Add xlnet-gcn as one of the benchmark in NER (#622)

Hi Sebastian,
I would like to update my work on improving NER using the combination of contextual information from XLNet and global information from GCN to boost NER performance in comparison to standalone information.
Thanks in advance.

english/named_entity_recognition.md

@@ -20,6 +20,7 @@ corpus tagged with four different entity types (PER, LOC, ORG, MISC). Models are
 | ACE + document-context (Wang et al., 2021) | 94.6 | [Automated Concatenation of Embeddings for Structured Prediction](https://arxiv.org/pdf/2010.05006.pdf) | [Official](https://github.com/Alibaba-NLP/ACE)|
 | LUKE (Yamada et al., 2020) | 94.3 | [LUKE: Deep Contextualized Entity Representations with Entity-aware Self-attention](https://www.aclweb.org/anthology/2020.emnlp-main.523/) | [Official](https://github.com/studio-ousia/luke) |
 | CL-KL (Wang et al., 2021) | 93.85 | [Improving Named Entity Recognition by External Context Retrieving and Cooperative Learning](https://arxiv.org/abs/2105.03654) | [Official](https://github.com/Alibaba-NLP/CLNER)|
+| XLNet-GCN (Tran et al., 2021) | 93.82 | [Improving Named Entity Recognition by External Context Retrieving and Cooperative Learning](https://link.springer.com/chapter/10.1007/978-3-030-91669-5_21) | [Official](https://github.com/honghanhh/ner-combining-contextual-and-global-features)|
 | InferNER (Moemmur et al., 2021) | 93.76| [InferNER: an attentive model leveraging the sentence-level information for Named Entity Recognition in Microblogs](https://journals.flvc.org/FLAIRS/article/view/128538) | |
 | ACE (Wang et al., 2021) | 93.6 | [Automated Concatenation of Embeddings for Structured Prediction](https://arxiv.org/pdf/2010.05006.pdf) | [Official](https://github.com/Alibaba-NLP/ACE)|
 | CNN Large + fine-tune (Baevski et al., 2019) | 93.5 | [Cloze-driven Pretraining of Self-attention Networks](https://arxiv.org/pdf/1903.07785.pdf) | |