The goal of the seminar is to re-implement the paper: End-to-end Sequence Labeling via Bi-directional LSTM-CNNs-CRF
You can find the complete paper in the docs folder.
State-of-the-art sequence labeling systems traditionally require large amounts of task- specific knowledge in the form of hand- crafted features and data pre-processing. In this paper, we introduce a novel neu- tral network architecture that benefits from both word- and character-level representa- tions automatically, by using combination of bidirectional LSTM, CNN and CRF. Our system is truly end-to-end, requir- ing no feature engineering or data pre- processing, thus making it applicable to a wide range of sequence labeling tasks. We evaluate our system on two data sets for two sequence labeling tasks — Penn Treebank WSJ corpus for part-of-speech (POS) tagging and CoNLL 2003 cor- pus for named entity recognition (NER). We obtain state-of-the-art performance on both datasets — 97.55% accuracy for POS tagging and 91.21% F1 for NER.
- Python 3.6
- pip packages from the
requirements.txtfile
A Big thanks to Martin Tutek who helped with the understanding of the paper.
Also thanks to TakeLab for providing all the resources.