Text Generation NLP – Everything You Need To Know / Python Code To Get Started

by | Dec 19, 2022 | Artificial Intelligence, Machine Learning, Natural Language Processing

What is text generation in NLP?

Text generation is a subfield of natural language processing (NLP) that deals with generating text automatically. It has a wide range of applications, including machine translation, content creation, and conversational agents.

One of the most common text generation techniques is statistical language models. These models are trained on large amounts of text data and use statistical techniques to predict the likelihood of a word or sequence of words given the context. The model can then generate text by sampling from this distribution of words and selecting the most likely words based on the context.

example text to image generation in python

Image generation from text using generative AI

Another approach to text generation is using neural network models, such as recurrent neural networks (RNNs) or transformers. These models are trained to take in a sequence of words and predict the next word in the sequence, similar to how a language model works. Neural network models can capture complex relationships between words and generate more coherent and natural-sounding text.

Popular applications of text generation in NLP

One popular application of text generation is machine translation, where the model is trained to translate text from one language to another. Another application is content creation, where the model can generate articles, summaries, or social media posts.

Conversational agents, such as chatbots or virtual assistants, also use text generation to produce responses to user inputs. These models are trained on a large dataset of conversational exchanges and can generate appropriate responses based on the context of the conversation.

Text generation has the potential to improve many aspects of our lives, from making it easier to communicate with people who speak different languages to helping businesses generate content more efficiently. However, there are also concerns about the potential for text generation models to produce biased or inaccurate content, so it is essential to carefully consider the ethical implications of these models.

Overall, text generation is a rapidly growing and important area of NLP with numerous applications. As technology advances, we can expect to see even more exciting developments in this field.

The future of generative AI

Generative AI, or artificial intelligence that can generate new content based on a set of inputs, has the potential to revolutionize a variety of fields and industries. Some potential applications of generative AI in the future include:

  1. Content creation: Generative AI could automatically generate news articles, social media posts, or even entire websites.
  2. Design and engineering: Generative AI could be used to design and optimize products, structures, or systems, potentially leading to more efficient and practical designs.
  3. Medicine: Generative AI could design and test new drugs, predict disease outbreaks, or assist with diagnosis and treatment planning.
  4. Education: Generative AI could create personalized learning experiences or generate new educational materials.
  5. Art and entertainment: Generative AI could create new music, art, movies, and television shows.

Overall, the future of generative AI is likely to be defined by its ability to augment and enhance human capabilities rather than replace them.

ChatGPT

Given the latest buzz, this article wouldn’t be complete without mentioning ChatGPT. ChatGPT is a variant of the GPT-3 (Generative Pre-trained Transformer 3) language model developed by OpenAI specifically for chatbots and other conversational agents. Like the original GPT-3 model, ChatGPT is trained on a large text dataset and can generate coherent and natural-sounding text in various languages.

One of the key features of ChatGPT is its ability to understand and respond to context. When generating text, ChatGPT takes into account the context of the conversation and can generate appropriate responses based on the previous exchanges. This makes it well-suited for use in chatbots and other conversational agents, where the ability to understand and respond to context is critical.

Another critical feature of ChatGPT is its ability to learn and adapt over time. As the chatbot or conversational agent using ChatGPT interacts with users, it can learn from the conversations and improve its responses. This makes ChatGPT a powerful tool for creating chatbots and conversational agents that can learn and adapt to their users.

Text generation in Python

There are several libraries and frameworks available for text generation in Python. One popular library for natural language processing (NLP) tasks, including text generation, is NLTK (Natural Language Toolkit). NLTK provides a range of tools for preprocessing, tokenization, and stemming, as well as language models and text generation algorithms.

Another popular library for text generation in Python is GPT-3 (Generative Pre-trained Transformer 3), which is a state-of-the-art language model developed by OpenAI. GPT-3 can generate coherent and natural-sounding text in a variety of languages and can be used for tasks such as translation, summarization, and content creation.

Other Python libraries and frameworks that can be used for text generation include TensorFlow, Keras, and PyTorch. These libraries provide tools for building and training neural network models, which can be used for text-generation tasks.

NLTK for text generation

One way to generate text using NLTK is to use a statistical language model, such as an n-gram model. An n-gram model is a language model that predicts the likelihood of a word or sequence of words based on the previous n-1 words in the sequence. To generate text using an n-gram model, you can sample from the distribution of words predicted by the model and select the most likely words based on the context.

Another approach to text generation using NLTK is a Hidden Markov Model. A Markov model is a statistical model that predicts the likelihood of a sequence of words based on the previous words in the sequence. To generate text using a Markov model, you can sample from the distribution of words predicted by the model and select the most likely words based on the context.

