I was reading an article that said:
An example of this is that, by choosing the technology stack well, the same language can now be used in all development contexts: Javascript in the desktop client (DOM), in the mobile client (Cordova, React Native), in the server (Node.js) or in the BBDD (MongoDB).
I don't understand what BBDD is, even looking it up comes with a bunch of spanish to english stuff. Do you know hat it stands for?
In Spanish, I believe you often repeat a letter in an abbreviation to indicate a plural, as in EE UU for Estados Unidos = USA.
So presumably, BBDD is the plural of BD = bases de datos or in English DB = databases.
Incidentally in French you will see the abbreviation BDD = base de données for database.
Related
I'm starting do develop a game (AS3), and in one step, the participants have to type a word in one of 5 different available languages, and then that word is translated to the other 4.
For the sake of example:
I choose the word "home" in English, and then these fields are filled:
Spanish: casa
Russian: домой
German: Zuhause
French: maison
So the question is, what would be the best approach to do it?, are there any downloadable dictionaries available for different languages?, or it would be better to feed from a web service?.
Also something to consider is that the translations shouldn't consist of more than one word.
I never worked with dictionaries before, so I'd rather investigate a bit instead of starting with the left foot. Thanks.
You have to use property file. This is best approach to do multi-languaged application.
Is there any library for auto detection of phrasal (compound) verbs in English texts? And maybe other kinds of word groups that form a special meaning?
I'm not aware of a library for that. However, you might find this article useful. It discusses using n-gram (Markvov) models and statistical methods (such as SVM) to detect not only phrasal verbs, but also other phrasal terms.
[[http://seer.lcc.ufmg.br/index.php/jidm/article/viewFile/79/39]]
"Using Statistical Features to Find Phrasal Terms in Text Collections".
André Luiz da Costa Carvalho, Edleno Silva de Moura, Pável Calado.
Journal of Information and Data Management 1(3), October 2010: 583–597.
I am writing an application. Such as Chinese/Japanese/Arabic/etc to English or English to french/dutch/russian. I do not have internet, where the project will use, so i can not use other existing internet translation services. So i am trying to collect a framework for my language such as Java or C or Python or D.
How to get Google like text translation framework as open source/free to use for quality text translation or which other framework can be used for this project?
Challenge is:
"how are you?" = hoe gaat het
= 你是如何
= お元気ですか？
= كيف حالك
= आप कैसे हैं
I don't understand the question.
If you want your application to display various user messages in several human languages, you need to worry about localization or internationalization. With C or C++ on Posix, functions like gettext are relevant.
Several libraries or frameworks also provide related features. As an example, Qt has its internationalization framework
Or do you want to automatically translate text from one human language to another? This is an entire research domain (machine translation), so prepare to get a PhD on that; you'll need several years of hard work (and the results are still not very satisfactory).
I'm looking into using a CASS-Certified address validation service to correct user-provided street addresses at the time of entry. (Specifically, I'm looking at SmartyStreets' LiveAddress.) However, USPS dictates that a correct address must be in all caps, so CASS services almost uniformly return addresses that way. When mailing to the client at that address, though, it would be preferable to use a more humane, conventional casing.
The question, of course, is how to make that happen. I know there's no such thing as a perfect solution that doesn't involve an complete nation-wide database of correctly capitalized street and city names. A set of passable heuristics might be good enough, though, since we will probably be kicking the corrected address back to the user, ultimately leaving it up to them.
A short list of problems that I was able to come up with after a few minutes of thought:
SW FIRST ST should be SW First St, not Sw First St.
MCDOUGLE ST should be McDougle St, not Mcdougle St.
MACDOUGLE ST should probably be Macdougle St rather than MacDougle St, since Macoroni Rd should usually not be MacOroni Rd.
1ST ST should be 1st St, not 1St St.
Not knowing if a street name is based on a surname, we can possibly not safely make VAN into van, but VON can probably become von.
Are there any existing libraries that could at least get me started? Addresses are complicated and fickle things, and I'd rather not home-brew the whole thing if I don't have to. I'm using C#, but I'm open to porting code from another language.
Barring that, does anyone have a decent reference of common capitalization exceptions for street and/or city names?
Great to see that you're using the LiveAddress service to facilitate address verification and standardization. There is one thing you may want to be aware of that will help you significantly in the process of applying casing rules to your standardized address:
We recently introduced a new REST+JSON endpoint that returns the standardized form of the address as well as various component parts of the address. Because of this, it's very easy to apply your casing rules to "street_name" and "city_name" values returned independent of the street suffix and pre/post-directionals.
You're welcome to contact SmartyStreets support for additional help with this issue in addition to questions here on Stack Overflow (which we monitor continually). I should probably also mention that I'm the founder of SmartyStreets. Lastly, we're working on being able to return properly cased addresses, but I don't have any kind of release time frame on it yet.
Not a library, but you could probably solve the problem with the Google Maps API depending on your requirements.
If you have used indeed.com before, you may know that for the keywords you look for, it returns a traditional search results as long as multiple search refinement options on the left side of screen.
For example, searching for keyword "designer", the refinement options are:
Salary Estimate
$40,000+ (45982)
$60,000+ (29795)
$80,000+ (15966)
$100,000+ (6896)
$120,000+ (2828)
Title
Floral Design Specialist (945)
Hair Stylist (817)
GRAPHIC DESIGNER (630)
Hourly Associates/Co-managers (589)
Web designer (584)
more »
Company
Kelly Services (1862)
Unlisted Company (1133)
CyberCoders Engineering (1058)
Michaels Arts & Crafts (947)
ULTA (818)
Elance (767)
Location
New York, NY (2960)
San Francisco, CA (1633)
Chicago, IL (1184)
Houston, TX (1057)
Seattle, WA (1025)
more »
Job Type
Full-time (45687)
Part-time (2196)
Contract (8204)
Internship (720)
Temporary (1093)
How does it gather statistics information so quickly (e.g. the number of job offers in each salary range). Looks like the refinement options are created in realtime since minor keywords load fast too.
Is there a specific SQL technique to create such feature? Or is there a manual on the web explaining the tech behind this?
The technology used in Indeed.com and other search engines is known as inverted indexing which is at the core of how search engines work (e.g Google). The filtering you refer to ("refinement options") are known as facets.
You can use Apache Solr, a full-fledged search server built using Lucene and easily integrable into your application using its RESTful API. Comes out-of-the-box with several features such as faceting, caching, scaling, spell-checking, etc. Is also used by several sites such as Netflix, C-Net, AOL etc. - hence stable, scalable and battle-tested.
If you want to dig deep into facet based filtering works, lookup Bitsets/Bitarrays and is described in this article.
Why do you think that they load "too fast"? They certainly have nice, scaled architecture, they use caching for sure, they might be using some denormalized datastore to accelerate some computations and queries.
Take a look at google and number of web pages worldwide - you also think that google works too fast?
In addition to what Mios said and as Daimon mentioned it does use a denormalized doc store. Here is a link to Indeed's tech talk about its docstore
http://engineering.indeed.com/blog/2013/03/indeedeng-from-1-to-1-billion-video/
Also another related article on their Engineering blog:
http://engineering.indeed.com/blog/2013/10/serving-over-1-billion-documents-per-day-with-docstore-v2/