Avoid wasting bandwidth capacity and processing time for webpages which are probably not worth the effort. Stay away from pages with little text in the target language or focus on other pages to gather links.

I recently attended the Web as Corpus Workshop in Gothenburg, where I had a talk for a paper of mine, Finding viable seed URLs for web corpora: a scouting approach and comparative study of available sources, and another with Felix Bildhauer and Roland Schäfer, Focused Web Corpus Crawling.

Summary

The comparison I did started from web crawling experiments I performed at the FU Berlin. The fact is that the conventional tools of the “Web as Corpus” framework rely heavily on URLs obtained from search engines. URLs were easily gathered that way until search engine companies restricted this allowance, meaning that …

I recently presented a paper at the third LRL Workshop (a joint LTC-ELRA-FLaReNet-META_NET workshop on “Less Resourced Languages, new technologies, new challenges and opportunities”).

Motivation

The state of the art tools of the “web as corpus” framework rely heavily on URLs obtained from search engines. Recently, this querying process became very slow or impossible to perform on a low budget.

Moreover, there are diverse and partly unknown search biases related to search engine optimization tricks and undocumented PageRank adjustments, so that diverse sources of URL seeds could at least ensure that there is not a single bias, but …

I am currently working on a project for which I need to identify WordPress blogs as fast as possible, given a list of URLs. I decided to write a review on this topic since I found relevant but sparse hints on how to do it.

First of all, let’s say that guessing if a website uses WordPress by analysing HTML code is straightforward if nothing was been done to hide it, which is almost always the case. As WordPress is one of the most popular content management systems, downloading every page and performing a check afterward is an option …

The analysis of URLs using natural language processing methods has recently become a research topic by itself, all the more since large URL lists are considered as being part of the big data paradigm. Due to the quantity of available web pages and the costs of processing large amounts of data, it is now an Information Retrieval task to try to classify web pages merely by taking their URLs into account and without fetching the documents they link to.

Why is that so and what can be taken away from these methods?

Interest and objectives

Obviously, the URLs contain clues …