Add ... Greek ?

For each major enviromnent/language please develop a written (printable / pdf) that describes how to get started and to write your first application using ypour API. Then supplement this with other examples illustrating simple but 'real' problems being solved through the software/API. - Thanks

Except language detection, no api parameters indicates in which language we need to work.
i saw only "en_associative or en-synonymous" .And It seems we have to contact sales to access a retina french database.
So how can we try to validate interest ?
( i tried with default en_.. but got only non significant words le,la, les, de, des ... which demonstrate no use of language )
is this product only trained for english ?

i am using the fullclient rest-api within python. i tried to extract keywords from german text. unfortunately the results are quite poor. major problem is that stop words like "der", "die", "das" and so on are primarily returned. these are probably the most common terms in an usual text but also the least relevant. is it possible to optimize the language specific operations, e.g. train the algorithm myself?

Tutorial on time series of texts

I have a large number of documents, of 500 to 2000 words, that I would like link to each other based on semantic similarity. The number of documents will increase rapidly so a process of comparing each document with all other documents will not scale.

As a solution I plan to retrieve a retina representation (Fingerprint) for each document from:
/text?retina_name=en_associative

Then find the 50 documents with the largest intersection of the values in the Fingerprint, then retrieve a representation of similarity with those 50 documents form:
compare?retina_name=en_associative

Is this the most efficient strategy ?

I am trying to compare two texts using the curl API function, but I'm not certain how based on the example because it only says:

Request URL
http://api.cortical.io:80/rest/compare?retina_name=en_associative

but doesn't provide how we would input the two elements. I tried curl -X GET -H "api-key: xxxxx" "http://api.cortical.io:80/rest/compare?retina_name=en_associative&body=[{"term": "Pablo Picasso"},{"text": "Gustav Klimt was born in Baumgarten, near Vienna in Austria-Hungary, the second of seven children"}]"

but that did not work. Any help would be appreciated. Thanks.

Human with Baby = 13% related but Shoes with Baby = 19% how exactly this is working i wanted to find out which is more related term to baby of course it's human not shoes.

I am using the compare api and ultimately I am trying to search for news articles that have to do with License agreements between companies. And in my first test I ran the following example but got a low 0.05 cosine similarity.

[
{
"term": "License Agreement"
},
{
"text": "Google and apple sign License Agreement to use a new search technology"
}
]

If we could use use topic modeler to define phrases in terms existing terms in Retina. Eg. Grease my hands = Corruption or Crime sub government. And after creating the signature train the retina's with that so next time if I have a sentence like "I will pass the deal just make sure Mr X hands are greased." Is it possible to do it in Amazon Image?

With the interactive api I tried "/expressions/contexts" (http://api.cortical.io/Expression.htm#!/expressions/getContextsForExpression_post_1) with the following text input and I did not expect to get “cheese” as a result in the list.
{
"text": "I was drinking beer with my friends at the Oktoberfest"
}
Do you have any suggestion to improve the result here? Maybe it would be awesome just to get words describing the context which are actually used in the text?

Would it be possible to use the cortical.io Retina for a specific and isolated business domain that’s only accessible for one partner?