A Multi-task Approach for Named Entity Recognition in Social Media Data

Abstract

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.

EMNLP 2017 (W-NUT)

References in corpus (1)

• Transition-Based Dependency Parsing with Stack Long Short-Term Memory

Cited by in corpus (7)

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• Named Entity Recognition on Code-Switched Data: Overview of the CALCS 2018 Shared Task
• Pretrained language model transfer on neural named entity recognition in Indonesian conversational texts
• Linguistically Informed Relation Extraction and Neural Architectures for Nested Named Entity Recognition in BioNLP-OST 2019