I have a question about parsing HTML pages, specificaly forums,
i want to parse a forum or thread containing certain post criterias, i havent defined the
algorithm yet, since i have only parsed structure text formats before,
A use case may be copy and paste each thread into the program by hand, or insert a URL like
http://www.forums.com/forum/showthread.php?t=46875&page=3 and let the program parse the pages
Given all this i would like to know:
Is it possible to parse a forum thread on a HTML page?
what would be the best/Fastest/easiest language for doing this?
If i prefer Java what tools/libraries do i need for this?
Any other thing i should consider?
1 / yes
2 / Use some compact language like python or ruby for prototyping.
For python there is a neat library for HTML/XML parsing called beautifulsoup
For ruby, you could try: nokogiri or hpricot
3 / A Java tool to consider: htmlparser
4 / If you are interested only in some particular text or some special classes, a regular expression might be sufficient. But as soon as you want to dig deeper into the structure of the content, you'll need some kind of model to hold your data, and hence a parser, which, in the best case, can cope with the occuring incosistencies of real world html.
You might want to look into some sort of html parsing library, rather than using regular expressions to do this. There are some really good html parsers for ruby and python, but a quick google shows there to be a number of parsers for java as well. The benefit of these libraries is that you don't have to handle every edge case with regular expressions/they handle malformed html (both of which can be impossible with regexes, depending on what you want to do) and they also give you a much way of dealing with the data (for example, beautiful soup lets you grab all elements which belong to a specific class or to use some other css selector to limit which page elements you want to deal with).
Personally, I would, at least for the beginning, start in ruby or python, as the libraries are known and there is a lot of info about using them for this purpose. Also, I find it easier to quickly prototype these types of things in ruby or python than in the jvm. You could even later bring that code onto the jvm with jruby or jython, if it becomes necessary.
yes
regular expressions, any flavor.
probably the ones w/regex
there are tools out there that will do this for you.
Related
I need a library to parse HTML, change some attributes of some elements, then write back result into HTML.
Is there a library for it?
In other languages (like PHP), there are DOM parsers. I found libraries for parsing HTML, but none of them allowed manipulation and generation (or I did not see it?).
Have you seen Floki?
Floki is a simple HTML parser that enables search for nodes using CSS selectors.
Its parsing capabilities seem decent but not sure about manipulation. It has a transform method but there are no usage examples.
Seems like there is not a library to parse and manipulate and write HTML.
There is this Eml that can write HTML, and although it has parser, it says it is not a multi-purpose one. I think I will do good for now, but for other usages, one might want to use another parser and Eml together to have a better solution.
PS. I will accept this answer if no one provides a better solution in a couple of days.
I'm looking for steps/libraries/approaches to solve this Problem statement.
Given a source file of a Programming language, I need to parse it and Subdivide it into components.
Example:
Given a Java File, I need to find the following in it.
list of Imports
Classes present in it
Attributes in the Class
Methods in it - along the Parameters if any.
etc.
I need to extract these and store it separately.
Reason Why I want to do it?
I want to build an Inverted Index on the top of these Components.
Example queries to Inverted index
1. Find the list of files with Class name: Sample
2. Find the positions where variable XXX is used within the class AAA.
I need to support queries likes the above
So, my plan is given a file, if I build these components from it, It would be easy to build an Inverted index on the top of it.
Example: Sample -- Class - Sample.java(Keyword - Component - FileName )
I want to build an Inverted index like above.
I see it is being implemented in many IDEs like IntelliJ.What I'm interested it how much effort it would take to build something like this. And I want to try implementing the same for at least one language.
Thanks in advance.
You can try to do this "just" a parser; for your specific example, that might be enough.
But you'll need a parser for each language. If you stick to just Java, you can find Java parsers pretty easily; just reuse one, there is little point in you reinventing one more set of grammar rules to describe Java.
For more than one language, this starts to get tricky. You can:
try to find a separate parser for each language. This may be sort of successful for mainstream languages. As you get to less well known languages, these get a lot harder to find. If you succeed, you'll have the problem that the parsers are likely incompatible technology; now gluing them together to collectively collect your index information is going to be a mess.
pick one parsing technology and get grammars for all the languages you care about. You have only two realistic choices: YACC/Bison, and ANTLR.
As a practical matter the YACC and Bison have been used to implement LOTS of languages... but the grammar files are not collected in one place, so they are hard to find. ANTLR at least has a single repository you can find at their web site. So that might kind of work.
Its going to be quite the effort to assemble all these into an integrated whole.
