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Machine learning methods offer a great potential to automatically investigate large amounts of data in the humanities. Our contribution to the workshop reports about ongoing work in the BMBF project KobRA (http://www.kobra.tu-dortmund.de) where we apply machine learning methods to the analysis of big corpora in language-focused research of computer-mediated communication (CMC). At the workshop, we will discuss first results from training a Support Vector Machine (SVM) for the classification of selected linguistic features in talk pages of the German Wikipedia corpus in DeReKo provided by the IDS Mannheim. We will investigate different representations of the data to integrate complex syntactic and semantic information for the SVM. The results shall foster both corpus-based research of CMC and the annotation of linguistic features in CMC corpora.
The paper presents an XML schema for the representation of genres of computer-mediated communication (CMC) that is compliant with the encoding framework defined by the TEI. It was designed for the annotation of CMC documents in the project Deutsches Referenzkorpus zur internetbasierten Kommunikation (DeRiK), which aims at building a corpus on language use in the most popular CMC genres on the German-speaking Internet. The focus of the schema is on those CMC genres which are written and dialogic―such as forums, bulletin boards, chats, instant messaging, wiki and weblog discussions, microblogging on Twitter, and conversation on “social network” sites.
The schema provides a representation format for the main structural features of CMC discourse as well as elements for the annotation of those units regarded as “typical” for language use on the Internet. The schema introduces an element <posting>, which describes stretches of text that are sent to the server by a user at a certain point in time. Postings are the main constituting elements of threads and logfiles, which, in our schema, are the two main types of CMC macrostructures. For the microlevel of CMC documents (that is, the structure of the <posting> content), the schema introduces elements for selected features of Internet jargon such as emoticons, interaction words and addressing terms. It allows for easy anonymization of CMC data for purposes in which the annotated data are made publicly available and includes metadata which are necessary for referencing random excerpts from the data as references in dictionary entries or as results of corpus queries.
Documentation of the schema as well as encoding examples can be retrieved from the web at http://www.empirikom.net/bin/view/Themen/CmcTEI. The schema is meant to be a core model for representing CMC that can be modified and extended by others according to their own specific perspectives on CMC data. It could be a first step towards an integration of features for the representation of CMC genres into a future new version of the TEI Guidelines.