The issue with URLs and URIs

A Uniform Resource Locator (URL), colloquially termed a web address, is a reference to a web resource that specifies its location on a computer network and a mechanism for retrieving it. A URL is a specific type of Uniform Resource Identifier (URI).

Both navigation on the Web and web crawling rely on the assumption that “the Web is a space in which resources are identified by Uniform Resource Identifiers (URIs).” (Berners-Lee et al., 2006) That being said, URLs cannot be expected to be entirely reliable. Especially as part of the Web 2.0 content on the Web is changing faster than ever before, it can be tailored to a particular geographic or linguistic profile and isn’t stable in time.

Although URLs cannot be expected to be perfect predictors for the content which gets downloaded, they are often the only indication according to which crawling strategies are developed. It can be really useful to identify and discard redundant URIs, that is different URIs leading to similar text, which can also be called DUST (Schonfeld et al. 2006). Refining and filtering steps relie on URL components such as host/domain name, path and parameters/query …

Introduction

Although text is ubiquitous on the Web, extracting information from web pages can prove to be difficult, and an important problem remains as to the most efficient way to gather language data. Metadata extraction is part of data mining and knowledge extraction techniques. Dates are critical components since they are relevant both from a philological standpoint and in the context of information technology.

In most cases, immediately accessible data on retrieved webpages do not carry substantial or accurate information: neither the URL nor the server response provide a reliable way to date a web document, i.e. to find when it was written or modified. In that case it is necessary to fully parse the document or apply robust scraping patterns on it.

State of the art

Diverse extraction and scraping techniques are routinely used on web document collections by companies and research institutions alike. Content extraction mostly draws on Document Object Model (DOM) examination, that is on considering a given HTML document as a tree structure whose nodes represent parts of the document to be operated on. Less thorough and not necessarily faster alternatives use superficial search patterns such as regular expressions in order to capture desirable excerpts …

Although text is ubiquitous on the Web, extracting information from web pages can prove to be difficult. They come in different shapes and sizes mostly because of the wide variety of platforms and content management systems, and not least because of varying reasons and diverging goals followed during web publication.

This wide variety of contexts and text genres leads to important design decisions during the collection of texts: should the tooling be adapted to particular news outlets or blogs that are targeted (which often amounts to the development of web scraping tools) or should the extraction be as generic as possible to provide opportunistic ways of gathering information? Due to a certain lack of time resources in academia and elsewhere, the second option is often best.

Consequently, an important problem remains as to the most efficient way to gather language data. Between CMS idiosyncrasies, bulky pages and malformed HTML, the chosen solution has to be precise, robust and fast at the same time. The purpose of this evaluation is to test currently available alternatives with respect to particular needs for coverage and speed.

The current benchmark focuses on Python, reportedly the most popular programming language in academia and one of …