Building Language Models in NLP - Key to Predictive Text and AI Applications

Learn how building language models in Natural Language Processing (NLP) powers predictive text, machine translation, speech recognition, and text generation. Explore the fundamental techniques behind creating computational models that predict the next word in a sequence, essential for modern AI applications.

Building Language Models in NLP

Building language models is a fundamental task in natural language processing (NLP) that involves creating computational models capable of predicting the next word in a sequence of words. These models are essential for various NLP applications, such as machine translation, speech recognition, and text generation.

In this article, we will build a language model using NLP with LSTM.

What is a Language Model?

A language model is a statistical model used to predict the probability of a sequence of words. It learns the structure and patterns of a language from a given text corpus and can generate new text similar to the original text. Language models are fundamental in many NLP tasks, such as machine translation, speech recognition, and text generation.

Steps to Build a Language Model in NLP

Here, we will implement these steps to build a language model in NLP.

Step 1: Importing Necessary Libraries

We will first import all the necessary libraries required for building our model.

import tensorflow as tf
from tensorflow.keras.layers import Embedding, LSTM, Dense
from tensorflow.keras.models import Sequential

Step 2: Generate Sample Data

We will first take a sample text data.

text_data = "Hi, how is everything? I am good, thank you!"

Step 3: Preprocessing the Data

The preprocessing involves tokenizing the input text data, creating input sequences, and padding the sequences to make them equal in length.

tokenizer = tf.keras.preprocessing.text.Tokenizer()
tokenizer.fit_on_texts([text_data])
total_words = len(tokenizer.word_index) + 1

input_sequences = []
for line in text_data.split('.'):
    token_list = tokenizer.texts_to_sequences([line])[0]
    for i in range(1, len(token_list)):
        n_gram_sequence = token_list[:i+1]
        input_sequences.append(n_gram_sequence)

max_sequence_len = max([len(x) for x in input_sequences])
input_sequences = tf.keras.preprocessing.sequence.pad_sequences(input_sequences, maxlen=max_sequence_len, padding='pre')

Step 4: One-hot Encoding

The input sequences are split into predictors (xs) and labels (ys). The labels are converted to one-hot encoding.

xs, labels = input_sequences[:,:-1], input_sequences[:,-1]
ys = tf.keras.utils.to_categorical(labels, num_classes=total_words)

Step 5: Defining and Compiling the Model

This step defines and compiles a simple LSTM-based language model using Keras.

model = Sequential()
model.add(Embedding(total_words, 64, input_length=max_sequence_len-1))
model.add(LSTM(100))
model.add(Dense(total_words, activation='softmax'))

model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])

history = model.fit(xs, ys, epochs=100, verbose=1)

Step 6: Generating Text

The generate_text function takes a seed text as input and generates next_words number of words using the provided model and max_sequence_len.

def generate_text(seed_text, next_words, model, max_sequence_len):
    for _ in range(next_words):
        token_list = tokenizer.texts_to_sequences([seed_text])[0]
        token_list = tf.keras.preprocessing.sequence.pad_sequences([token_list], maxlen=max_sequence_len-1, padding='pre')
        predicted_probs = model.predict(token_list, verbose=0)[0]
        predicted_index = tf.argmax(predicted_probs, axis=-1).numpy()
        output_word = ""
        for word, index in tokenizer.word_index.items():
            if index == predicted_index:
                output_word = word
                break
        seed_text += " " + output_word
    return seed_text

# Generate text
print(generate_text("how", 5, model, max_sequence_len))

how is everything thank you

Summary

Constructing language models for NLP includes various stages, such as tokenization, sequence creation, model construction, training, and text generation. Tokenization converts text into numerical representations, while sequence creation generates input-output pairs for model training. The model typically includes layers like Embedding and LSTM, followed by a Dense layer for predictions. After training, text generation uses the model to generate new text based on a seed input. Language models are crucial for NLP tasks, such as text generation, machine translation, and sentiment analysis.