Named Entity Recognition (NER)

Named Entity means anything that is a real-world object such as a person, a place, any organisation, any product which has a name. For example – “My name is Aman, and I and a Machine Learning Trainer”. In this sentence the name “Aman”, the field or subject “Machine Learning” and the profession “Trainer” are named entities.

In Machine Learning Named Entity Recognition (NER) is a task of Natural Language Processing to identify the named entities in a certain piece of text.

Have you ever used software known as Grammarly? It identifies all the incorrect spellings and punctuations in the text and corrects it. But it does not do anything with the named entities, as it is also using the same technique. In this article, I will take you through the task of Named Entity Recognition (NER) with Machine Learning.

Loading the Data for Named Entity Recognition (NER)

The dataset, that I will use for this task can be easily downloaded from here. Now the first thing I will fo is to load the data and have a look at it to know what I am working with. So let’s simply import the pandas library and load the data:

from google.colab import files uploaded = files.upload() import pandas as pd data = pd.read_csv('ner_dataset.csv', encoding= 'unicode_escape') data.head()
Code language: Python (python)
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In the data, we can see that the words are broken into columns which will represent our feature X, and the Tag column in the right will represent our label Y.

Data Preparation for Neural Networks

I will train a Neural Network for the task of Named Entity Recognition (NER). So we need to do some modifications in the data to prepare it in such a manner so that it can easily fit into a neutral network. I will start this step by extracting the mappings that are required to train the neural network:

from itertools import chain def get_dict_map(data, token_or_tag): tok2idx = {} idx2tok = {} if token_or_tag == 'token': vocab = list(set(data['Word'].to_list())) else: vocab = list(set(data['Tag'].to_list())) idx2tok = {idx:tok for idx, tok in enumerate(vocab)} tok2idx = {tok:idx for idx, tok in enumerate(vocab)} return tok2idx, idx2tok token2idx, idx2token = get_dict_map(data, 'token') tag2idx, idx2tag = get_dict_map(data, 'tag')
Code language: Python (python)

Now I will transform the columns in the data to extract the sequential data for our neural network:

data['Word_idx'] = data['Word'].map(token2idx) data['Tag_idx'] = data['Tag'].map(tag2idx) data_fillna = data.fillna(method='ffill', axis=0) # Groupby and collect columns data_group = data_fillna.groupby( ['Sentence #'],as_index=False )['Word', 'POS', 'Tag', 'Word_idx', 'Tag_idx'].agg(lambda x: list(x))
Code language: Python (python)

Now I will split the data into training and test sets. I will create a function for splitting the data because the LSTM layers accept sequences of the same length only. So every sentence that appears as integer in the data must be padded with the same length:

from sklearn.model_selection import train_test_split from keras.preprocessing.sequence import pad_sequences from keras.utils import to_categorical def get_pad_train_test_val(data_group, data): #get max token and tag length n_token = len(list(set(data['Word'].to_list()))) n_tag = len(list(set(data['Tag'].to_list()))) #Pad tokens (X var) tokens = data_group['Word_idx'].tolist() maxlen = max([len(s) for s in tokens]) pad_tokens = pad_sequences(tokens, maxlen=maxlen, dtype='int32', padding='post', value= n_token - 1) #Pad Tags (y var) and convert it into one hot encoding tags = data_group['Tag_idx'].tolist() pad_tags = pad_sequences(tags, maxlen=maxlen, dtype='int32', padding='post', value= tag2idx["O"]) n_tags = len(tag2idx) pad_tags = [to_categorical(i, num_classes=n_tags) for i in pad_tags] #Split train, test and validation set tokens_, test_tokens, tags_, test_tags = train_test_split(pad_tokens, pad_tags, test_size=0.1, train_size=0.9, random_state=2020) train_tokens, val_tokens, train_tags, val_tags = train_test_split(tokens_,tags_,test_size = 0.25,train_size =0.75, random_state=2020) print( 'train_tokens length:', len(train_tokens), '\ntrain_tokens length:', len(train_tokens), '\ntest_tokens length:', len(test_tokens), '\ntest_tags:', len(test_tags), '\nval_tokens:', len(val_tokens), '\nval_tags:', len(val_tags), ) return train_tokens, val_tokens, test_tokens, train_tags, val_tags, test_tags train_tokens, val_tokens, test_tokens, train_tags, val_tags, test_tags = get_pad_train_test_val(data_group, data)
Code language: Python (python)

