2018 The 4th Workshop on Noisy User-generated Text (W-NUT)
Oct 31 or Nov 1, 2018, Brussels, Belgium (at EMNLP 2018)
The WNUT workshop focuses on Natural Language Processing applied to noisy user-generated text, such as that found in social media, online reviews, crowdsourced data, web forums, clinical records and language learner essays. The workshop hashtag is #wnut.
We again have best paper award(s) sponsored by Microsoft Research this year.
Submission deadline: July 20, 2018 (research papers / one-page abstracts)
We seek submissions of regular papers on original and unpublished work (same page limit EMNLP main conference). 1-page abstracts on work-in-progress or work published elsewhere are also welcome and will *not* be included in the conference proceedings.
All accepted submissions will be presented as posters. Additionally, selected submissions will be presented orally.
Topics of interest include but are not limited to:
NLP Preprocessing of Noisy Text
Part of speech tagging
Named entity tagging, including a wide range of categories, e.g. product names
Chunking of user-generated text
Text Normalization and Error Correction
Normalizing noisy text for downstream tasks and for human readability
Error detection and correction
Multilingual NLP in noisy text
Crowdsourcing of text data
User prediction, e.g. geolocation, gender, age, etc
Stylistics, e.g. formality, politeness, etc
Colloquial language, e.g. code-switching, idiom detection
Bilingual translation of noisy text
Paraphrase identification and semantic similarity of short text or noisy text
Information extraction from noisy text
Domain adaptation to user-generated text
Global and regional trend detection and event extraction
Detecting rumors, contradictory information, sarcasms and humors on social media
Extracting user demographics, profiles and major life events
Temporal aspects of user-generated content (resolving time expressions, concept drift, diachronic analyses, etc...)
All submissions should conform to EMNLP 2018 style guidelines. Long and short paper submissions must be anonymized. Abstract submissions should include author information (and where the work was published in a footnote on front page, if applicable). Please submit your papers at the softconf link.