A Multi-task Approach for Named Entity Recognition in Social Media Data
Gustavo Aguilar, Suraj Maharjan, Adrián Pastor López-Monroy, Thamar Solorio · 2017
Named Entity Recognition for social media data is challenging because of its inherent noisiness.In addition to improper grammatical structures, it contains spelling inconsistencies and numerous informal abbreviations.We propose a novel multi-task approach by employing a more general secondary task of Named Entity (NE) segmentation together with the primary task of fine-grained NE categorization.The multi-task neural network architecture learns higher order feature representations from word and character sequences along with basic Part-of-Speech tags and gazetteer information.This neural network acts as a feature extractor to feed a Conditional Random Fields classifier.We were able to obtain the first position in the 3rd Workshop on Noisy User-generated Text (WNUT-2017) with a 41.86% entity F1-score and a 40.24% surface F1-score.