fields', the analyzer used to pick up the terms in 'like_text' is the one > associated with the field, unless specified specified by 'analyzer'. So as > an example, the default is to create a boolean query of 25 interesting > terms where only 30% of the should clauses must match. > >> > >> On Wednesday, May 7, 2014 5:14:11 AM UTC+2, Zoran Jeremic wrote: > >>> > >>> Hi Alex, > >>> > >>> > >>> If you are looking for exact duplicates then hashing the file content, > and doing a search for that hash would do the job. > >>> This trick won't work for me as these are not exact duplicates. For > example, I have 10 students working on the same 100 pages long word > document. Each of these students could change only one sentence and upload > a document. The hash will be different, but it's 99,99 % same documents. > >>> I have the other service that uses mlt_like_text to recommend some > relevant documents, and my problem is if this document has best score, then > all duplicates will be among top hits and instead recommending users with > several most relevant documents I will recommend 10 instances of same > document.
Re: MoreLikeThis can't identify that 2 documents with exactly same attachments are duplicates
On May 8, 2014 8:09 AM, "Zoran Jeremic" wrote: > > Hi Alex, > > Thank you for this explanation. This really helped me to understand how it works, and now I managed to get results I was expecting just after setting max_query_terms value to be 0 or some very high value. With these results in my tests I was able to identify duplicates. I noticed couple of things though. > > - I got much better results with web pages when I indexed attachment as html source and use text extracted by Jsoup in query, then when I indexed text extracted from web page as attachment and used text in query. I suppose that difference is related to the fact that Jsoup did not extract text in the same way as Tika parser used by ES did. > - There was significant improvement in the results in the second test when I have indexed 50 web pages, then in first test when I indexed 10 web pages. I deleted index before each test. I suppose that this is related to the tf*idf. > If so, does it make sense to provide some training set for elasticsearch that will be used to populate index before system is started to be used? Perhaps you are asking for a background dataset to bias the selection of interesting terms. This could make sense depending on your application. > Could you please define "relevant" in your setting? In a corpus of very similar documents, is your goal to find the ones which are oddly different? Have you looked into ES significant terms? > I have the service that recommends documents to the students based on their current learning context. It creates tokenized string from titles, descriptions and keywords of the course lessons student is working at the moment. I'm using this string as input to the mlt_like_text to find some interesting resources that could help them. > I want to avoid having duplicates (or very similar documents) among top documents that are recommended. > My idea was that during the documents uploading (before I index it with elasticsearch) I find if there already exists it's duplicate, and store this information as ES document field. Later, in query I can specify that duplicates are not recommended. > > Here you should probably strip the html tags, and solely index the text in its own field. > As I already mentioned this didn't give me good results for some reason. > > Do you think this approach would work fine with large textual documents, e.g. pdf documents having couple of hundred of pages? My main concern is related to performances of these queries using like_text, so that's why I was trying to avoid this approach and use mlt with document id as input. I don't think this approach would work well in this case, but you should try. I think what you are after is to either extract good features for your PDF documents and search on that, or finger printing. This could be achieved by playing with analyzers. > Thanks, > Zoran > > > > On Wednesday, 7 May 2014 06:14:56 UTC-7, Alex Ksikes wrote: >> >> Hi Zoran, >> >> In a nutshell 'more like this' creates a large boolean disjunctive query of 'max_query_terms' number of interesting terms from a text specified in 'like_text'. The interesting terms are picked up with respect to the their tf-idf scores in the whole corpus. These later parameters could be tuned with 'min_term_freq', 'min_doc_freq', and 'min_doc_freq' parameters. The number of boolean clauses that must match is controlled by 'percent_terms_to_match'. In the case of specifying only one field in 'fields', the analyzer used to pick up the terms in 'like_text' is the one associated with the field, unless specified specified by 'analyzer'. So as an example, the default is to create a