A “Bidirectional LSTM” or BiLSTM is a type of recurrent neural network (RNN) that is used for sequences of data, such as time series or text.
An LSTM, which stands for Long Short-Term Memory, is a special type of RNN that can learn long-term dependencies, making it useful for tasks like language modeling where context from many steps back can be important.
A Bidirectional LSTM is an extension of this idea. Instead of just reading the data from the start to the end, a BiLSTM reads the data in both directions, from start to end and from end to start. This means it has two layers of LSTMs: a forward layer that reads the input sequence forwards, and a backward layer that reads the input sequence backwards.
For example, if we are processing a sentence like “The cat sat on the mat”, the forward LSTM would read the sentence as “The cat sat on the mat”, while the backward LSTM would read it as “mat the on sat cat The”. By reading the data in both directions, a BiLSTM can capture information from both the past and the future, relative to each point in the sequence.
This can be very useful in tasks like text classification or named entity recognition, where context from both before and after a word can be important to understand the word’s meaning. For example, in the sentence “I live in Paris and love French food”, knowing that “French food” comes after “Paris” can help us understand that “Paris” is being used as a location, not a person’s name.
So in summary, a Bidirectional LSTM is a type of neural network that is good at processing sequences of data, and can capture context from both before and after each point in the sequence.
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