The Neural NER system implemented by me as part of the papers TALLIP paper and ACL 2018 Paper achieves the following F1-Scores on various languages.
Results Language Dataset Word Embeddings Reference F1 Score English CoNLL 2003 Spectral Embeddings Arxiv Paper 90.94 Spanish CoNLL 2002 Spectral Embeddings Arxiv Paper 85.75 Dutch CoNLL 2002 Spectral Embeddings Arxiv Paper 85.20 German Link Spectral Embeddings ACL 2018 Paper 87.64 Italian Evalita 2009 Spectral Embeddings ACL 2018 Paper 75.
Transfer learning approaches for Neural Machine Translation (NMT) train a NMT model on the assisting-target language pair (parent model) which is later fine-tuned for the source-target language pair of interest (child model), with the target language …
Existing supervised solutions for Named Entity Recognition (NER) typically rely on a large annotated corpus. Collecting large amounts of NER annotated corpus is time-consuming and requires considerable human effort. However, collecting small amounts …
Multilingual learning for Neural Named Entity Recognition (NNER) involves jointly training a neural network for multiple languages. Typically, the goal is improving the NER performance of one of the languages (the primary language) using the other …