boolean query of 25 interesting terms where only 30% of the should clauses must match. >> >> On Wednesday, May 7, 2014 5:14:11 AM UTC+2, Zoran Jeremic wrote: >>> >>> Hi Alex, >>> >>> >>> If you are looking for exact duplicates then hashing the file content, and doing a search for that hash would do the job. >>> This trick won't work for me as these are not exact duplicates. For example, I have 10 students working on the same 100 pages long word document. Each of these students could change only one sentence and upload a document. The hash will be different, but it's 99,99 % same documents. >>> I have the other service that uses mlt_like_text to recommend some relevant documents, and my problem is if this document has best score, then all duplicates will be among top hits and instead recommending users with several most relevant documents I will recommend 10 instances of same document. >> >> >> Could you please define "relevant" in your setting? In a corpus of very similar documents, is your goal to find the ones which are oddly different? Have you looked into ES significant terms? >> >>> >>> If you are looking for near duplicates, then I would recommend extracting whatever text you have in your html, pdf, doc, indexing that and running more like this with like_text set to that content. >>> I tried that as well, and results are ver
Re: MoreLikeThis can't identify that 2 documents with exactly same attachments are duplicates
Hi Alex, Thank you for this explanation. This really helped me to understand how it works, and now I managed to get results I was expecting just after setting max_query_terms value to be 0 or some very high value. With these results in my tests I was able to identify duplicates. I noticed couple of things though. - I got much better results with web pages when I indexed attachment as html source and use text extracted by Jsoup in query, then when I indexed text extracted from web page as attachment and used text in query. I suppose that difference is related to the fact that Jsoup did not extract text in the same way as Tika parser used by ES did. - There was significant improvement in the results in the second test when I have indexed 50 web pages, then in first test when I indexed 10 web pages. I deleted index before each test. I suppose that this is related to the tf*idf. If so, does it make sense to provide some training set for elasticsearch that will be used to populate index before system is started to be used? Could you please define "relevant" in your setting? In a corpus of very similar documents, is your goal to find the ones which are oddly different? Have you looked into ES significant terms? I have the service that recommends documents to the students based on their current learning context. It creates tokenized string from titles, descriptions and keywords of the course lessons student is working at the moment. I'm using this string as input to the mlt_like_text to find some interesting resources that could help them. I want to avoid having duplicates (or very similar documents) among top documents that are recommended. My idea was that during the documents uploading (before I index it with elasticsearch) I find if there already exists it's duplicate, and store this information as ES document field. Later, in query I can specify that duplicates are not recommended. Here you should probably strip the html tags, and solely index the text in its own field. As I already mentioned this didn't give me good results for some reason. Do you think this approach would work fine with large textual documents, e.g. pdf documents having couple of hundred of pages? My main concern is related to performances of these queries using like_text, so that's why I was trying to avoid this approach and use mlt with document id as input. Thanks, Zoran On Wednesday, 7 May 2014 06:14:56 UTC-7, Alex Ksikes wrote: > > Hi Zoran, > > In a nutshell 'more like this' creates a large boolean disjunctive query > of 'max_query_terms' number of interesting terms from a text specified in > 'like_text'. The interesting terms are picked up with respect to the their > tf-idf scores in the whole corpus. These later parameters could be tuned > with 'min_term_freq', 'min_doc_freq', and 'min_doc_freq' parameters. The > number of boolean clauses that must match is controlled by > 'percent_terms_to_match'. In the case of specifying only one field in > 'fields', the analyzer used to pick up the terms in 'like_text' is the one > associated with the field, unless specified specified by 'analyzer'. So as > an example, the default is to create a boolean query of 25 interesting > terms where only 30% of the should clauses must match. > > On Wednesday, May 7, 2014 5:14:11 AM UTC+2, Zoran Jeremic wrote: >> >> Hi Alex, >> >> >> If you are looking for exact duplicates then hashing the file content, >> and