Sentiment analysis is an area of research that shows an upward trend, especially in the last
two years due to large-scale production of opinions and comments from users active over …

Lexical normalization is the task of transforming an utterance into its standardized form. This
task is beneficial for downstream analysis, as it provides a way to harmonize (often …

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

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 …

We present the winning entry to the Multilingual Lexical Normalization (MultiLexNorm)
shared task at W-NUT 2021 (van der Goot et al., 2021a), which evaluates lexical …

In this paper, we introduce and demonstrate the online demo as well as the command line
interface of a lexical normalization system (MoNoise) for a variety of languages. We further …

Current benchmark tasks for natural language processing contain text that is qualitatively
different from the text used in informal day to day digital communication. This discrepancy …

Lexical normalization, the translation of non-canonical data to standard language, has
shown to improve the performance of manynatural language processing tasks on social …

Neural machine translation (MT) models achieve strong results across a variety of settings,
but it is widely believed that they are highly sensitive to" noisy" inputs, such as spelling …

Social media is notoriously difficult to process for existing natural language processing tools,
because of spelling errors, non-standard words, shortenings, non-standard capitalization …