Recognizing named entities in a document is a key task in many NLP
applications. Although current state-of-the-art approaches to this task reach a
high performance on clean text (e.g. newswire genres), those algorithms
dramatically degrade when they are moved to noisy environments such as social
media domains. We present two systems that address the challenges of processing
social media data using character-level phonetics and phonology, word
embeddings, and Part-of-Speech tags as features. The first model is a multitask
end-to-end Bidirectional Long Short-Term Memory (BLSTM)-Conditional Random
Field (CRF) network whose output layer contains two CRF classifiers. The second
model uses a multitask BLSTM network as feature extractor that transfers the
learning to a CRF classifier for the final prediction. Our systems outperform
the current F1 scores of the state of the art on the Workshop on Noisy
User-generated Text 2017 dataset by 2.45% and 3.69%, establishing a more
suitable approach for social media environments.

本文提出两种利用字符级音韵、词嵌入和词性标注等特征解决社交媒体文本处理困难的模型，比现有技术在噪声环境下的表现更好， F1 得分比之前提高了 2.45% 和 3.69%。