doing a search for that hash would do the job. >> This trick won't work for me as these are not exact duplicates. For >> example, I have 10 students working on the same 100 pages long word >> document. Each of these students could change only one sentence and upload >> a document. The hash will be different, but it's 99,99 % same documents. >> I have the other service that uses mlt_like_text to recommend some >> relevant documents, and my problem is if this document has best score, then >> all duplicates will be among top hits and instead recommending users with >> several most relevant documents I will recommend 10 instances of same >> document. >> > > Could you please define "relevant" in your setting? In a corpus of very > similar documents, is your goal to find the ones which are oddly different? > Have you looked into ES significant terms? > > >> If you are looking for near duplicates, then I would recommend extracting >> whatever text you have in your html, pdf, doc, indexing that and running >> more like this with like_text set to that content. >> I tried that as well, and results are very disappointing, though I'm not >> sure if that would be good idea having in mind that long textual documents >> could be used. For testing purpose, I made a simple test with 10 web pages. >> Maybe I'm making some mistake there. What I did is to index 10 web pages >> and store it in document as attachment. Content is stored as byte[]. Then >> I'm using the same 10 pages, extract content
Re: MoreLikeThis can't identify that 2 documents with exactly same attachments are duplicates
Hi Zoran,
In a nutshell 'more like this' creates a large boolean disjunctive query of
'max_query_terms' number of interesting terms from a text specified in
'like_text'. The interesting terms are picked up with respect to the their
tf-idf scores in the whole corpus. These later parameters could be tuned
with 'min_term_freq', 'min_doc_freq', and 'min_doc_freq' parameters. The
number of boolean clauses that must match is controlled by
'percent_terms_to_match'. In the case of specifying only one field in
'fields', the analyzer used to pick up the terms in 'like_text' is the one
associated with the field, unless specified specified by 'analyzer'. So as
an example, the default is to create a boolean query of 25 interesting
terms where only 30% of the should clauses must match.
On Wednesday, May 7, 2014 5:14:11 AM UTC+2, Zoran Jeremic wrote:
>
> Hi Alex,
>
>
> If you are looking for exact duplicates then hashing the file content, and
> doing a search for that hash would do the job.
> This trick won't work for me as these are not exact duplicates. For
> example, I have 10 students working on the same 100 pages long word
> document. Each of these students could change only one sentence and upload
> a document. The hash will be different, but it's 99,99 % same documents.
> I have the other service that uses mlt_like_text to recommend some
> relevant documents, and my problem is if this document has best score, then
> all duplicates will be among top hits and instead recommending users with
> several most relevant documents I will recommend 10 instances of same
> document.
>
Could you please define "relevant" in your setting? In a corpus of very
similar documents, is your goal to find the ones which are oddly different?
Have you looked into ES significant terms?
> If you are looking for near duplicates, then I would recommend extracting
> whatever text you have in your html, pdf, doc, indexing that and running
> more like this with like_text set to that content.
> I tried that as well, and results are very disappointing, though I'm not
> sure if that would be good idea having in mind that long textual documents
> could be used. For testing purpose, I made a simple test with 10 web pages.
> Maybe I'm making some mistake there. What I did is to index 10 web pages
> and store it in document as attachment. Content is stored as byte[]. Then
> I'm using the same 10 pages, extract content using Jsoup, and try to find
> similar web pages. Here is the code that I used to find similar web pages
> to the provided one:
> System.out.println("Duplicates for link:"+link);
> System.out.println(
> "");
> String indexName=ESIndexNames.INDEX_DOCUMENTS;
> String indexType=ESIndexTypes.DOCUMENT;
> String mapping = copyToStringFromClasspath(
> "/org/prosolo/services/indexing/document-mapping.json");
> client.admin().indices().putMapping(putMappingRequest(
> indexName).type(indexType).source(mapping)).actionGet();
> URL url = new URL(link);
> org.jsoup.nodes.Document doc=Jsoup.connect(link).get();
> String html=doc.html(); //doc.text();
> QueryBuilder qb = null;
> // create the query
> qb = QueryBuilders.moreLikeThisQuery("file")
> .likeText(html).minTermFreq(0).minDocFreq(0);
> SearchResponse sr = client.prepareSearch(ESIndexNames.
