Papers by Suzan Uskudarli

1 papers
Improving Named Entity Recognition by Jointly Learning to Disambiguate Morphological Tags (C18-1)

Copied to clipboard

Challenge: Named entity recognition (NER) taggers require external morphological disambiguation tools to function which are hard to obtain or non-existent for many languages.
Approach: They propose a model which jointly learns NER and MD taggers in languages for which one can provide a list of candidate morphological analyses.
Outcome: The proposed model can be trained independently of the morphological annotation schemes, and it performs competitively.

What is GenGO?

GenGO is an NLP powered publication search system. It currenctly indexes 30k+ papers from ACL Anthology, and implements multi-aspect summarization, semantic search, and more!

Information

About
Limitations