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Deep Dive into Sequences: Unveiling Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTM)
In our previous articles, we explored the initial steps of Natural Language Processing (NLP), from tokenization to creating sequences. With the foundations set, let’s shift gears and move towards a more intriguing component of NLP: handling sequential data with Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTM). This article aims to demystify these fascinating techniques, allowing you to understand and implement them effectively in your NLP tasks.
Why Sequences Matter in NLP
When dealing with language, sequence matters. “The cat ate the mouse” has a drastically different meaning from “The mouse ate the cat”. Traditional feed-forward networks lack the ability to maintain the order of words, leading to poor performance on sequential tasks. This is where RNNs and LSTMs come into play.
Understanding Recurrent Neural Networks (RNNs)
The Concept of RNNs
Recurrent Neural Networks, as the name suggests, have loops in them, providing a form of internal memory that helps them remember past inputs in the sequence. This characteristic makes them ideal for processing sequential data.
A simple RNN consists of a layer of neurons, each receiving input not only from incoming data but also from the output of its neighboring neuron on the left. This allows information to persist or “recur” from one step of the sequence to the next.
RNNs and Sequences
RNNs are versatile in handling various types of problems:
- Sequence-to-Vector: For example, sentiment analysis where a sequence of words (sentence) is input, and a single output is required (sentiment).
- Vector-to-Sequence: For example, image captioning where an image is input, and a sequence of words describing the image is output.
- Sequence-to-Sequence: For example, machine translation where a sequence of words in one language is input, and a sequence of words in another language is output.