> INDEX_DOCUMENTS)
> .setQuery(qb).addFields("url", "title", "contentType"
> )
> .setFrom(0).setSize(5).execute().actionGet();
> if (sr != null) {
> SearchHits searchHits = sr.getHits();
> Iterator hitsIter = searchHits.iterator();
> while (hitsIter.hasNext()) {
> SearchHit searchHit = hitsIter.next();
> System.out.println("Duplicate:" + searchHit.getId()
> + " title:"+searchHit.getFields().get("url").
> getValue()+" score:" + searchHit.getScore());
> }
> }
>
> And results of the execution of this for each of 10 urls is:
>
> Duplicates for link:http://en.wikipedia.org/wiki/Mathematical_logic
>
> Duplicate:Crwk_36bTUCEso1ambs0bA URL:http://
> en.wikipedia.org/wiki/Mathematical_logic score:0.3335998
> Duplicate:--3l-WRuQL2osXg71ixw7A URL:http://
> en.wikipedia.org/wiki/Chemistry score:0.16319205
> Duplicate:8dDa6HsBS12HrI0XgFVLvA URL:http://
> en.wikipedia.org/wiki/Formal_science score:0.13035104
> Duplicate:1APeDW0KQnWRv_8mihrz4A
> URL:http://en.wikipedia.org/wiki/Starscore:0.12292466
> Duplicate:2NElV2ULQxqcbFhd2pVy0w URL:http://
> en.wikipedia.org/wiki/Crystallography score:0.117023855
>
> Duplicates for link:http://en.wikipedia.org/wiki/Mathematical_statistics
> **
Re: MoreLikeThis can't identify that 2 documents with exactly same attachments are duplicates
Hi Zoran,
If you are looking for exact duplicates then hashing the file content, and
doing a search for that hash would do the job. If you are looking for near
duplicates, then I would recommend extracting whatever text you have in
your html, pdf, doc, indexing that and running more like this with
like_text set to that content. Additionally you can perform a mlt search on
more fields including the meta-data fields extracted with the attachment
plugin. Hope this helps.
Alex
On Monday, May 5, 2014 8:08:30 PM UTC+2, Zoran Jeremic wrote:
>
> Hi Alex,
>
> Thank you for your explanation. It makes sense now. However, I'm not sure
> I understood your proposal.
>
> So I would adjust the mlt_fields accordingly, and possibly extract the
> relevant portions of texts manually
> What do you mean by adjusting mlt_fields? The only shared field that is
> guaranteed to be same is file. Different users could add different titles
> to documents, but attach same or almost the same documents. If I compare
> documents based on the other fields, it doesn't mean that it will match,
> even though attached files are exactly the same.
> I'm also not sure what did you mean by extract the relevant portions of
> text manually. How would I do that and what to do with it?
>
> Thanks,
> Zoran
>
>
> On Monday, 5 May 2014 01:23:49 UTC-7, Alex Ksikes wrote:
>>
>> Hi Zoran,
>>
>> Using the attachment type, you can text search over the attached document
>> meta-data, but not its actual content, as it is base 64 encoded. So I would
>> adjust the mlt_fields accordingly, and possibly extract the relevant
>> portions of texts manually. Also set percent_terms_to_match = 0, to ensure
>> that all boolean clauses match. Let me know how this works out for you.
