Chemlistem - chemical named entity recognition using recurrent neural networks

Chemical named entity recognition has traditionally been dominated by CRF (Conditional Random Fields)-based approaches but given the success of WKH DUWLILFLDO QHXUDO QHWZRUN WHFKQLTXHV NQRZQ DV 3GHHS OHDUQLQJ ́ Ze decided to examine them as an alternative to CRFs. We present here three systems. The first system translates the traditional CRF-based idioms into a deep learning framework, using rich per-token features and neural word embeddings, and producing a sequence of tags using bidirectional Long Short Term Memory (LSTM) networks ± a type of recurrent neural net. The second system eschews the rich feature set ± and even tokenisation ± in favour of character labelling using neural character embeddings and multiple LSTM layers. The third system is an ensemble that combines the results of the first two systems, achieving an F score of 0.9032 on the test data (precision 0.9002, recall 0.9062).