The 6th Workshop on Noisy User-generated Text EMNLP - WNUT 2020

Страна: США

Город: Online

Тезисы до: 25.08.2020

Даты: 19.11.20 — 19.11.20

Область наук: Филологические;

Е-мейл Оргкомитета: https://forms.gle/HtnNezLTgkuj4DyQ8

Организаторы: EMNLP

 

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. This year, there will be a shared task on Named Entity Recognition over Wet-lab Protocols.


We seek submissions of both long and short papers on original and unpublished work (same format and page limit as EMNLP main conference). 1-page abstracts on work-in-progress or work published elsewhere are also welcome and will *not* be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. We have [best paper awards] sponsored by Twitter this year.

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
- Parsing
* Text Normalization and Error Correction
- Normalizing noisy text for downstream tasks and for human readability
- Error detection and correction
* Robustness to Noise, both Natural and Adversarial
* Multilingual NLP in noisy text
* Machine Translation of Noisy Text
* Sentiment analysis
* Crowdsourcing of text data
* User prediction, e.g. gender, age, etc
* Stylistics, e.g. formality, politeness, etc
* Colloquial language, e.g. code-switching, idiom detection
* Bilingual translation of the noisy text
* Paraphrase identification and semantic similarity of short text or noisy text
* Information extraction from noisy text
* Domain adaptation to user-generated text
* Geolocation prediction
* Global and regional trend detection and event extraction
* Detecting rumors, contradictory information, sarcasm and humor 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...)

Веб-сайт конференции: http://noisy-text.github.io/2020/