>>
>> Cheers,
>>
>> Alex
>>
>> On Monday, May 5, 2014 5:50:07 AM UTC+2, Zoran Jeremic wrote:
>>>
>>> Hi guys,
>>>
>>> I have a document that stores a content of html file, pdf, doc or other
>>> textual document in one of it's fields as byte array using attachment
>>> plugin. Mapping is as follows:
>>>
>>> { "document":{
>>> "properties":{
>>> "title":{"type":"string","store":true },
>>> "description":{"type":"string","store":"yes"},
>>> "contentType":{"type":"string","store":"yes"},
>>> "url":{"store":"yes", "type":"string"},
>>> "visibility": { "store":"yes", "type":"string"},
>>> "ownerId": {"type": "long", "store":"yes" },
>>> "relatedToType": { "type": "string", "store":"yes" },
>>> "relatedToId": {"type": "long", "store":"yes" },
>>> "file":{
>>> "path": "full","type":"attachment",
>>> "fields":{
>>> "author": { "type": "string" },
>>> "title": { "store": true,"type": "string" },
>>> "keywords": { "type": "string" },
>>> "file": { "store": true, "term_vector":
>>> "with_positions_offsets","type": "string" },
>>> "name": { "type": "string" },
>>> "content_length": { "type": "integer" },
>>> "date": { "format": "dateOptionalTime", "type":
>>> "date" },
>>> "content_type": { "type": "string" }
>>> }
>>> }}
>>> And the code I'm using to store the document is:
>>>
>>> VisibilityType.PUBLIC
>>>
>>> These files seems to be stored fine and I can search content. However, I
>>> need to identify if there are duplicates of web pages or files stored in
>>> ES, so I don't return the same documents to the user as search or
>>> recommendation result. My expectation was that I could use MoreLikeThis
>>> after the document was indexed to identify if there are duplicates of that
>>> document and accordingly to mark it as duplicate. However, results look
>>> weird for me, or I don't understand very well how MoreLikeThis works.
>>>
>>> For example, I indexed web page http://en.wikipedia.org/wiki/Linguistics3
>>> times, and all 3 documents in ES have exactly the same binary content
>>> under file. Then for the following query:
>>>
>>> http://localhost:9200/documents/document/WpkcK-ZjSMi_l6iRq0Vuhg/_mlt?mlt_fields=file&min_doc_freq=1
>>> where ID is id of one of these documents I got these results:
>>> http://en.wikipedia.org/wiki/Linguistics with score 0.6633003
>>> http://en.wikipedia.org/wiki/Linguistics with score 0.6197818
>>> http://en.wikipedia.org/wiki/Computational_linguistics with score
>>> 0.48509508
>>> ...
>>>
>>> For some other examples, scores for the same documents are much lower,
>>> and sometimes (though not that often) I don't get duplicates on the first
>>> positions. I would expect here to have score 1.0 or higher for documents
>>> that are exactly the same, but it's not the case, and I can't figure out
>>> how could I identify if there are duplicates in the Elasticsearch index.
>>>
>>> I
Re: MoreLikeThis can't identify that 2 documents with exactly same attachments are duplicates
Hi Alex,
Thank you for your explanation. It makes sense now. However, I'm not sure I
understood your proposal.
So I would adjust the mlt_fields accordingly, and possibly extract the
relevant portions of texts manually
What do you mean by adjusting mlt_fields? The only shared field that is
guaranteed to be same is file. Different users could add different titles
to documents, but attach same or almost the same documents. If I compare
documents based on the other fields, it doesn't mean that it will match,
even though attached files are exactly the same.
I'm also not sure what did you mean by extract the relevant portions of
text manually. How would I do that and what to do with it?
Thanks,
Zoran
On Monday, 5 May 2014 01:23:49 UTC-7, Alex Ksikes wrote:
>
> Hi Zoran,
>
> Using the attachment type, you can text search over the attached document
> meta-data, but not its actual content, as it is base 64 encoded. So I would
> adjust the mlt_fields accordingly, and possibly extract the relevant
> portions of texts manually. Also set percent_terms_to_match = 0, to ensure
> that all boolean clauses match. Let me know how this works out for you.
