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
Related
I have users that have authenticated with a social media site. Now based on their last X (let's say 200) posts, I want to map how much that content matches up with a finite list of keywords.
What would be the best way to do this to capture associated words/concepts (maybe that's too difficult) or just get a score of how much, say, my tweet history maps to 'Walrus' or 'banana'?
Would a naive Bayes work here to separate into 'matches' and 'no match'?
In Python I would say NLTK can easily do it. In Ruby maybe gem called lda-ruby will help you. Whole LDA concept is well explained here - look at Sarah Palin's email for example. There's even the example of an app (not entirely in Ruby, but still) which did that -> github.com/echen/sarah-palin-lda
Or maybe I just say stupid things and that can't help you at all. I'm not an expert ;)
A simple bayes would work in this case, it is highly used to detect if emails are spam or not so for a simple keyword matching it should work pretty well.
For this problem you could also apply a recommendation system where you look for the top recommended keyword for a user (or for a post).
There are a ton of ways for doing this. I would recommend you to read Programming Collective Intelligence. It is explained using python but since you know ruby there should be not problem to understand the code.
What's a good way to parse HTML in AppleScript?
I haven't dabbled in AppleScript in quite some time, and even when I did it was very minimal and uninvolved, so I don't really think naturally in the language quite yet. But I need to do some string manipulation and parse some HTML (basically some simple screen scraping).
Naturally, I'd like to avoid common pitfalls of HTML parsing. However, this is a temporary script and doesn't need to be particularly robust or supportable. I really just need to scrape specific substrings (from a known starting substring to the next known character) into a file.
I've done plenty of string manipulation in C# and similar languages, but AppleScript is an interesting change of pace to say the least. Can somebody point me to some good resources (Google searches on this subject seem to have a high noise-to-signal ratio), or help me out with some sample code snippets?
The ultimate goal of what I'm doing is to take a pre-determined list of pages, open each one in Safari (I'm doing everything through tell application "Safari"), parse out links which fit a certain pattern, and store all of those links in a file. Then go through that file, open each of those links, parse out more links which fit another pattern, and store all of those links in a file.
(The site is actually owned by someone we're working with, so don't worry about me violating any terms of service or anything like that. But for reasons outside the scope of this question, I'm doing some page scraping in AppleScript.)
I can't say enough good things about Matt Neuburg's AppleScript: the Definitive Guide. Without a doubt the most complete documentation of AppleScript ever done. Matt's also one of my favorite tech writers.
I would also check out this article. It contains a tutorial on how to do this; the example provided there parses HTML data from only one source, but I think it's worth looking at.
I'm building a site that allows users to make sense of a debate by graphically representing arguments for and against a particular issue. (Wrangl)
I'd like to categorise these debates so they are more easily found and connected. I don't want to irritate the person creating the debate by asking them to add tags and categories before they see any benefit, so I'm looking at a way of automatically extracting keywords.
What's a good approach for taking the debate's title and description (and possibly the content of the arguments themselves once there are some) to pull out, say, ten strong keywords that could be used as metadata to connect similar debates together, or even as the content of the "meta" keywords tag in the head of the HTML page where the debate is viewable. Eg. Datamapper vs ActiveRecord
The site is coded in Ruby with Sinatra, using DataMapper for data storage. I'm ideally looking for something which will work on Heroku (I don't have a way of writing files to disk dynamically), and I'd consider a web service, an API or ideally a Ruby gem.
Maybe you can use TextAnalyzer.
I understand that you're wanting to find an easy way of achieving this, I've recently dived into the world of NLP (Natural Language Processing) and Text-mining and its a daunting process of which most went far above my head.
Although i managed to code some functionality that resembles what you're looking for, though I did it in PHP. What i would suggest, that if you want it tailored to your project (Wrangl) then do it yourself.
Using the Porter stemming algorithm which I'm sure there will be Ruby code for.
Ruby Porter stemmer
You can try the salsaAPI to automatically extract keywords and categorize the debates!
I want in an application with a simple text input, enriched with some marks to include formatting or semantic labeling. I want the syntax as easy as possible and I want to include self-defined labels.
Example:
[bold]Stackoverflow[/bold] is a [tag]good[/tag] resource for programmers.
Tables would be needed too.
HTML/XML and LaTeX are mighty enough to allow this, but too complicated. Wiki-Syntax seems simple, but uses another symbol for each markup, has unclear quoting and every Wiki seems to have another syntax. For tables and similar stuff Wiki becomes very complicated.
Exists a language/syntax, that matches my needs or can be slightly changed to do so? Or do I have to invent something myself? In that case, do you have suggestions?
Definitely do NOT invent your own. There are plenty of simple markup languages already, and users HATE learning new ones. Trust me on this!
I would suggest using one of the following:
Textile
Markdown
BBCode
Make your decision based on your userbase, as well as what tools and parsers are available in your chosen language. For my site, we went with Textile, but I've found that BBCode tends to be the language that most people already know. However, this will vary with different user demographics.
StackOverflow, along with several other sites, uses Markdown. I think it will give you the best balance between features and simplicity.
Let me add ReStructuredText to the list.
An additional benefit of using it is given by the availability of ReStructuredText to Anything service that makes extremely easy to create HTML or PDF versions of the document.
As already pointed out there are a lot of lightweight markup languages (many are listed here: wikipedia article), there should be no need of creating your own.
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.