Word Embeddings

Word embedding is a technique used in natural language processing (NLP) and machine learning to represent words in a mathematical form. It is a way of mapping words or phrases to vectors of real numbers, such that similar words are represented by vectors that are close together in some mathematical space.

Word embeddings are created using neural network models, which are trained on large datasets of text. During training, the model learns to assign each word a vector representation based on the context in which it appears in the text. This means that words that are often used in similar contexts will have similar vector representations.

Word embeddings have many applications in NLP, including sentiment analysis, text classification, and language translation. They are also used in recommendation systems and search engines to help users find related content.

Overall, word embeddings provide a powerful way to represent language in a way that can be easily processed by machine learning algorithms. By converting words to vectors, they enable us to perform a wide range of NLP tasks that would otherwise be difficult or impossible.

An example of word embedding using vector numbers could be:

The word "cat" can be represented by the vector [0.2, 0.6, -0.3], while the word "dog" can be represented by the vector [-0.1, 0.8, 0.4]. These vectors were obtained through a neural network model trained on a large dataset of text, where the model learned to map words to vectors based on the context in which they appeared. Since "cat" and "dog" are both animals and are often used in similar contexts, their vector representations are relatively close to each other, with a cosine similarity of 0.8.

Glove

GloVe stands for Global Vectors for Word Representation. It is another word embedding technique that uses a co-occurrence matrix to capture the statistical patterns of word occurrences in large text corpora. GloVe is used to create word embeddings that are widely used in NLP and machine learning applications.

co-occurrence matrix

Is a matrix that records the frequency of word co-occurrences in a corpus of text. Each row and column of the matrix correspond to a different word in the corpus, and each cell represents the number of times the corresponding pair of words co-occurs in the text. The co-occurrence matrix can then be used as input to a variety of machine learning algorithms, including neural networks, to create word embeddings. GloVe is one such word embedding technique that uses a co-occurrence matrix to capture the statistical patterns of word occurrences in large text corpora. By analyzing the patterns of co-occurrence, GloVe creates word vectors that capture the meaning of words in a way that is useful for NLP and machine learning tasks.

FastText

FastText is a word embedding technique developed by Facebook AI Research. It is similar to other word embedding techniques, but with the added ability to represent subword information, which allows it to handle out-of-vocabulary words and rare words more effectively. FastText is often used in NLP tasks such as text classification, part-of-speech tagging, and natural language understanding.

Word2vec

Word2vec is another word embedding technique used in natural language processing and machine learning. It is based on a neural network model that is trained to predict the context in which a given word appears in a text corpus. During training, the model learns to assign each word a vector representation based on the patterns of co-occurrence with other words in the corpus. Word2vec is widely used in NLP and machine learning applications, including sentiment analysis, text classification, and language translation.

How they are used in ML pipeline:

Word embeddings such as GloVe, FastText, and Word2vec use different techniques to create vector representations of words, but all are designed to capture the meaning of words in a way that is useful for NLP and machine learning tasks.

GloVe, FastText, and Word2vec are all used in the machine learning pipeline for natural language processing tasks. Here are the general steps of using these word embeddings in a machine learning pipeline:

1. Text preprocessing: The text is cleaned and tokenized into individual words or phrases.
2. Word embedding: The words or phrases are converted into vector representations using one of the word embedding techniques, such as GloVe, FastText, or Word2vec.
3. Feature extraction: The vector representations are used to extract features that can be used as input to a machine learning algorithm.
4. Model training: A machine learning model is trained using the extracted features and labeled data.
5. Model evaluation: The trained model is evaluated on a separate set of data to measure its performance.
6. Model deployment: The trained model is deployed in a production environment to perform the NLP task for which it was designed.

These steps may vary depending on the specific NLP task and the choice of word embedding technique. However, the general idea is to use word embeddings to convert text into a format that can be effectively processed by machine learning algorithms.