Guide
Analyze

Generate Concepts Risk Scores

Generate Concepts Risk Scores

Using the Named Entity Recognition (NER)

Steps

workertasks
quality-scoreFilters out document not reaching a predefined threshold.
ner-linkingFilters out document not mentioning any of the companies defined in the request.
conceptAnnotates the occurrences of concepts keywords within the sentences.

Some workers are restricted to certain languages, see the Language Support page for more informations

Query

POST /analyze/dataset

payload.json
{
  "workers": ["quality-score", "ner-linking", "concept", "entity-similarity"],
  "start_date": "2019-01-31",
  "qscore": 50,
  "entities": [
    {
      "context": "Apple is a multinational corporation that designs, manufactures, and markets consumer electronics, personal computers, and software.",
      "entity_of_interest": "Q312",
      "keywords": ["Tim Cook", "Apple TV"]
    },
    {
      "context": "Boeing Company manufactures and sells aircraft, rotorcraft, rockets, and satellites and provides product leasing and support services.",
      "entity_of_interest": "Q66",
      "keywords": ["Boeing", "Alteon Training"]
    }
  ],
  "end_date": "2019-02-01",
  "concepts": {
    "environment": ["environmental impact", "environmental controversy", "pesticide"],
    "governance": ["offshore transaction", "dupery", "humbug"],
    "pollution": ["fuel leakage", "greenhouse gases"],
    "social": ["unscrupulous", "inequality", "malfeasance", "workplace violence"]
  }
}

Using the NER and the entity-similarity

Steps

workertasks
quality-scoreFilters out document not reaching a predefined threshold.
ner-linkingFilters out document not mentioning any of the companies defined in the request.
conceptAnnotates the occurrences of concepts keywords within the sentences.
entity-similarityCalculates the cosine similarity between the each entity description and each sentence where the entity is detected.
Filters out document not reaching a given similarity threshold.

Query

POST /analyze/dataset

payload.json
{
  "workers": ["quality-score", "ner-linking", "concept", "entity-similarity"],
  "start_date": "2019-01-31",
  "qscore": 50,
  "entities": [
    {
      "context": "Apple is a multinational corporation that designs, manufactures, and markets consumer electronics, personal computers, and software.",
      "entity_of_interest": "Q312",
      "keywords": ["Tim Cook", "Apple TV"]
    },
    {
      "context": "Boeing Company manufactures and sells aircraft, rotorcraft, rockets, and satellites and provides product leasing and support services.",
      "entity_of_interest": "Q66",
      "keywords": ["Boeing", "Alteon Training"]
    }
  ],
  "end_date": "2019-02-01",
  "concepts": {
    "environment": ["environmental impact", "environmental controversy", "pesticide"],
    "governance": ["offshore transaction", "dupery", "humbug"],
    "pollution": ["fuel leakage", "greenhouse gases"],
    "social": ["unscrupulous", "inequality", "malfeasance", "workplace violence"]
  }
}

Using the raw-matcher and the entity-similarity

Steps

workertasks
quality-scoreFilters out document not reaching a predefined threshold.
raw-matcherAnnotates the mentions of entities keywords within the sentences.
conceptAnnotates the occurrences of concepts keywords within the sentences.
entity-similarityCalculates the cosine similarity between the each entity description and each sentence where the entity is detected.
Filters out document not reaching a given similarity threshold.

Query

POST /analyze/dataset

payload.json
{
  "workers": ["quality-score", "ner-linking", "concept", "entity-similarity"],
  "start_date": "2019-01-31",
  "qscore": 50,
  "entities": [
    {
      "context": "Apple is a multinational corporation that designs, manufactures, and markets consumer electronics, personal computers, and software.",
      "entity_of_interest": "Q312",
      "keywords": ["Tim Cook", "Apple TV"]
    },
    {
      "context": "Boeing Company manufactures and sells aircraft, rotorcraft, rockets, and satellites and provides product leasing and support services.",
      "entity_of_interest": "Q66",
      "keywords": ["Boeing", "Alteon Training"]
    }
  ],
  "end_date": "2019-02-01",
  "concepts": {
    "environment": ["environmental impact", "environmental controversy", "pesticide"],
    "governance": ["offshore transaction", "dupery", "humbug"],
    "pollution": ["fuel leakage", "greenhouse gases"],
    "social": ["unscrupulous", "inequality", "malfeasance", "workplace violence"]
  }
}