English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

This paper describes the goals, design and results of a shared task on the automatic linguistic annotation of German language data from genres of computer-mediated communication (CMC), social media interactions and Web corpora. The two subtasks of tokenization and part-of-speech tagging were performed on two data sets: (i) a genuine CMC data set with samples from several CMC genres, and (ii) a Web corpora data set of CC-licensed Web pages which represents the type of data found in large corpora crawled from the Web. The teams participating in the shared task achieved a substantial improvement over current off-the-shelf tools for German. The best tokenizer reached an F1score of 99.57% (vs. 98.95% off-the-shelf baseline), while the best tagger reached an accuracy of 90.44% (vs. 84.86% baseline). The gold standard (more than 20,000 tokens of training and test data) is freely available online together with detailed annotation guidelines. 1 Motivation, premises and goals Over the past decade, there has been a growing interest in collecting, processing and analyzing data from genres of computer-mediated communication and social media interactions (henceforth referred to as CMC) such as chats, blogs, forums, tweets, newsgroups, messaging applications (SMS, WhatsApp), interactions on “social network” sites and on wiki talk pages. The development of resources, tools and best practices for automatic linguistic processing and annotation of CMC discourse has turned out to be a desideratum for several fields of research in the humanities: 1. Large corpora crawled from the Web often contain substantial amounts of CMC (blogs, forums, etc.) and similar forms of noncanonical language. Such data are often regarded as “bycatch” that proves difficult for linguistic annotation by means of standard natural language processing (NLP) tools that are optimized for edited text (Giesbrecht and Evert, 2009). 2. For corpus-based variational linguistics, corpora of CMC discourse are an important resource that closes the “CMC gap” in corpora of contemporary written language and language-in-interaction. With a considerable part of contemporary everyday communication being mediated through CMC technologies, up-to-date investigations of language change and linguistic variation need to be able to include CMC discourse in their empirical analyses. In order to harness the full potential of corpusbased research, the preparation of any type of linguistic corpus which includes CMC discourse— whether a genuine CMC corpus or a broadcoverage Web corpus—faces the challenge of handling and annotating the linguistic peculiarities characteristic for the types of written discourse found in CMC genres. Two fundamental (but nontrivial) tasks are (i) accurate tokenization and (ii) sufficiently reliable part-of-speech (PoS) annotation. Together, they provide a layer of basic linguistic information on the token level that is a pre-

EmpiriST 2015: A Shared Task on the Automatic Linguistic Annotation of Computer-Mediated Communication and Web Corpora