hmmEntityModel

HMM-based model for named entity recognition (NER)

Since R2023a

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. If you want to train a custom model that predicts different tags, or train a model using your own data, then you can use the trainHMMEntityModel function.

Creation

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

Properties

Entities — Named entities

categorical array

Named entities, specified as categorical array.

Data Types: categorical

Object Functions

predict

Predict entities using named entity recognition (NER) model

Examples

Train HMM-based NER Model

Read the example entity data from exampleEntities.csv 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 that correspond to the token 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: [3×1 categorical]

View the entities of the model.

mdl.Entities

ans = 3×1 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