>
> Cheers,
>
> Alex
>
> On Monday, May 5, 2014 5:50:07 AM UTC+2, Zoran Jeremic wrote:
>>
>> Hi guys,
>>
>> I have a document that stores a content of html file, pdf, doc or other
>> textual document in one of it's fields as byte array using attachment
>> plugin. Mapping is as follows:
>>
>> { "document":{
>> "properties":{
>> "title":{"type":"string","store":true },
>> "description":{"type":"string","store":"yes"},
>> "contentType":{"type":"string","store":"yes"},
>> "url":{"store":"yes", "type":"string"},
>> "visibility": { "store":"yes", "type":"string"},
>> "ownerId": {"type": "long", "store":"yes" },
>> "relatedToType": { "type": "string", "store":"yes" },
>> "relatedToId": {"type": "long", "store":"yes" },
>> "file":{
>> "path": "full","type":"attachment",
>> "fields":{
>> "author": { "type": "string" },
>> "title": { "store": true,"type": "string" },
>> "keywords": { "type": "string" },
>> "file": { "store": true, "term_vector":
>> "with_positions_offsets","type": "string" },
>> "name": { "type": "string" },
>> "content_length": { "type": "integer" },
>> "date": { "format": "dateOptionalTime", "type":
>> "date" },
>> "content_type": { "type": "string" }
>> }
>> }}
>> And the code I'm using to store the document is:
>>
>> VisibilityType.PUBLIC
>>
>> These files seems to be stored fine and I can search content. However, I
>> need to identify if there are duplicates of web pages or files stored in
>> ES, so I don't return the same documents to the user as search or
>> recommendation result. My expectation was that I could use MoreLikeThis
>> after the document was indexed to identify if there are duplicates of that
>> document and accordingly to mark it as duplicate. However, results look
>> weird for me, or I don't understand very well how MoreLikeThis works.
>>
>> For example, I indexed web page http://en.wikipedia.org/wiki/Linguistics3
>> times, and all 3 documents in ES have exactly the same binary content
>> under file. Then for the following query:
>>
>> http://localhost:9200/documents/document/WpkcK-ZjSMi_l6iRq0Vuhg/_mlt?mlt_fields=file&min_doc_freq=1
>> where ID is id of one of these documents I got these results:
>> http://en.wikipedia.org/wiki/Linguistics with score 0.6633003
>> http://en.wikipedia.org/wiki/Linguistics with score 0.6197818
>> http://en.wikipedia.org/wiki/Computational_linguistics with score
>> 0.48509508
>> ...
>>
>> For some other examples, scores for the same documents are much lower,
>> and sometimes (though not that often) I don't get duplicates on the first
>> positions. I would expect here to have score 1.0 or higher for documents
>> that are exactly the same, but it's not the case, and I can't figure out
>> how could I identify if there are duplicates in the Elasticsearch index.
>>
>> I would appreciate if somebody could explain if this is expected
>> behaviour or I didn't use it properly.
>>
>> Thanks,
>> Zoran
>>
>>
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Re: MoreLikeThis can't identify that 2 documents with exactly same attachments are duplicates
Hi Zoran,
Using the attachment type, you can text search over the attached document
meta-data, but not its actual content, as it is base 64 encoded. So I would
adjust the mlt_fields accordingly, and possibly extract the relevant
portions of texts manually. Also set percent_terms_to_match = 0, to ensure
that all boolean clauses match. Let me know how this works out for you.
Cheers,
Alex
On Monday, May 5, 2014 5:50:07 AM UTC+2, Zoran Jeremic wrote:
>
> Hi guys,
>
> I have a document that stores a content of html file, pdf, doc or other
> textual document in one of it's fields as byte array using attachment
> plugin. Mapping is as follows:
>
> { "document":{
> "properties":{
> "title":{"type":"string","store":true },
> "description":{"type":"string","store":"yes"},
> "contentType":{"type":"string","store":"yes"},
> "url":{"store":"yes", "type":"string"},
> "visibility": { "store":"yes", "type":"string"},
> "ownerId": {"type": "long", "store":"yes" },
> "relatedToType": { "type": "string", "store":"yes" },
> "relatedToId": {"type": "long", "store":"yes" },
> "file":{
> "path": "full","type":"attachment",
> "fields":{
> "author": { "type": "string" },
> "title": { "store": true,"type": "string" },
> "keywords": { "type": "string" },
> "file": { "store": true, "term_vector":
> "with_positions_offsets","type": "string" },
> "name": { "type": "string" },
> "content_length": { "type": "integer" },
> "date": { "format": "dateOptionalTime", "type":
> "date" },
> "content_type": { "type": "string" }
> }
> }}
> And the code I'm using to store the document is:
>
> VisibilityType.PUBLIC
>
> These files seems to be stored fine and I can search content. However, I
> need to identify if there are duplicates of web pages or files stored in
> ES, so I don't return the same documents to the user as search or
> recommendation result. My expectation was that I could use MoreLikeThis
> after the document was indexed to identify if there are duplicates of that
> document and accordingly to mark it as duplicate. However, results look
> weird for me, or I don't understand very well how MoreLikeThis works.
