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.

提出了一种新的多任务方法，通过使用 NE 分段及精细 NE 分类的主要任务和更一般的辅助任务，利用多任务神经网络构架来学习更高阶特征表示，以传统的 CRF 分类器和神经网络结合的方式来解决 Twitter 和分类不一致的问题。