Update README.md

README.md

@@ -17,6 +17,44 @@ See [Zhang et al., 2021](https://arxiv.org/abs/2112.07887) for the details.
 
 Note that some prior systems like [BioSyn](https://aclanthology.org/2020.acl-main.335.pdf), [SapBERT](https://aclanthology.org/2021.naacl-main.334.pdf), and their follow-up work (e.g., [Lai et al., 2021](https://aclanthology.org/2021.findings-emnlp.140.pdf)) claimed to do entity linking, but their systems completely ignore the context of an entity mention, and can only predict a surface form in the entity dictionary (See Figure 1 in [BioSyn](https://aclanthology.org/2020.acl-main.335.pdf)), _**not the canonical entity ID (e.g., CUI in UMLS)**_. Therefore, they can't disambiguate ambiguous mentions. For instance, given the entity mention "_ER_" in the sentence "*ER crowding has become a wide-spread problem*", their systems ignore the sentence context, and simply predict the closest surface form, which is just "ER". Multiple entities share this surface form as a potential name or alias, such as *Emergency Room (C0562508)*, *Estrogen Receptor Gene (C1414461)*, and *Endoplasmic Reticulum(C0014239)*. Without using the context information, their systems can't resolve such ambiguity and pinpoint the correct entity *Emergency Room (C0562508)*. More problematically, their evaluation would deem such an ambiguous prediction as correct. Consequently, the reported results in their papers do not reflect true performance on entity linking.
 
+
+## Usage of KRISSBERT for Entity Linking
+
+Here, we use the [MedMentions](https://github.com/chanzuckerberg/MedMentions) data to show you how to 1) **generate prototype embeddings**, and 2) **run entity linking**.
+
+(We are currently unable to release the self-supervised mention examples, because they require the UMLS and PubMed licenses.)
+
+
+#### 1. Create conda environment and install requirements
+```bash
+conda create -n kriss -y python=3.8 && conda activate kriss
+pip install -r requirements.txt
+```
+
+#### 2. Switch the root dir to [usage](https://huggingface.co/microsoft/BiomedNLP-KRISSBERT-PubMed-UMLS-EL/tree/main/usage)
+```bash
+cd usage
+```
+
+#### 3. Download the MedMentions dataset
+
+```bash
+git clone https://github.com/chanzuckerberg/MedMentions.git
+```
+
+#### 4. Generate prototype embeddings
+```bash
+python generate_prototypes.py
+```
+
+#### 5. Run entity linking
+```bash
+python run_entity_linking.py
+```
+
+This will give you about `58.3%` top-1 accuracy.
+
+
 ## Citation
 
 If you find KRISSBERT useful in your research, please cite the following paper:
@@ -30,4 +68,5 @@ If you find KRISSBERT useful in your research, please cite the following paper:
   eprinttype = {arXiv},
   eprint = {2112.07887},
 }
-```
+```
+[https://arxiv.org/pdf/2112.07887.pdf](https://arxiv.org/pdf/2112.07887.pdf)