NGram question

Elastic Stack Elasticsearch
1

Hello there,

I index a document containing the sentence "please search me".

I would like to retrieve this document by searching :

  • please

  • PlEasE

  • pleas

  • ase sear

  • earch me

etc etc....

Some people told me that NGram was great and faster than prefixed
queries.

But it does not work for me.

Could you please give me the analyzer configuration and the mapping
for this use case ?

Thanks a lot.

2

Please gist (https://gist.github.com/) your example and we'll try to find
out what you've missed.

Thanks,

Paul.

On Wed, Jun 29, 2011 at 10:35 AM, alheim alexheimburger@gmail.com wrote:

Hello there,

I index a document containing the sentence "please search me".

I would like to retrieve this document by searching :

  • please

  • PlEasE

  • pleas

  • ase sear

  • earch me

etc etc....

Some people told me that NGram was great and faster than prefixed
queries.

But it does not work for me.

Could you please give me the analyzer configuration and the mapping
for this use case ?

Thanks a lot.

--

Paul Loy
paul@keteracel.com
http://uk.linkedin.com/in/paulloy

3

OK. I got help on IRC and things are clearer now.

For your information, here is a GIST with a working example of NGram
implementation. This can be used for an autocomplete box.

On Wed, Jun 29, 2011 at 11:38 AM, Paul Loy keteracel@gmail.com wrote:

Please gist (https://gist.github.com/) your example and we'll try to find
out what you've missed.

Thanks,

Paul.

On Wed, Jun 29, 2011 at 10:35 AM, alheim alexheimburger@gmail.com wrote:

Hello there,

I index a document containing the sentence "please search me".

I would like to retrieve this document by searching :

  • please

  • PlEasE

  • pleas

  • ase sear

  • earch me

etc etc....

Some people told me that NGram was great and faster than prefixed
queries.

But it does not work for me.

Could you please give me the analyzer configuration and the mapping
for this use case ?

Thanks a lot.

--

Paul Loy
paul@keteracel.com
http://uk.linkedin.com/in/paulloy

--
Alexandre Heimburger
R&D Manager
blueKiwi Software
tel : +33687880997
email : ahb@bluekiwi-software.com
adress : 93 rue Vieille du Temple, 75003 Paris

What is blueKiwi? blueKiwi - the first Enterprise Social Software Suite in
the world building professional networks on conversations and relationships

  • helps large organizations increase their productivity, foster innovations
    and boost people satisfaction.
4

Just a note on the name of the custom analyzers you created, calling them keyword might be confusing down the road, since they don't use the keyword tokenizer, but the standard one.

On Wednesday, June 29, 2011 at 1:36 PM, Alexandre Heimburger wrote:

OK. I got help on IRC and things are clearer now.

For your information, here is a GIST with a working example of NGram implementation. This can be used for an autocomplete box.

Ngram working sample · GitHub

On Wed, Jun 29, 2011 at 11:38 AM, Paul Loy <keteracel@gmail.com (mailto:keteracel@gmail.com)> wrote:

Please gist (https://gist.github.com/) your example and we'll try to find out what you've missed.

Thanks,

Paul.

On Wed, Jun 29, 2011 at 10:35 AM, alheim <alexheimburger@gmail.com (mailto:alexheimburger@gmail.com)> wrote:

Hello there,

I index a document containing the sentence "please search me".

I would like to retrieve this document by searching :

  • please

  • PlEasE

  • pleas

  • ase sear

  • earch me

etc etc....

Some people told me that NGram was great and faster than prefixed
queries.

But it does not work for me.

Could you please give me the analyzer configuration and the mapping
for this use case ?

Thanks a lot.

--

Paul Loy
paul@keteracel.com (mailto:paul@keteracel.com)
http://uk.linkedin.com/in/paulloy

--
Alexandre Heimburger
R&D Manager
blueKiwi Software
tel : +33687880997
email : ahb@bluekiwi-software.com (mailto:ahb@bluekiwi-software.com)
adress : 93 rue Vieille du Temple, 75003 Paris

What is blueKiwi? blueKiwi - the first Enterprise Social Software Suite in the world building professional networks on conversations and relationships - helps large organizations increase their productivity, foster innovations and boost people satisfaction.