Scispacy.md

ScispaCy annotator

The example API provides the ScispacyBiomedAnnotator annotator which invokes models from ScispaCy.

ScispaCy has multiple models which are tuned for finding different entities. A full list is available on the page https://allenai.github.io/scispacy/. When a model is enabled in the example API, its entities are available for the text annotations.

Note that some models might require a non-trivial amount of memory for loading and running. When enabling more models, make sure to increase the memory limits of your environment accordingly.

Querying Annotator Capabilities

You can query the capabilities of this annotator:

curl -X POST -H "Content-Type: application/json" -H  "Authorization: test 123"\
    -d '{"features": {"entity_names": true, "relationship_names": true, "property_names": true, "labels": true}}' \
    http://localhost:5000/api/v1/annotators/ScispacyBiomedAnnotator

Body in nicer format:

{
  "features": {
    "entity_names": true,
    "relationship_names": true,
    "property_names": true,
    "labels": true
  }
}

The output will show the list all the entity types of the scispacy models enabled in the annotator (see above).

Expected response:

{
  "entity_names": [
    "entity",
    "cell_line",
    "cell_type",
    "dna",
    "protein",
    "rna",
    "chemical",
    "disease"
  ],
  "relationship_names": [],
  "property_names": [],
  "supported_object_types": [
    "text",
    "table"
  ],
  "labels": {
    "entities": [
      {
        "key": "entity",
        "description": "ENTITY from en_core_sci_md model."
      },
      {
        "key": "cell_line",
        "description": "CELL_LINE from en_ner_jnlpba_md model."
      },
      {
        "key": "cell_type",
        "description": "CELL_TYPE from en_ner_jnlpba_md model."
      },
      {
        "key": "dna",
        "description": "DNA from en_ner_jnlpba_md model."
      },
      {
        "key": "protein",
        "description": "PROTEIN from en_ner_jnlpba_md model."
      },
      {
        "key": "rna",
        "description": "RNA from en_ner_jnlpba_md model."
      },
      {
        "key": "chemical",
        "description": "CHEMICAL from en_ner_bc5cdr_md model."
      },
      {
        "key": "disease",
        "description": "DISEASE from en_ner_bc5cdr_md model."
      }
    ],
    "relationships": [],
    "properties": []
  }
}

Finding Entities in Text

You can test it as in the following example:

curl -X POST "http://localhost:5000/api/v1/annotators/ScispacyBiomedAnnotator" -H  "accept: application/json" -H  "Authorization: test 123" -H  "Content-Type: application/json" -d "{\"find_entities\":{\"object_type\":\"text\",\"entity_names\":[\"disease\",\"protein\",\"chemical\"],\"texts\":[\"Spinal and bulbar muscular atrophy (SBMA) is an inherited motor neuron disease caused by the expansion of a polyglutamine tract within the androgen receptor (AR). SBMA can be caused by this easily.\",\"Consult for laparoscopic gastric bypass.\"]}}"

Here is the request body in more readable format.

• object_type is what you will pass, here text, not tables or images.
• With entity_names you can restrict which entities you want to get, if the annotator offers several types. If the list is empty, all possible entities will be returned.
• texts is a list of sentences to annotate.

{
  "find_entities": {
    "object_type": "text",
    "entity_names": [
        "disease",
        "protein",
        "chemical"
    ],
    "texts": [
      "Spinal and bulbar muscular atrophy (SBMA) is an inherited motor neuron disease caused by the expansion of a polyglutamine tract within the androgen receptor (AR). SBMA can be caused by this easily.",
      "Consult for laparoscopic gastric bypass."
    ]
  }
}

Example results:

{
  "entities": [
    {
      "disease": [
        {
          "match": "Spinal and bulbar muscular atrophy",
          "original": "Spinal and bulbar muscular atrophy",
          "range": [
            0,
            34
          ],
          "type": "disease"
        },
        {
          "match": "SBMA",
          "original": "SBMA",
          "range": [
            36,
            40
          ],
          "type": "disease"
        },
        {
          "match": "polyglutamine tract",
          "original": "polyglutamine tract",
          "range": [
            108,
            127
          ],
          "type": "disease"
        },
        {
          "match": "SBMA",
          "original": "SBMA",
          "range": [
            163,
            167
          ],
          "type": "disease"
        }
      ],
      "protein": [
        {
          "match": "androgen receptor",
          "original": "androgen receptor",
          "range": [
            139,
            156
          ],
          "type": "protein"
        },
        {
          "match": "AR",
          "original": "AR",
          "range": [
            158,
            160
          ],
          "type": "protein"
        }
      ],
      "chemical": []
    },
    {
      "disease": [],
      "protein": [],
      "chemical": []
    }
  ]
}