I'm wanting to incorporate Google's libphonenumber library into a CRM solution that I'm working on, to identify things such as:
Whether a phone number is mobile or landline
Geo-location of the number
I've done some searching online, and can't seem to find anything discussing what algorithms the library is using to determine this information, and how reliable those methods are.
Is there any such documentation (ie, details of the these algorithms and their respective reliability)? Or really, anything to help me understand what happens under-the-covers for this library?
It's an Open Source library, so you can see exactly how it works :)
svn checkout http://code.google.com/p/libphonenumber/source/checkout
I've had a quick look at the source, and it seems to work by testing the phone number with a series of regular expressions. Big regex files are defined for various countries, which define the regular expressions that will tell you the type of phone number (for example, in the UK, all mobiles start with "07", so there will be a regex based on that).
Related
I need to create a universal web scraper to parse articles on the different websites. Of course, I know about XPath, but I want to try to make it universal for any website despite the HTML markup of a page.
I need to determine whether there is an article on the page and if it is - parse a text of title, body and tags (if exists).
Frankly speaking, my knowledge in DS is not very huge, but I assume this task (determine whether it is article, and parsing only needed parts) is possible to solve.
What tools should I use? Any help?
Actually, for the second task, I need to implement something similar that google chrome mobile does. When page is not optimised for mobile, then propose to show the page in adaptive mode (just title, and main content).
If you are using Python, some libraries to look at are:
scrapy, which scrapes data and can extract some of the results) and,
BeautifulSoup, which is more geared towards the extraction part itself.
It is possible to request a version of a website (e.g. for Chrome, Safari, Mobile, old-school systems) by creating a custom header for your scraper.
HAve a look at the relevant documentation, and you can get an idea of how to use headers in scrapy here.
I do not know of any more specialised tools. Your tasks are more analytical and are typically not performed with the use of models for estimating e.g. what content is where on a webpage. This might be an intersting research direction though; to see if you can create a model that generalises across many websites to extract the desired content.
That leads me on to my last point, which is to say that creating a single scraper that works for any website *containing your artile type) is not usually possible. People create websites differently, however they see fit, which means they also change them. This usually leads to a good scraper requiring constant updates as time (and developers) moves on.
EDIT:
Then if you have lots of labelled examples, it might be possible to train a model. The challenge might be the look-back range of the model. For example, a typical LSTM model is given a parameter that tells it how far to look back into the past. It is stored within its memory internally. In your case, you might be looking for a start and end HTML tag of an article, to then extract just that part. These tahs could be thousands of words apart. Something a standard LSTM might not be fit to retain and use.
If you could pose your problem a little differently, then there are other approaches that might be plausible. E.g., you could make it a "question-answer" problem, by saying: I have this HTML, where is the article content? If that sounds ok for your use-case, have a look here for some model based approaches.
I got a financial application and I wish to add to it the ability to get user command or input in textbox and then take the right action. for example, wish the user to write "show the revenue in the last 10 days" and it'll show the revenue to him/her - the point is that I wish it to really understand the meaning of the question, so the previus statement will bring the same results as "do I got any revenue in the last 10 days" or something like that - BI (something like the Wolfram|Alpha engine).
I wonder if there's any opensource library or algorithm books or whatever that I can use to learn the subject. Regards to opensource libraries - I don't mind which language it'll be written in.
I've read about this subject and saw many engines and services (OpenNLP, Apache UIMA, CoreNLP etc.) but did not figure out if they're right for my needs.
Any answer or suggestion is welcome.
Many thanks!
The field you're talking about is usually called "natural language processing". It's hard, and an active field of research. There are various libraries which you could consider based on your preferred programming language and use case:
http://en.wikipedia.org/wiki/List_of_natural_language_processing_toolkits
I've used NLTK a little bit. This field is seriously difficult to get right, so you might want to try to restrict your application to some small set of verbs and nouns such that people are using a controlled vocabulary in the first instance, and then try to extend it beyond that.
I'm looking for a library or technique to detect the input language of blocks of text provided by users. Online lookups (like Google translate) won't work for this task as I'm writing an app which must run offline.
Thanks.
Here are two more n-gram-based gems you might want to try. They work offline.
https://github.com/echen/unsupervised-language-identification, optimized for separating english and other languages (has a live demo)
https://github.com/feedbackmine/language_detector, less specialized, will detect more languages. Some languages may need some extra training — I found it to be not precise enough for German text.
For anyone interested, I've found http://rubygems.org/gems/kenwaln-whatlanguage, which is performing excellently.
