Important

NeMo 2.0 is an experimental feature and currently released in the dev container only: nvcr.io/nvidia/nemo:dev. Please refer to the Migration Guide for information on getting started.

Entity Linking

Entity linking is the process of matching concepts mentioned in natural language to their unique IDs and canonical forms stored in a knowledge base. For example, an entity linking model might match the phrase blood thinners mentioned in conversation to the knowledge base concept UID45623 anticoagulant. Entity linking applications range from helping automate ingestion of large amounts of data to assisting in real time concept normalization.

Within NeMo we use the entity linking approach described in Liu et. al’s NAACL 2021 “Self-alignment Pre-training for Biomedical Entity Representations” [nlp-entity_linking1]. The main idea behind this approach is to reshape an initial concept embedding space such that synonyms of the same concept are pulled closer together and unrelated concepts are pushed further apart. The concept embeddings from this reshaped space can then be used to build a knowledge base embedding index.

Our BERT-base + Self Alignment Pretraining implementation allows you to train an entity linking encoder. We also provide example code on building an index with Medical UMLS concepts NeMo/examples/nlp/entity_linking/build_index.py.

Please try the example Entity Linking model in a Jupyter notebook (can run on Google’s Colab).

Connect to an instance with a GPU (Runtime -> Change runtime type -> select GPU for the hardware accelerator).

An example script on how to train the model can be found here: NeMo/examples/nlp/entity_linking.

References

nlp-entity_linking1

Fangyu Liu, Ehsan Shareghi, Zaiqiao Meng, Marco Basaldella, and Nigel Collier. Self-alignment pretraining for biomedical entity representations. 2021. arXiv:2010.11784.