train_tokens length: 32372
train_tokens length: 32372
test_tokens length: 4796
test_tags: 4796
val_tokens: 10791 val_tags: 10791

Training Neural Network for Named Entity Recognition (NER)

Now, I will proceed with training the neural network architecture of our model. So let’s start with importing all the packages we need for training our neural network:

import numpy as np import tensorflow from tensorflow.keras import Sequential, Model, Input from tensorflow.keras.layers import LSTM, Embedding, Dense, TimeDistributed, Dropout, Bidirectional from tensorflow.keras.utils import plot_model from numpy.random import seed seed(1) tensorflow.random.set_seed(2)
Code language: Python (python)

The layer below will take the dimensions from the LSTM layer and will give the maximum length and maximum tags as an output:

input_dim = len(list(set(data['Word'].to_list())))+1 output_dim = 64 input_length = max([len(s) for s in data_group['Word_idx'].tolist()]) n_tags = len(tag2idx)
Code language: Python (python)

Now I will create a helper function which will help us in giving the summary of every layer of the neural network model for Named Entity Recognition (NER):

def get_bilstm_lstm_model(): model = Sequential() # Add Embedding layer model.add(Embedding(input_dim=input_dim, output_dim=output_dim, input_length=input_length)) # Add bidirectional LSTM model.add(Bidirectional(LSTM(units=output_dim, return_sequences=True, dropout=0.2, recurrent_dropout=0.2), merge_mode = 'concat')) # Add LSTM model.add(LSTM(units=output_dim, return_sequences=True, dropout=0.5, recurrent_dropout=0.5)) # Add timeDistributed Layer model.add(TimeDistributed(Dense(n_tags, activation="relu"))) #Optimiser # adam = k.optimizers.Adam(lr=0.0005, beta_1=0.9, beta_2=0.999) # Compile model model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy']) model.summary() return model
Code language: Python (python)

Now I will create a helper function to train the Named Entity Recognition model:

def train_model(X, y, model): loss = list() for i in range(25): # fit model for one epoch on this sequence hist =, y, batch_size=1000, verbose=1, epochs=1, validation_split=0.2) loss.append(hist.history['loss'][0]) return loss
Code language: Python (python)

Driver code:

results = pd.DataFrame() model_bilstm_lstm = get_bilstm_lstm_model() plot_model(model_bilstm_lstm) results['with_add_lstm'] = train_model(train_tokens, np.array(train_tags), model_bilstm_lstm)
Code language: Python (python)

The model will give the final output after running for 25 epochs. So it will take some time to run.

Testing the Named Entity Recognition (NER) Model:

Now let’s test our model on a piece of text:

import spacy from spacy import displacy nlp = spacy.load('en_core_web_sm') text = nlp('Hi, My name is Aman Kharwal \n I am from India \n I want to work with Google \n Steve Jobs is My Inspiration') displacy.render(text, style = 'ent', jupyter=True)
Code language: Python (python)
Named Entity Recognition

So we can see a very good result from our model. I hope you liked this article on Named Entity Recognition (NER) with Machine Learning. Feel free to ask your valuable questions in the comments section below. You can also follow me on Medium to learn every topic of Machine Learning.

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Aman Kharwal

I am a programmer from India, and I am here to guide you with Data Science, Machine Learning, Python, and C++ for free. I hope you will learn a lot in your journey towards Coding, Machine Learning and Artificial Intelligence with me.

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