I'm using CLD which I really like, succinct and easy to use. Give it a try.
A quick demo of WhatLanguage in Ruby:
http://www.youtube.com/watch?v=lNqZ2cqOReo&list=UUJ_3fstMOH-g4yBxtvgAWkw&index=0&feature=plcp
I'm starting to modify my app, which uses all hardcoded strings for errors, GUI, etc. I'm considering these two approaches, but let me know if there is an even better way:
-Put all string in ressource (.rc) files.
-define all strings in a file, once for each language. Use a preprocessor define to decide which strings get compiled in.
Which of these two approaches is generally prefered?
Put all the strings in resource files. Once you've done that, there's several good translation packages available. One useful thing these packages do is allow you to get translation done by somebody who doesn't program.
Remember, also, that internationalization (i18n) is a large subject, and there's a lot of things to consider. It isn't just a matter of translating strings. Do a web search on it, at the very least. You might want to read a book on it: I used International Programming for Windows by Schmitt as a guide. It's an old book from Microsoft Press, and I had to get it through a used book service; most of the more modern stuff seems to be on internationalizing .NET apps.
Without knowing more about your project (what sort of software, who the intended audience is, what sort of organization you have, what sort of budget, why you're interested in internationalization, etc.), this is about the most I can tell you.
Generally you see locale specific resource files containing strings referenced by key. Compiling different versions for different locales is a very rigid solution and will be a maintenance nightmare. Using resource files also allows the user to have fallback locales.
There's another approach of just putting strings in the source with somethign like tr(" ") and usign one of the tools that strips them out and converts them.
It works with any toolkit/GUI library.
You can mark text to be converted and text not to change (such as protocol strings or db keys).
It makes the source easier to read and search, isntead of having to lookup what IDS_MESSAGE34 means.
One problem with resource files, at least with Windows/MFC, is that you can't use the stringtable in dialogs. So you have some text in the stringtabel and some in the dialog section which you have to dela with separately.
What are the best algorithms for recognizing structured data on an HTML page?
For example Google will recognize the address of home/company in an email, and offers a map to this address.
A named-entity extraction framework such as GATE has at least tackled the information extraction problem for locations, assisted by a gazetteer of known places to help resolve common issues. Unless the pages were machine generated from a common source, you're going to find regular expressions a bit weak for the job.
If you have the markup proper—and not just the text from the page—I second the Beautiful Soup suggestion above. In particular, the address tag should provide the lowest of low-hanging fruit. Also look into the adr microformat. I'd only falll back to regexes if the first two didn't pull enough info or I didn't have the necessary data to look for the first two.
If you also have to handle international addresses, you're in for a world of headaches; international address formats are amazingly varied.
I'd guess that Google takes a two step approach to the problem (at least that's what I would do). First they use some fairly general search pattern to pick out everything that could be an address, and then they use their map database to look up that string and see if they get any matches. If they do it's probably an address if they don't it probably isn't. If you can use a map database in your code that will probably make your life easier.
Unless you can limit the geographic location of the addresses, I'm guessing that it's pretty much impossible to identify a string as an address just by parsing it, simply due to the huge variation of address formats used around the world.
Do not use regular expressions. Use an existing HTML parser, for example in Python I strongly recommend BeautifulSoup. Even if you use a regular expression to parse the HTML elements BeautifulSoup grabs.
If you do it with your own regexs, you not only have to worry about finding the data you require, you have to worry about things like invalid HTML, and lots of other very non-obvious problems you'll stumble over..
What you're asking is really quite a hard problem if you want to get it perfect. While a simple regexp will get it mostly right most of them time, writing one that will get it exactly right everytime is fiendishly hard. There are plenty of strange corner cases and in several cases there is no single unambiguous answer. Most web sites that I've seen to a pretty bad job handling all but the simplest URLs.
If you want to go down the regexp route your best bet is probably to check out the sourcecode of
http://metacpan.org/pod/Regexp::Common::URI::http
Again, regular expressions should do the trick.
Because of the wide variety of addresses, you can only guess if a string is an address or not by an expression like "(number), (name) Street|Boulevard|Main", etc
You can consider looking into some firefox extensions which aim to map addresses found in text to see how they work
You can check this USA extraction example http://code.google.com/p/graph-expression/wiki/USAAddressExtraction
It depends upon your requirement.
for email and contact details regex is more than enough.
For addresses regex alone will not help. Think about NLP(NER) & POS tagging.
For finding people related information you cant do anything without NER.
If you need information like paragraphs get the contents by using tags.