Synopsis: MLPs for Text Classification

Multi-Layer Perceptrons (MLPs) are simple feedforward neural networks that can be applied to various text classification tasks such as sentiment analysis, topic categorization, and spam detection. While they are not inherently sequence-aware, MLPs provide a fast and easy-to-train baseline when used with proper text representations like word embeddings.

Role of Embeddings: Text data is first tokenized into words or subwords. Each token is then mapped to a dense vector using an embedding layer, such as Word2Vec, GloVe, or task-specific learned embeddings. These embeddings transform sparse and high-dimensional textual input into compact and trainable representations.

Training Embeddings: Embeddings can either be pre-trained or initialized randomly. Pre-trained embeddings like GloVe may be kept frozen for efficiency or fine-tuned during training to adapt to the specific task. Alternatively, the embedding weights can be learned from scratch along with the MLP’s weights.

Input to MLP: To feed text into an MLP, token-level embeddings are aggregated into a fixed-size vector using simple operations like averaging, summing, or concatenation. This aggregated vector is then passed through one or more fully connected layers with nonlinear activation functions (such as ReLU), and finally to a softmax layer for outputting class probabilities.

Despite their simplicity, MLPs have several limitations when applied to text classification:

No Sequence Awareness: MLPs do not consider word order or dependencies. For example, phrases like "not good" and "good not" may produce the same result if embeddings are averaged.

Loss of Context: Aggregating all word embeddings into a single vector can dilute important contextual nuances, especially in longer texts.

Fixed-Length Input Requirement: MLPs require fixed-size input vectors, necessitating padding or truncation of variable-length texts, which can degrade performance.

Weak for Long or Complex Texts: Without architectural features like recurrence or attention, MLPs struggle with tasks that require understanding of long-range dependencies or subtle linguistic cues.

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