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hmmEntityModel

HMM-based model for named entity recognition (NER)

Since R2023a

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    Description

    A hmmEntityModel object is a named entity recognition (NER) model that is based on a hidden Markov model (HMM).

    The addDependencyDetails function automatically detects person names, locations, organizations, and other named entities in text. To train a custom model that predicts different tags or train a model using your own data, use the trainHMMEntityModel function.

    Creation

    Create a trained HMM-based NER model using the trainHMMEntityModel function.

    Properties

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    Named entities, specified as categorical array.

    Data Types: categorical

    Object Functions

    predictPredict entities using named entity recognition (NER) model

    Examples

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    Open Live Script

    Read the example entity data from the exampleEntities CSV file into a table. 

    tbl = readtable("exampleEntities.csv",TextType="string");

    View the first few rows of the table. The table has two columns, Token and Entity, which correspond to the tokens and entities, respectively.

    head(tbl)
                 Token                 Entity   
    ________________________    ____________

    "Analyze"                   "non-entity"
    "text"                      "non-entity"
    "in"                        "non-entity"
    "MATLAB"                    "product"   
    "using"                     "non-entity"
    "Text Analytics Toolbox"    "product"   
    "."                         "non-entity"
    "Engineers"                 "non-entity"

    Train an HMM-based NER model using the trainHMMEntityModel function.

    mdl = trainHMMEntityModel(tbl)
    mdl = 
  hmmEntityModel with properties:

    Entities: [3x1 categorical]

    View the entities of the model.

    mdl.Entities
    ans = 3x1 categorical
     organization 
     product 
     non-entity

    To add entity details to documents using the trained hmmEntityModel object, use the addEntityDetails function and set the Model option to the trained NER model.

    Create a tokenized document containing text data.

    str = "MathWorks develops MATLAB and Simulink.";
document = tokenizedDocument(str);

    Add entity details using the trained hmmEntityModel object and view the updated token details using the tokenDetails function. The Entity column contains the predicted entities.

    document = addEntityDetails(document,Model=mdl);
details = tokenDetails(document)
    details=6×8 table
       Token       DocumentNumber    SentenceNumber    LineNumber       Type        Language      PartOfSpeech          Entity   
    ___________    ______________    ______________    __________    ___________    ________    _________________    ____________

    "MathWorks"          1                 1               1         letters           en       proper-noun          organization
    "develops"           1                 1               1         letters           en       verb                 non-entity  
    "MATLAB"             1                 1               1         letters           en       proper-noun          product     
    "and"                1                 1               1         letters           en       coord-conjunction    non-entity  
    "Simulink"           1                 1               1         letters           en       proper-noun          product     
    "."                  1                 1               1         punctuation       en       punctuation          non-entity

    Extract the tokens that are named entities.

    idx = details.Entity ~= "non-entity";
details(idx,["Token" "Entity"])
    ans=3×2 table
       Token          Entity   
    ___________    ____________

    "MathWorks"    organization
    "MATLAB"       product     
    "Simulink"     product

    Version History

    Introduced in R2023a

    See Also

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