>
> For example, I indexed web page http://en.wikipedia.org/wiki/Linguistics3
> times, and all 3 documents in ES have exactly the same binary content
> under file. Then for the following query:
>
> http://localhost:9200/documents/document/WpkcK-ZjSMi_l6iRq0Vuhg/_mlt?mlt_fields=file&min_doc_freq=1
> where ID is id of one of these documents I got these results:
> http://en.wikipedia.org/wiki/Linguistics with score 0.6633003
> http://en.wikipedia.org/wiki/Linguistics with score 0.6197818
> http://en.wikipedia.org/wiki/Computational_linguistics with score
> 0.48509508
> ...
>
> For some other examples, scores for the same documents are much lower, and
> sometimes (though not that often) I don't get duplicates on the first
> positions. I would expect here to have score 1.0 or higher for documents
> that are exactly the same, but it's not the case, and I can't figure out
> how could I identify if there are duplicates in the Elasticsearch index.
>
> I would appreciate if somebody could explain if this is expected behaviour
> or I didn't use it properly.
>
> Thanks,
> Zoran
>
>
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MoreLikeThis can't identify that 2 documents with exactly same attachments are duplicates
Hi guys,
I have a document that stores a content of html file, pdf, doc or other
textual document in one of it's fields as byte array using attachment
plugin. Mapping is as follows:
{ "document":{
"properties":{
"title":{"type":"string","store":true },
"description":{"type":"string","store":"yes"},
"contentType":{"type":"string","store":"yes"},
"url":{"store":"yes", "type":"string"},
"visibility": { "store":"yes", "type":"string"},
"ownerId": {"type": "long", "store":"yes" },
"relatedToType": { "type": "string", "store":"yes" },
"relatedToId": {"type": "long", "store":"yes" },
"file":{
"path": "full","type":"attachment",
"fields":{
"author": { "type": "string" },
"title": { "store": true,"type": "string" },
"keywords": { "type": "string" },
"file": { "store": true, "term_vector":
"with_positions_offsets","type": "string" },
"name": { "type": "string" },
"content_length": { "type": "integer" },
"date": { "format": "dateOptionalTime", "type":
"date" },
"content_type": { "type": "string" }
}
}}
And the code I'm using to store the document is:
VisibilityType.PUBLIC
These files seems to be stored fine and I can search content. However, I
need to identify if there are duplicates of web pages or files stored in
ES, so I don't return the same documents to the user as search or
recommendation result. My expectation was that I could use MoreLikeThis
after the document was indexed to identify if there are duplicates of that
document and accordingly to mark it as duplicate. However, results look
weird for me, or I don't understand very well how MoreLikeThis works.
For example, I indexed web page http://en.wikipedia.org/wiki/Linguistics 3
times, and all 3 documents in ES have exactly the same binary content under
file. Then for the following query:
http://localhost:9200/documents/document/WpkcK-ZjSMi_l6iRq0Vuhg/_mlt?mlt_fields=file&min_doc_freq=1
where ID is id of one of these documents I got these results:
http://en.wikipedia.org/wiki/Linguistics with score 0.6633003
http://en.wikipedia.org/wiki/Linguistics with score 0.6197818
http://en.wikipedia.org/wiki/Computational_linguistics with score 0.48509508
...
For some other examples, scores for the same documents are much lower, and
sometimes (though not that often) I don't get duplicates on the first
positions. I would expect here to have score 1.0 or higher for documents
that are exactly the same, but it's not the case, and I can't figure out
how could I identify if there are duplicates in the Elasticsearch index.
I would appreciate if somebody could explain if this is expected behaviour
or I didn't use it properly.
Thanks,
Zoran
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