Named Entity Recognition

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Sai Kamal
Sai Kamal

Software Engineer

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Named Entity Recognition
3 min read

The goal of Named Entity Recognition is to locate and classify named entities in a sequence. The named entities are pre-defined categories chosen according to the use case such as names of people, organizations, places, codes, time notations, monetary values, etc. Essentially, NER aims to assign a class to each token (usually a single word) in a sequence. Because of this, NER is also referred to as token classification.

Model developing Process

Model developing Process

For this we are using pre-trained models from simple transformers which build over Hugging face Library.

Implemetation Process

The process of performing Named Entity Recognition in Simple Transformers does not deviate from the standard pattern.

  • Initialize a NERModel
  • Train the model with train_model()
  • Evaluate the model with eval_model()
  • Make predictions on (unlabelled) data with predict()

Supported Model Types using simple transformers

Copy Code
            
    1 .  ALBERT	       = albert
    2 .  BERT          = bert
    3 .  BERTweet      = bertweet
    4 .  BigBird       = bigbird
    5 .  CamemBERT     = camembert
    6 .  DeBERTa       = deberta
    7 .  DeBERTa       = deberta
    8 .  DeBERTaV2     = deberta-v2
    9 .  DistilBERT    = distilbert
    10 . ELECTRA       = electra
    11 . HerBERT       = herbert
    12 . LayoutLM      = layoutlm
    13 . Longformer    = longformer
    14 . MobileBERT    = mobilebert
    15 . MPNet         = mpnet
    16 . RoBERTa       = roberta
    17 . SqueezeBert   = squeezebert
    18 . XLM           = xlm
    19 . XLM-RoBERTa   = xlmroberta
    20 . XLNet         = xlnet

The above models completely uses concept encoders and decoders

Data Formats

Data Formats

A DataFrame containing the 3 columns sentence_id, words, labels. Each value in words will have a corresponding labels value. The sentence_id determines which words belong to a given sentence. I.e. the words from the same sequence should be assigned the same unique sentence_id.

Named entity recognition depends on the lables . Model can develop in different lable format here we are using an couple of lablels ['O', 'B-geo', 'B-gpe', 'B-per', 'I-geo', 'B-org', 'I-org', 'B-tim', 'B-art', 'I-art', 'I-per', 'I-gpe', 'I-tim', 'B-nat', 'B-eve', 'I-eve', 'I-nat']

Explanation of the labels

  • O = Outside of a named entity
  • B-MIS = Beginning of a miscellaneous entity right after another miscellaneous entity
  • I-MIS = Miscellaneous entity
  • B-PER = Beginning of a person's name right after another person's name
  • I-PER = Person's name
  • B-ORG = Beginning of an organisation right after another organisation
  • I-ORG = Organisation
  • B-LOC = Beginning of a location right after another location
  • I-LOC = Location

Model used for Named entity recognition

Bert-Based-cased

  • Arguments.num_train_epochs = 3
  • Arguments.train_batch_size = 32
  • Arguments.eval_batch_size = 32
  • Arguments.learning_rate = 4e-5
  • Arguments.max_seq_length = 128
  • Arguments.adam_epsilon = 1e-8
  • Arguments.do_lower_case = True
  • Arguments.n_gpu = 1
  • Arguments.overwrite_output_dir = True

Follow technical report.docx file for complete explanation about models used and for dataset.

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