The Flair NLP Framework

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My group maintains and develops Flair, an open source framework for state-of-the-art NLP. Flair is an official part of the PyTorch ecosystem and to-date is used in hundreds of industrial and academic projects. Together with the open source community and Zalando Resarch, my group is are actively developing Flair - and invite you to join us!

Research behind Flair

My current research proposes a new approach to address core natural language processing tasks such as part-of-speech (PoS) tagging, named entity recognition (NER), sense disambiguation and text classification. Our approach leverages character-level neural language modeling to learn powerful, contextualized representations of human language from large corpora. The Figure below illustrates how it works:

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Here, a sentence (bottom) is input as a character sequence into a bidirectional character language model (LM, yellow in Figure) that was pre-trained on extremely large unlabeled text corpora. From this LM, we retrieve for each word a contextual embedding by extracting the first and last character cell states. This word embedding is then passed into a vanilla BiLSTM-CRF sequence labeler (blue in Figure), achieving robust state-of-the-art results on downstream tasks (NER in this example).

This simple approach works incredibly well. In fact, it outperforms all previous approaches by a significant margin across many classic NLP tasks. Check out some results below:

Task Dataset Our Result Previous best
Named Entity Recognition (English) Conll-03 93.09 (F1) 92.22 (Peters et al., 2018)
Named Entity Recognition (English) Ontonotes 89.71 (F1) 86.28 (Chiu et al., 2016)
Emerging Entity Detection (English) WNUT-17 50.20 (F1) 45.55 (Aguilar et al., 2018)
Named Entity Recognition (German) Conll-03 88.32 (F1) 78.76 (Lample et al., 2016)
Named Entity Recognition (German) Germeval 84.65 (F1) 79.08 (Hänig et al, 2014)
Part-of-Speech tagging (English) WSJ 97.85 97.64 (Choi, 2016)
Chunking (English) Conll-2000 96.72 (F1) 96.36 (Peters et al., 2017)

Check out the corresponsing publication for more details:

Contextual String Embeddings for Sequence Labeling. Alan Akbik, Duncan Blythe and Roland Vollgraf. 27th International Conference on Computational Linguistics, COLING 2018. [pdf]

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Alan Akbik

Professor of Machine Learning
Humbold-Universität zu Berlin
alan [dot] akbik [ät] hu-berlin [dot] de