as there is no named entity recognition built into elasticsearch (which
detects and extracts museum as a location for tagging and new york as a
city), you have to options:
Execute this named entity extraction in your application (quite some work)
Use the all field for this kind of queries. See all field contains the
concatenated content of all your other fields, and will therefore match, if
you search for 'museum in new york'. For more information about the all
field, see Elasticsearch Platform — Find real-time answers at scale | Elastic
If both doesnt match your requirements, feel free add some more infos and
I'll try to think harder.
Thanks. I did think about using NLP as a pre processor.
Would I be using boost to improve the score if the "museum" is a match in
the list of tags and/or "New York" in the city field, instead of some text
that has both words in the article description. I'm trying to improve the
scores of articles that have a direct match for either the tags or
location.
On Thursday, September 19, 2013 3:22:57 PM UTC+8, Alexander Reelsen wrote:
Hey,
as there is no named entity recognition built into elasticsearch (which
detects and extracts museum as a location for tagging and new york as a
city), you have to options:
Execute this named entity extraction in your application (quite some
work)
Use the all field for this kind of queries. See all field contains the
concatenated content of all your other fields, and will therefore match, if
you search for 'museum in new york'. For more information about the all
field, see Elasticsearch Platform — Find real-time answers at scale | Elastic
If both doesnt match your requirements, feel free add some more infos and
I'll try to think harder.
--Alex
On Thu, Sep 19, 2013 at 5:51 AM, Nora Olsen <nora.o...@gmail.com<javascript:>
wrote:
Hi,
I'm very new to Elastic Search or search indexes in general.
We are using haystack and have the search index mapped to our model with
text (title + description)
country name
city name
array of tags
I can see that in the index, _source is properly populated.
With this, I can perform a search on the text easily that is provided for
by ES.
But what techniques can I use this perform searches such as this
"concerts in UK"
"hiking in brazil"
or even with tags
"museums in new york" , where museums=tag and new york=city
How do people power their search bar to perform more complex searches?
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