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Common Crawl is a considerably large, heterogeneous multilingual corpus comprised of crawled documents from the internet, surpassing 20TB of data and distributed as a set of more than 50 thousand plain text files where each contains many documents written in a wide variety of languages. Even though each document has a metadata block associated to it, this data lacks any information about the language in which each document is written, making it extremely difficult to use Common Crawl for monolingual applications. We propose a general, highly parallel, multithreaded pipeline to clean and classify Common Crawl by language; we specifically design it so that it runs efficiently on medium to low resource infrastructures where I/O speeds are the main constraint. We develop the pipeline so that it can be easily reapplied to any kind of heterogeneous corpus and so that it can be parameterised to a wide range of infrastructures. We also distribute a 6.3TB version of Common Crawl, filtered, classified by language, shuffled at line level in order to avoid copyright issues, and ready to be used for NLP applications.
Text corpora come in many different shapes and sizes and carry heterogeneous annotations, depending on their purpose and design. The true benefit of corpora is rooted in their annotation and the method by which this data is encoded is an important factor in their interoperability. We have accumulated a large collection of multilingual and parallel corpora and encoded it in a unified format which is compatible with a broad range of NLP tools and corpus linguistic applications. In this paper, we present our corpus collection and describe a data model and the extensions to the popular CoNLL-U format that enable us to encode it.
As the Web ought to be considered as a series of sources rather than as a source in itself, a problem facing corpus construction resides in meta-information and categorization. In addition, we need focused data to shed light on particular subfields of the digital public sphere. Blogs are relevant to that end, especially if the resulting web texts can be extracted along with metadata and made available in coherent and clearly describable collections.
Nearly all of the very large corpora of English are “static”, which allows a wide range of one-time, pre-processed data, such as collocates. The challenge comes with large “dynamic” corpora, which are updated regularly, and where preprocessing is much more difficult. This paper provides an overview of the NOW corpus (News on the Web), which is currently 8.2 billion words in size, and which grows by about 170 million words each month. We discuss the architecture of NOW, and provide many examples that show how data from NOW can (uniquely) be extracted to look at a wide range of ongoing changes in English.
This contribution aims to describe privacy, publicness and anonymity as essential analytic dimensions for media linguistic research. The dimensions are not inherent in and predetermined by the technical features and forms of communication provided by mobile devices, but are used by the participants as an orientation grid for shaping their online and offline practices in and with mobile media. Consid-ering both mobile device use in the public realm and the dissemina-tion of increasingly private content in social media (which is said to lead to ‘blurred boundaries’ between the private and the public), the paper provides a brief overview of the main developments in mobile media research: Studies adopting various approaches – e. g. socio-logical-ethnographic, linguistic and media studies – illustrate how publicness, privacy and anonymity are actively shaped and brought about by mobile media users in face-to-face and remote social en-counters. As this shows that publicness, privacy and anonymity are still relevant concepts for users, future media linguistics studies should focus on the dynamic multimodal practices by which they are contextualized and accomplished.
This paper aims at investigating the usage of present subjunctive (Konjunktiv I), which is traditionally labelled as a feature of standard written language and therefore as typically occurring in communication genres based on it such as press texts and reporting, in everyday spoken German. Through an analysis of corpus data performed according to theory and method of Interactional Linguistics and encompassing private, institutional and public interactional domains, the paper will show how this particular verb form expresses different epistemic stances according to its syntactic embedment.