Fatma Mallek

Fixing paper assignments

  1. Please select all papers that belong to the same person.
  2. Indicate below which author they should be assigned to.
Provide a valid ORCID iD here. This will be used to match future papers to this author.
Provide the name of the school or the university where the author has received or will receive their highest degree (e.g., Ph.D. institution for researchers, or current affiliation for students). This will be used to form the new author page ID, if needed.

TODO: "submit" and "cancel" buttons here

2016

pdf bib
UQAM-NTL: Named entity recognition in Twitter messages
Ngoc Tan Le | Fatma Mallek | Fatiha Sadat
Proceedings of the 2nd Workshop on Noisy User-generated Text (WNUT)

This paper describes our system used in the 2nd Workshop on Noisy User-generated Text (WNUT) shared task for Named Entity Recognition (NER) in Twitter, in conjunction with Coling 2016. Our system is based on supervised machine learning by applying Conditional Random Fields (CRF) to train two classifiers for two evaluations. The first evaluation aims at predicting the 10 fine-grained types of named entities; while the second evaluation aims at predicting no type of named entities. The experimental results show that our method has significantly improved Twitter NER performance.

2014

pdf bib
Collaboratively Constructed Linguistic Resources for Language Variants and their Exploitation in NLP Application – the case of Tunisian Arabic and the Social Media
Fatiha Sadat | Fatma Mallek | Mohamed Boudabous | Rahma Sellami | Atefeh Farzindar
Proceedings of Workshop on Lexical and Grammatical Resources for Language Processing

Search
Co-authors
Venues
Fix data