library(keras)
# Define an RNN
model <- keras_model_sequential() %>%
layer_embedding(input_dim = 10000, output_dim = 32) %>%
layer_simple_rnn(units = 32) %>%
layer_dense(units = 1, activation = "sigmoid")
# Compile the model
model %>% compile(
optimizer = "adam",
loss = "binary_crossentropy",
metrics = c("accuracy")
)
# Summary of the model
summary(model)9 Advanced NLP Techniques
Natural Language Processing (NLP) has evolved significantly, thanks to advancements in neural network-based architectures. In this chapter, we’ll explore advanced techniques, including recurrent neural networks (RNNs), Transformers, and their applications in tasks like summarization and translation.
9.1 Introduction to Advanced NLP
Advanced NLP techniques leverage deep learning to process and generate human-like text. These methods enable applications such as: - Machine translation. - Summarization of large text corpora. - Conversational AI and chatbots.
9.2 Recurrent Neural Networks (RNNs)
RNNs are a type of neural network designed to handle sequential data, such as text or time series. They maintain a memory of previous inputs, making them suitable for tasks like language modeling and sentiment analysis.