Building a Wikipedia Text Corpus for Natural Language Processing
Wikipedia is a rich source of well-organized textual data, and a vast collection of knowledge. What we will do here is build a corpus from the set of English Wikipedia articles, which is freely and conveniently available online.
One of the first things required for natural language processing (NLP) tasks is a corpus. In linguistics and NLP, corpus (literally Latin for body) refers to a collection of texts. Such collections may be formed of a single language of texts, or can span multiple languages -- there are numerous reasons for which multilingual corpora (the plural of corpus) may be useful. Corpora may also consist of themed texts (historical, Biblical, etc.). Corpora are generally solely used for statistical linguistic analysis and hypothesis testing.
The good thing is that the internet is filled with text, and in many cases this text is collected and well oganized, even if it requires some finessing into a more usable, precisely-defined format. Wikipedia, in particular, is a rich source of well-organized textual data. It's also a vast collection of knowledge, and the unhampered mind can dream up all sorts of uses for just such a body of text.
What we will do here is build a corpus from the set of English Wikipedia articles, which is freely and conveniently available online.
In order to easily build a text corpus void of the Wikipedia article markup, we will use gensim, a topic modeling library for Python. Specifically, the
gensim.corpora.wikicorpus.WikiCorpus class is made just for this task:
Construct a corpus from a Wikipedia (or other MediaWiki-based) database dump.
In order to properly progress through the following steps, you will need to have gensim installed. It's a simple enough process; using pip:
Download the Wikipedia Dump File
A Wikipedia dump file is also required for this procedure, quite obviously. The latest such files can be found here.
A warning: the latest such English Wikipedia database dump file is ~14 GB in size, so downloading, storing, and processing said file is not exactly trivial.
The file I aquired and used for this task was
enwiki-latest-pages-articles.xml.bz2. Go ahead and download it or another similar file to use in the next steps.
Make the Corpus
I wrote a simple Python script (with inspiration from here) to build the corpus by stripping all Wikipedia markup from the articles, using gensim. You can read up on the WikiCorpus class (mentioned above) here.
The code is pretty straightforward: the Wikipedia dump file is opened and read article by article using the
get_texts() method of the
WikiCorpus class, all of which are ultimately written to a single text file. Both the Wikipedia dump file and the resulting corpus file must be specified on the command line.
After several hours, the above code leaves me with a corpus file named
Check the Corpus
A second script then checks the corpus text file we just built.
Now, keep in mind that this large Wikipedia dump file then resulted in a very large corpus file. Given its enormous size, you may have dificulty reading the full file into memory at one time.
This script, then, starts by reading 50 lines -- which equates to 50 full articles -- from the text file and outputting them to the terminal, after which you can press a key to output another 50, or type 'STOP' to quit. If you do stop, the script then proceeds to load the entire file into memory. Which could be a problem for you. You can, however, verify the text by batches of lines, in order to satisfy your curiousity that something good happened as a result of running the first script.
If you are planning on working on such a large text file, you may need some workarounds for its large size in comparison to your machine's memory.
The corpus file must be specified at the command line to execute.
And that's it. Some simple code to accomplish what gensim makes a simple task. Now that you are armed with an ample corpus, the natural language processing world is your oyster. Time for something fun.
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