A complication is that you may want more than just raw syntax; you might want to know the meaning of the symbols, and for each symbol, precisely where it is defined in which file. After all, you want your index to be accurate at scale, and this will require differentiating foo the variable name from foo the function name. Arguably you need symbol tables.
As a general rule, this is where pure-parsing of languages breaks down;
there is serious Life After Parsing.
In that case, you want an integrated set of tools for extracting information from the different languages.
Our DMS Software Reengineering Toolkit is such a framework, and has some 40 languages predefined for it. We use something like OP's suggested process to build indexes of a code base for search tools based on DMS. Building something like DMS is an enormous effort.
I've seen answers to this question but I couldn't figure out which of the answers would perform the fastest. These are the answers I've seen- which is best?
Read one line at a time using each or each_line
Read one line at a time using gets
Save it all into an array of lines using readlines and then use each
Use grep (not sure what exactly to do with grep...)
Use sed (not sure what exactly to do with sed...)
Something else?
Also, would it be better to just use another language or should Ruby be fine?
EDIT:
More details: Each line contains something like "id1 attr1_1 attr2_1 id2 attr1_2 attr2_2... idn attr1_n attr2_n" (n is very big) and I need to insert those into a database. For that example line, I would need to insert n rows into the database.
Ruby will likely be using the same or very similar low-level code (written in C) to do the actual reading from disk for the first three options, so they should perform similarly. Given that, you should choose whichever is most convenient for you; the ability to do that is what makes languages like Ruby so useful! You will be reading a lot of data from disk, so I would suggest using each_line and processing each line as you read it.
I would not recommend bringing grep, sed, or any other such external utilities into the picture unless you have a very good reason, as they will make your code less portable and expose you to failures that may be difficult to diagnose.
If you're using Ruby then there's no need to worry about performance. The language is such that it suits an iterative approach to reading a file, line by line, and works very nicely. So long as you're using the language the way it's designed you can let the interpreter people worry about performance. Job done.
If one particular readLargeFileFast method is needed then it should be because it's really hindering the program somehow. Now, you write a C program to do it and popen it as a separate process within your ruby code. You could call it read_large.c and (perhaps) use command line arguments to tell it how to behave.
This is championing the idea that a scripting language is used for a fast development rather than a fast run time. As such a developer can be very productive by swiftly 'prototyping' a program in something like Ruby and only later rewriting the components warrant some low level code. Often, however, once it's working in script, it's not necessary to do anything else at all.
The Ruby Docs describe launching a separate process and treating it as a file. It's easy-peasy! A good start is The Art of Linux Programming's introductory paragraph on program modularity. This book also makes a great example of using linux's standard stream editor, called sed, which you could probably use from Ruby right now.
If you need to parse or edit a lot of text then many interpreters or editors have been written around sed's functionality. Further, it may save you a lot of effort writing something super efficient if you don't know C. Good is the Introduction to SED by Bruce Barnett.
I'm not talking about HTML tags, but tags used to describe blog posts, or youtube videos or questions on this site.
If I was crawling just a single website, I'd just use an xpath to extract the tag out, or even a regex if it's simple. But I'd like to be able to throw any web page at my extract_tags() function and get the tags listed.
I can imagine using some simple heuristics, like finding all HTML elements with id or class of 'tag', etc. However, this is pretty brittle and will probably fail for a huge number of web pages. What approach do you guys recommend for this problem?
Also, I'm aware of Zemanta and Open Calais, which both have ways to guess the tags for a piece of text, but that's not really the same as extracting tags real humans have already chosen. But I would still love to hear about any other services/APIs to guess the tags in a document.
EDIT: Just to be clear, a solution that already works for this would be great. But I'm guessing there's no open-source software that already does this, so I really just want to hear from people about possible approaches that could work for most cases. It need not be perfect.
EDIT2: For people suggesting a general solution that usually works is impossible, and that I must write custom scrapers for each website/engine, consider the arc90 readability tool. This tool is able to extract the article text for any given article on the web with surprising accuracy, using some sort of heuristic algorithm I believe. I have yet to dig into their approach, but it fits into a bookmarklet and does not seem too involved. I understand that extracting an article is probably simpler than extracting tags, but it should serve as an example of what's possible.
Systems like the arc90 example you give work by looking at things like the tag/text ratios and other heuristics. There is sufficent difference between the text content of the pages and the surrounding ads/menus etc. Other examples include tools that scrape emails or addresses. Here there are patterns that can be detected, locations that can be recognized. In the case of tags though you don't have much to help you uniqely distinguish a tag from normal text, its just a word or phrase like any other piece of text. A list of tags in a sidebar is very hard to distinguish from a navigation menu.
