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Found 2 papers, 1 papers with code
Improving Chemical Named Entity Recognition in Patents with Contextualized Word Embeddings
In this paper, we explore the NER performance of a BiLSTM-CRF model utilising pre-trained word embeddings, character-level word representations and contextualized ELMo word representations for chemical patents.
Tagging Funding Agencies and Grants in Scientific Articles using Sequential Learning Models
In this paper we present a solution for tagging funding bodies and grants in scientific articles using a combination of trained sequential learning models, namely conditional random fields (CRF), hidden markov models (HMM) and maximum entropy models (MaxEnt), on a benchmark set created in-house.
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