To develop an LSTM-based model for recognizing the named entities in the text.
Named-entity recognition (NER) (also known as entity identification, entity chunking and entity extraction) is a subtask of information extraction that seeks to locate and classify named entities mentioned in unstructured text into pre-defined categories such as person names, organisations, locations, medical codes, time expressions, quantities, monetary values, percentages, etc. It adds a wealth of semantic knowledge to your content and helps you to promptly understand the subject of any given text.
Load the NER dataset.
:Prepare the dataset for training.
Create and train the model.
Predict the output for the test data and compare it with the actual value.
Name : M.Gunasekhar
Reg no : 212221240014
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
from tensorflow.keras.preprocessing import sequence
from sklearn.model_selection import train_test_split
from keras import layers
from keras.models import Model
data = pd.read_csv("ner_dataset.csv", encoding="latin1")
data.head(50)
data = data.fillna(method="ffill")
data.head(50)
print("Unique words in corpus:", data['Word'].nunique())
print("Unique tags in corpus:", data['Tag'].nunique())
words=list(data['Word'].unique())
words.append("ENDPAD")
tags=list(data['Tag'].unique())
print("Unique tags are:", tags)
num_words = len(words)
num_tags = len(tags)
num_words
class SentenceGetter(object):
def __init__(self, data):
self.n_sent = 1
self.data = data
self.empty = False
agg_func = lambda s: [(w, p, t) for w, p, t in zip(s["Word"].values.tolist(),
s["POS"].values.tolist(),
s["Tag"].values.tolist())]
self.grouped = self.data.groupby("Sentence #").apply(agg_func)
self.sentences = [s for s in self.grouped]
def get_next(self):
try:
s = self.grouped["Sentence: {}".format(self.n_sent)]
self.n_sent += 1
return s
except:
return None
getter = SentenceGetter(data)
sentences = getter.sentences
len(sentences)
sentences[0]
word2idx = {w: i + 1 for i, w in enumerate(words)}
tag2idx = {t: i for i, t in enumerate(tags)}
word2idx
plt.hist([len(s) for s in sentences], bins=50)
plt.show()
X1 = [[word2idx[w[0]] for w in s] for s in sentences]
type(X1[0])
X1[0]
max_len = 50
nums = [[1], [2, 3], [4, 5, 6]]
sequence.pad_sequences(nums)
nums = [[1], [2, 3], [4, 5, 6]]
sequence.pad_sequences(nums,maxlen=2)
X = sequence.pad_sequences(maxlen=max_len,
sequences=X1, padding="post",
value=num_words-1)
X[0]
y1 = [[tag2idx[w[2]] for w in s] for s in sentences]
y = sequence.pad_sequences(maxlen=max_len,
sequences=y1,
padding="post",
value=tag2idx["O"])
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=1)
X_train[0]
y_train[0]
input_word = layers.Input(shape=(max_len,))
embedding_layer=layers.Embedding(input_dim=num_words,output_dim=50,input_length=max_len)(input_word)
dropout_layer=layers.SpatialDropout1D(0.1)(embedding_layer)
bidirectional_lstm=layers.Bidirectional(layers.LSTM(units=100,return_sequences=True,recurrent_dropout=0.1))(dropout_layer)
output=layers.TimeDistributed(layers.Dense(num_tags,activation="softmax"))(bidirectional_lstm)
model = Model(input_word, output)
model.summary()
model.compile(optimizer="adam",
loss="sparse_categorical_crossentropy",
metrics=["accuracy"])
history = model.fit(
x=X_train,
y=y_train,
validation_data=(X_test,y_test),
batch_size=32,
epochs=3,
)
metrics = pd.DataFrame(model.history.history)
metrics.head()
metrics[['accuracy','val_accuracy']].plot()
metrics[['loss','val_loss']].plot()
i = 20
p = model.predict(np.array([X_test[i]]))
p = np.argmax(p, axis=-1)
y_true = y_test[i]
print("{:15}{:5}\t {}\n".format("Word", "True", "Pred"))
print("-" *30)
for w, true, pred in zip(X_test[i], y_true, p[0]):
print("{:15}{}\t{}".format(words[w-1], tags[true], tags[pred]))
Successfully developed LSTM based rnn model for named-entity-recognition.