Some blogs like tumblr do have tags whose urls have the word "tagged" in them that you could use. Wordpress similarly has ".../tag/..." type urls for tags. Solutions like this would work for a large number of blogs independent of their individual page layout but they won't work everywhere.
If the sources expose their data as a feed (RSS/Atom) then you may be able to get the tags (or labels/categories/topics etc.) from this structured data.
Another option is to parse each web page and look for for tags formatted according to the rel=tag microformat.
Damn, was just going to suggest Open Calais. There's going to be no "great" way to do this. If you have some target platforms in mind, you could sniff for Wordpress, then see their link structure, and again for Flickr...
I think your only option is to write custom scripts for each site. To make things easier though you could look at AlchemyApi. They have simlar entity extraction capabilities as OpenCalais but they also have a "Structured Content Scraping" product which makes it a lot easier than writing xpaths by using simple visual constraints to identify pieces of a web page.
This is impossible because there isn't a well know, followed specification. Even different versions of the same engine could create different outputs - hey, using Wordpress a user can create his own markup.
If you're really interested in doing something like this, you should know it's going to be a real time consuming and ongoing project: you're going to create a lib that detects which "engine" is being used in a page, and parse it. If you can't detect a page for some reason, you create new rules to parse and move on.
I know this isn't the answer you're looking for, but I really can't see another option. I'm into Python, so I would use Scrapy for this since it's a complete framework for scraping: it's complete, well documented and really extensible.
Try making a Yahoo Pipe and running the source pages through the Term Extractor module. It may or may not give great results, but it's worth a try. Note - enable the V2 engine.
Looking at arc90 it seems they are also asking publishers to use semantically meaningful mark-up [see https://www.readability.com/publishers/guidelines/#view-exampleGuidelines] so they can parse it rather easily, but presumably they must either have developed a generic rules such as #dunelmtech suggested tag/text ratios, which can work with article detection, or they might be using with a combination of some text-segmentation algorithms (from Natural Language Processing field) such as TextTiler and C99 which could be quite usefull for article detection - see http://morphadorner.northwestern.edu/morphadorner/textsegmenter/ and google for more info on both [published in academic literature - google scholar].
It seems that, however, to detect "tags" as you required is a difficult problem (for already mentioned reasons in comments above). One approach I would try out would be to use one of the text-segmentation (C99 or TextTiler) algorithms to detect article start/end and then look for DIV's / SPAN's / ULs with CLASS & ID attributes containing ..tag.. in them, since in terms of page-layout's tags tend to be generally underneath the article and just above the comment feed this might work surprisingly well.
Anyway, would be interesting to see whether you got somewhere with the tag detection.
Martin
EDIT: I just found something that might really be helpfull. The algorithm is called VIPS [see: http://www.zjucadcg.cn/dengcai/VIPS/VIPS.html] and stands for Vision Based Page Segmentation. It is based on the idea that page content can be visually split into sections. Compared with DOM based methods, the segments obtained by VIPS are much more semantically aggregated. Noisy information, such as navigation, advertisement, and decoration can be easily removed because they are often placed in certain positions of a page. This could help you detect the tag block quite accurately!
there is a term extractor module in Drupal. (http://drupal.org/project/extractor) but it's only for Drupal 6.
Out of curiosity, I wonder what can people do with parsers, how they are applied, and what do people usually create with it?
I know it's widely used in programming language industry, however I think this is just a tiny portion of it, right?
Besides special-purpose languages, my most ambitious use of a parser generator yet (with good old yacc back in C, and again later with pyparsing in Python) was to extract, validate and possibly alter certain meta-info from SQL queries -- parsing SQL properly is a real challenge (especially if you hope to support more than one dialect!-), a parser generator (and a lexer it sits on top of) at least remove THAT part of the job!-)
They are used to parse text....
To give a more concrete example, where I work we use lexx/yacc to parse strings coming over sockets.
Also from the name it should give you an idea what javacc is used for (java compiler compiler!)
Generally to parse Domain Specific Languages or scripting languages, or similar support for code snipits.
Previously I have seen it used to parse the command line based output of another software tool. This way the outer tool (VPN software) could re-use the base router IPSec code without modification. As lots of what was being parsed was IP Route tables and other structured repeated text.
Using a parser allowed simple changes when the formatting changed, instead of trying to find and tweak the a hand written parser. And the output did change a few times of the life of the product.
I used parsers to help process +/- 800 Clipper source files into similar PRGs that could be compiled with Alaksa Xbase 32.
You can use it to extend your favorite language by getting its language definition from their repository and then adding what you've always wanted to have. You can pass the regular syntax to your application and handle the extension in your own program.