[BangPypers] NLTK

[Gopalakrishnan Subramani]

Thanks Deepu. This seems to be viable approach. ElasticSearch does similar?


On Mon, Sep 16, 2013 at 12:34 AM, Deepu Thomas Philip
<deepu.dtp at gmail.com>wrote:

> Take a look at Apache Solr (http://lucene.apache.org/solr/).
> You can find python clients for Solr here
> http://wiki.apache.org/solr/SolPython
>
> For a quick solution, add all your products to solr with the name of the
> product as an indexed field.
> Add the *product id* if you plan on storing specs elsewhere.
> Add all the spec names to a list of stopwords while creating your solr
> index.
> Throw the user's query against the index and you should get a list of
> products rank ordered by a match score between the query and the product
> names stored.
> Remove the product name from the user's query and then do a match for the
> spec the user is looking for. You can use regular expressions or
> https://code.google.com/p/esmre/ for this.
>
> You should be able to account for spelling errors in the user's query with
> a little more work on the Solr side of things. Solr will also open up use
> cases where the user wants a list of phones which weigh 200g and costs
> <10k.
>
> Regards,
> Deepu
>
> On Sun, Sep 8, 2013 at 12:04 PM, Gopalakrishnan Subramani <
> gopalakrishnan.subramani at gmail.com> wrote:
>
> > I have database of specs in json format. This is not manual effort.
> >
> > Right now, NLTK seems to be hard to me. I will try a plain Python
> wrappers
> > based on word match, approach NLTK later.
> >
> > Thanks.
> >
> >
> > On Sun, Sep 8, 2013 at 11:29 AM, harish badrinath <
> > harishbadrinath at gmail.com
> > > wrote:
> >
> > > Hello,
> > >
> > > On Sun, Sep 8, 2013 at 2:34 AM, Gopalakrishnan Subramani <
> > > gopalakrishnan.subramani at gmail.com> wrote:
> > >
> > > > Dear All,
> > > >
> > > > I  want to build a simple automatic text based chat bot for mobile,
> > > tablet
> > > > specs for proof of concept.
> > > >
> > > > How do you plan to preseed the knowledge for the application
> (manually
> > or
> > > information extraction through webpages,etc).
> > >
> > >
> > > > The question is, when the user talks about  "Samsung Galaxy S3
> Weight",
> > > > "Galaxy SIII Weight", can NLTK predict a product (ex: Galaxy SIII)
> and
> > > give
> > > > me the unique _id of the product for further look up for
> > group/attribute
> > > > like weight?
> > > >
> > > > If it is manually enter the knowledge then nltk should not be
> required
> > (
> > > something like yacc plus a good database schema should suffice, again
> > > depends on the type of input language you plan to support).
> > >
> > > Warm regards,
> > > Harish Badrinath
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