text: 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.
  @cite_13: Multitask learning has been applied successfully to a range of tasks, mostly morphosyntactic. However, little is known on when MTL works and whether there are data characteristics that help to determine its success. In this paper we evaluate a range of semantic sequence labeling tasks in a MTL setup. We examine different auxiliary tasks, amongst which a novel setup, and correlate their impact to data-dependent conditions. Our results show that MTL is not always effective, significant improvements are obtained only for 1 out of 5 tasks. When successful, auxiliary tasks with compact and more uniform label distributions are preferable.
  @cite_12: We analyze some of the fundamental design challenges and misconceptions that underlie the development of an efficient and robust NER system. In particular, we address issues such as the representation of text chunks, the inference approach needed to combine local NER decisions, the sources of prior knowledge and how to use them within an NER system. In the process of comparing several solutions to these challenges we reach some surprising conclusions, as well as develop an NER system that achieves 90.8 F1 score on the CoNLL-2003 NER shared task, the best reported result for this dataset.
  @cite_17: 
summary: Traditional NER systems use hand-crafted features, gazetteers and other external resources to perform well @cite_12 . obtain state-of-the-art results by relying on heavily hand-crafted features, which are expensive to develop and maintain. Recently, many studies have outperformed traditional NER systems by applying neural network architectures. For instance, use a bidirectional LSTM-CRF architecture. They obtain a state-of-the-art performance without relying on hand-crafted features. , who achieved the first place on WNUT-2016 shared task, use a BLSTM neural network to leverage orthographic features. We use a similar approach but we employ CNN and BLSTM in parallel instead of forwarding the CNN output to the BLSTM. Nevertheless, our main contribution resides on Multi-Task Learning (MTL) and a combination of POS tags and gazetteers representation to feed the network. Recently, MTL has gained significant attention. Researchers have tried to correlate the success of MTL with label entropy, regularizers, training data size, and other aspects @cite_13 @cite_17 . For instance, use a multi-task network for different NLP tasks and show that the multi-task setting improves generality among shared tasks. In this paper, we take advantage of the multi-task setting by adding a more general secondary task, NE segmentation, along with the primary NE categorization task.