To get started with text generation using NLTK, you will need to install the library and familiarize yourself with its language modelling and text generation functions. You will also need a text dataset for your model to use as training data. Once you have these resources, you can build and train your text generation model using NLTK.

Example code for text generation in NLTK

Here is an example of how you could use the NLTK library to train a simple generative model for text using a bigram language model:

import nltk
from nltk.corpus import brown
from nltk.probability import FreqDist, ConditionalFreqDist

# Load and preprocess the data
text = brown.words()

# Create a bigram language model
bigrams = nltk.bigrams(text)
cfd = ConditionalFreqDist(bigrams)

# Generate text
seed_text = "The quick"
generated_text = seed_text
for i in range(10):
    # Find the next word using the bigram model
    next_word = cfd[seed_text].max()
    generated_text += " " + next_word
    seed_text = next_word
print(generated_text)

This code trains a bigram language model on the Brown corpus and then uses the trained model to generate 10 new words by feeding the model a seed phrase and sampling from the model’s predictions. The bigram model is trained to predict the next word in the sequence based on the previous word, so the generated text should be coherent with the language of the original corpus.

Tensorflow, Keras, and PyTorch

Tensorflow, Keras, and PyTorch are popular open-source software libraries for machine learning that can be used to develop and train generative models for text generation. There are several approaches to using these libraries for generative text, including:

  1. Sequence-to-sequence models: These models are trained to map input sequences to output sequences and can be used to generate text by feeding in a seed phrase and generating the next word or phrase in the sequence.
  2. Language models: These models are trained to predict the next word in a sequence based on the context of the previous words. Language models can generate text by sampling from the model’s predictions at each time step.
  3. Variational autoencoders (VAEs): VAEs are a generative model that can generate text by learning to reconstruct a given input sequence and then sampling from the latent space to generate new sequences.

Example code for text generation in Keras

Here is an example of how you could use Keras to train a simple generative model for text using an LSTM network:

import numpy as np
from keras.utils import to_categorical
from keras.layers import Embedding, LSTM, Dense
from keras.preprocessing.text import Tokenizer
from keras.preprocessing.sequence import pad_sequences
from keras.models import Sequential

# Load and preprocess the data
text = "This is an example of some text that we want to use to train a generative model."

# Tokenize the text and create a vocabulary
tokenizer = Tokenizer()
tokenizer.fit_on_texts([text])
vocab_size = len(tokenizer.word_index) + 1

# Convert the text to a sequence of word indices
sequences = tokenizer.texts_to_sequences([text])

# Pad the sequences to have the same length
sequences = pad_sequences(sequences, maxlen=10, padding='pre')

# Convert the sequences to one-hot encodings
X = to_categorical(sequences, num_classes=vocab_size)
y = np.roll(X, -1, axis=1)

# Define the model
model = Sequential()
model.add(Embedding(vocab_size, 10, input_length=10))
model.add(LSTM(50))
model.add(Dense(vocab_size, activation='softmax'))

# Compile and fit the model
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
model.fit(X, y, epochs=100)

# Generate text
seed_text = "This is an"
for i in range(10):
    # Encode the seed text as a sequence of word indices
    seed_sequence = tokenizer.texts_to_sequences([seed_text])[0]
    seed_sequence = pad_sequences([seed_sequence], maxlen=10, padding='pre')
    # One-hot encode the seed sequence
    seed_sequence = to_categorical(seed_sequence, num_classes=vocab_size)
    # Generate the next word using the model
    next_word_probs = model.predict(seed_sequence)[0]
    next_word_idx = np.argmax(next_word_probs)
    next_word = tokenizer.index_word[next_word_idx]
    seed_text += " " + next_word
print(seed_text)

This code trains a simple LSTM model on a single sentence of text and then uses the trained model to generate 10 new words by feeding the model a seed phrase and sampling from the model’s predictions. The model is trained to predict the next word in the sequence given the previous words, so the generated text should be coherent with the original sentence.

Conclusion

Text generation has many use cases and is a prominent area in which we see a lot of advancement. Now is the time to start implementing these new technologies to stay ahead.

If you aren’t convinced, you probably haven’t played around with ChatGPT, and then we suggest you do so. For the rest of us, let’s start implementing this technology in our workflows to help our organisations get ahead.

What applications are you building that will utilise the power of generative text? Let us know in the comments.

About the Author

Neri Van Otten

Neri Van Otten

Neri Van Otten is the founder of Spot Intelligence, a machine learning engineer with over 12 years of experience specialising in Natural Language Processing (NLP) and deep learning innovation. Dedicated to making your projects succeed.

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