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language | js |
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title | Example Configuration |
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"algorithm": "Levenshtein" "algorithm_params": {} "dictionary": "dict-provider:people-lowercase", |
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" |
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dontProcessTags": [" |
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color", " |
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currency"] |
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,
"normalizeAccents": false,
"removeChars": false,
"charsList": "_-‿⁀⁔︳︴﹍﹎﹏_"
"spellchecking": false
"cosineSimThreshold": 0.7
"lowercase": true
"matchAll": false
"matchAllThreshold": 1.0 |
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"people-lowercase" resource must be in the format |
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specified below. |
In the following example, "abraham lincoln" is in the dictionary as a person, "lincoln" as a place, and "macaroni", "cheese" and "macaroni and cheese" are all specified as foods:
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V--------------[abraham lincoln likes macaroni and cheese]--------------------V |
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^--[abraham]--V--[lincoln]--V--[likes]--V--[macaroni]--V--[and]--V--[cheese]--^ |
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^-- |
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[{place}]-- |
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^ ^--- |
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[{food}]--- |
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^ ^-- |
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[{food} |
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]--^ |
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^--------- |
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[{person}]-------- |
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^ ^---------------- |
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[{food}]------------- |
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^ |
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No vertices are created in this stage |
The dictionary tagger must have an "entity dictionary" (a string to JSON map) which is a list of JSON records, indexed by entity ID. In addition, there may also be a pattern map and a token index.
The only file that is absolutely required is the entity dictionary. It is a series of JSON records, typically indexed by entity ID.
Each JSON record represents an entity. The format
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is as follows:
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"_id" : "KGAAJGsBemSwA0nZTLXA",
"id":"Q28260",
"tag":"{city}",
"display": "Lincon"
"patterns":[
"Lincoln", "Lincoln, Nebraska", "Lincoln, NE"
],
"fields": {
"coord": [40.813639, -96.702611]
}
"confAdjust": 0.95
. . . additional fields as needed go here . . . |
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These will all be added to the interpretation graph with the SEMANTIC_TAG flag.
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Tags are hierarchical representations of the same intent. For example, {city} → {administrative-area} → {geographical-area} |
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Patterns will be tokenized and there may be multiple variations which can match.
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Currently, tokens are separated on simple white-space and punctuation, and then reduced to lowercase. |
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To improve performance especially for every large databases of entities, the entity dictionary is inverted and indexed.
This currently happens in RAM inside the DictionaryTagger stage. An off-line option for pre-inverting the dictionary will be provided in the future.
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