Text normalization is the task of map** non-canonical language, typical of speech
transcription and computer-mediated communication, to a standardized writing. It is an up …

Text normalization methods have been commonly applied to historical language or user-
generated content, but less often to dialectal transcriptions. In this paper, we introduce …

Readability research has a long and rich tradition, but there has been too little focus on
general readability prediction without targeting a specific audience or text genre. Moreover …

Tweets often contain a large proportion of abbreviations, alternative spellings, novel words
and other non-canonical language. These features are problematic for standard language …

Swiss dialects of German are, unlike many dialects of other standardised languages, widely
used in everyday communication. Despite this fact, automatic processing of Swiss German is …

The paper presents two manually annotated Slovene language text normalisation datasets,
one of historical texts and the other of tweets, and proposes several variants of character …

Text normalization is an indispensable stage in processing noncanonical language from
natural sources, such as speech, social media or short text messages. Research in this field …

One of the main obstacles for many Digital Humanities projects is the low data availability.
Texts have to be digitized in an expensive and time consuming process whereas Optical …

Abstract The Swiss German dialect corpus Archi-Mob poses great challenges for NLP and
corpus linguistic research due to the massive amount of variation found in the transcriptions …

User generated texts on the web are freely-available and lucrative sources of data for
language technology researchers. Unfortunately, these texts are often dominated by …