This fancy-sounding term basically means using code to create human-like language, like what you might see on a website or in an email.
So how does it work? Well, let me break it down for ya:
1. First, we need some data this could be anything from news articles to product descriptions. We’ll use the NLTK library (which stands for Natural Language Toolkit) to preprocess and clean up our text so that it’s ready for analysis.
2. Next, we’ll train a machine learning model using techniques like recurrent neural networks or transformer models. These algorithms can learn patterns in language and generate new sentences based on what they’ve learned from the data.
3. Once our model is trained, we can use it to generate new text! This could be anything from summarizing a news article to writing product descriptions for an e-commerce site. The possibilities are endless!
Here’s some code to get you started:
# Import necessary libraries
import nltk # Import Natural Language Toolkit library
from nltk.corpus import stopwords # Import stopwords from NLTK corpus
from nltk.stem.porter import PorterStemmer # Import PorterStemmer from NLTK stemmer
from sklearn.model_selection import train_test_split # Import train_test_split function from sklearn model_selection
from keras.models import Sequential # Import Sequential model from Keras
from keras.layers import Dense, Embedding, LSTM # Import Dense, Embedding, and LSTM layers from Keras
import pandas as pd # Import pandas library for data manipulation
# Load data and preprocess text
df = pd.read_csv('data/train.csv') # Read csv file and store it in a dataframe called 'df'
texts = df['description'].values # Extract the 'description' column from the dataframe and store it in a variable called 'texts'
stop_words = set(stopwords.words('english')) # Create a set of stopwords from the English language
stemmer = PorterStemmer() # Create an instance of PorterStemmer
def clean_text(doc):
doc = re.sub(r'[^A-Za-z0-9\s]', '', doc) # Remove punctuation and special characters using regular expressions
words = nltk.word_tokenize(doc) # Tokenize the text into individual words
filtered_words = [w for w in words if not w in stop_words] # Remove stopwords from the text
stemmed_words = [stemmer.stem(w) for w in filtered_words] # Stem each word in the text to reduce word length and improve model performance
return ' '.join(stemmed_words) # Join the stemmed words back into a single string
texts = list(map(clean_text, texts)) # Apply the clean_text function to each text in the 'texts' list and store the results in a new list called 'texts'
# Split data into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(texts, df['category'], test_size=0.2, random_state=42) # Split the data into training and testing sets, with 20% of the data used for testing and a random state of 42 for reproducibility
# Build model using Keras
model = Sequential() # Create a sequential model
model.add(Embedding(input_dim=len(set(' '.join(X_train).split())), output_dim=16)) # Add an embedding layer to convert text into numerical vectors for input to the LSTM layer. The input dimension is the length of the vocabulary (unique words) in the training data, and the output dimension is 16.
model.add(LSTM(units=32, return_sequences=True)) # Add an LSTM layer with 32 units and return the last output of each time step (for generating new sentences)
model.add(Dense(16, activation='relu')) # Add a dense layer with 16 neurons and ReLU activation function
model.add(Dense(len(set(' '.join(X_train).split())), activation='sigmoid')) # Add an output layer with sigmoid activation function to generate probabilities for each word in the vocabulary (instead of a single output)
model.compile(loss='categorical_crossentropy', optimizer='adam') # Compile the model with a categorical crossentropy loss function and the Adam optimizer
# Train model on training data and evaluate performance on testing data
history = model.fit(X_train, y_train, epochs=10, batch_size=32, validation_data=(X_test, y_test)) # Train the model on the training data for 10 epochs with a batch size of 32, and evaluate its performance on the testing data. The history variable stores the training and validation loss for each epoch.
And that’s it! With this code, you can generate new text based on the patterns learned from your training data. The possibilities are endless whether you want to write product descriptions for an e-commerce site or summarize news articles for a busy executive, NLG in Python has got you covered!