) What is an embedding? Did you use an embedding in Project 2?
An embedding in the context of natural language processing (NLP) refers to a numerical representation of textual data such as words, sentences, or documents. It aims to capture the semantic meaning or context of the text in a continuous vector format, making it suitable for machine learning algorithms. Embeddings are usually learned from large amounts of unlabeled text data using techniques such as Word2Vec, GloVe, or BERT.
In Project 2, the use of embeddings depends on the specific task and approach you have followed. Embeddings can be utilized in various stages:
1. Input Embedding: During the preprocessing of textual data, each individual word or token is typically transformed into an embedding representation before feeding it to the model.
2. Embedding Layer: Many deep learning models in NLP, such as recurrent neural networks (RNNs) or convolutional neural networks (CNNs), include embedding layers. These layers map the input tokens to their corresponding embeddings within the model architecture.
3. Pretrained Embeddings: In some cases, pretrained embeddings trained on massive text corpora are used directly as a fixed feature representation for downstream tasks.
Depending on the project requirements, you may or may not have explicitly used embeddings in Project 2. However, it is a common practice in many NLP tasks to employ embeddings to enhance the understanding and representation of textual data.