What Are Embeddings in Machine Learning?

by | AI Education

Midjourney depiction of high-dimensional data
In machine learning, embeddings are a technique used to represent complex, high-dimensional data like words, sentences, or even entire documents in a more manageable, lower-dimensional space.

An analogy would be nice.

Right. Think about Lego bricks. A lot of them.

High-dimensional data is like the individual Lego bricks in their original, separate forms. Each brick is distinct – different shapes, colors, and sizes, and in different positions and relations to each other. If we were to describe all of these details times every brick this would be a lot of information.

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This image is like high-dimensional data where every detail is preserved, but it’s also complex and unwieldy. Just as sorting each individual Lego brick by hand would be time-consuming and overwhelming, processing high-level data can be computationally intensive and complex.

Now, imagine that instead of dealing with individual bricks, you group them into pre-assembled Lego sets – a castle, a spaceship, and a car.

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Each set represents a collection of bricks (data points) combined in a meaningful way.

This is what embeddings do. They take the high-level, detailed data and transform it into a lower-dimensional, more abstract representation. In this form, the data is easier to manage and understand. Instead of handling countless individual bricks (data points), you’re now dealing with a smaller number of coherent, meaningful sets (embeddings).

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Just like it’s easier to describe and work with a few Lego sets than thousands of individual bricks, embeddings make it easier for machine learning models to process and learn from data. They distill complex, detailed information into simpler, more meaningful representations.

With the Lego analogy in mind, here are two key technical concepts related to embeddings in machine learning.

Dimensionality Reduction in Embeddings
Embeddings reduce the dimensionality of data, which originally might be extremely sparse (especially in the case of textual data) and high-dimensional, into a more dense, lower-dimensional space. This makes the data easier to work with and can improve the performance of machine learning models.

Capturing Semantics in Embeddings
In the case of word embeddings, for example, similar words tend to have similar vector representations. This means that the embeddings capture not just the identity of words but also their semantic (meaning) and syntactic (grammatical) properties. For instance, in a well-trained embedding space, words like “king” and “queen” would be closer to each other than to a word like “apple.”

Examples and benefits of different types of embeddings in machine learning

Here are 10 examples of embedding types, with real-world examples to help illustrate how they function.

Word2Vec Embedding

This is a popular word embedding used in natural language processing. It represents words in a vector space where similar words are positioned close to each other.

Word2Vec in E-Commerce Search Engines: By understanding semantic similarities between words, the search engine can return more relevant product results even if the search terms don’t exactly match the product titles.

GloVe (Global Vectors) Embeddings

Another word embedding technique that aggregates global word-word co-occurrence statistics from a corpus, and then maps these statistics onto a lower-dimensional space.

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GloVe in Customer Service Chatbots: A telecom company can employ GloVe embeddings in their chatbots to better understand customer queries. This helps the chatbot to comprehend variations in language, improving its ability to respond accurately to customer requests and reduce the load on human customer service representatives.

BERT Embeddings

Developed by Google, BERT (Bidirectional Encoder Representations from Transformers) generates embeddings for words based on their context within a sentence, leading to more accurate representations.

BERT Embeddings in Legal Document Analysis: A legal firm can use BERT embeddings to analyze legal documents. The embeddings help in identifying similar clauses and precedents across large volumes of legal texts, thus aiding lawyers in research and case preparation.

Doc2Vec Embedding

An extension of Word2Vec, Doc2Vec creates vector representations for entire documents or paragraphs, not just individual words.

Doc2Vec in News Aggregation Platforms: A news aggregator platform can use Doc2Vec to group similar news articles together. This helps in providing users with a more organized reading experience by clustering related news, improving user engagement.

FastText Embedding

Developed by Facebook, FastText extends Word2Vec to consider subword information, making it efficient in handling out-of-vocabulary words and morphologically rich languages.

FastText in Social Media Analytics: A marketing agency can leverage FastText for social media sentiment analysis. Its ability to handle misspellings and abbreviations common in social media posts allows for more accurate analysis of public sentiment regarding brands or products.

Node2Vec Embeddings

An algorithm to generate embeddings for nodes in a graph, capturing the network structure. This is useful in social network analysis, recommendation systems, and more.

Node2Vec Embeddings in Financial Fraud Detection: A bank can use Node2Vec embeddings to analyze transaction networks. By understanding the structure of normal transaction patterns, the system can flag anomalies that may indicate fraudulent activities, thereby enhancing the security of transactions.

FaceNet Embeddings

Used in facial recognition systems, FaceNet creates embeddings for faces that capture the key features of each face. These embeddings can then be used for identifying or verifying individuals.

FaceNet Embeddings in Retail Customer Service: A high-end retail store can employ FaceNet embeddings for facial recognition to identify VIP customers as they enter the store. This enables personalized customer service, enhancing the shopping experience for key clients.

Siamese Networks for Image Embeddings

Used in tasks like image similarity and object tracking, these networks learn to encode images into a vector space where similar images are closer together.

Siamese Networks in Real Estate Image Matching: A real estate website can use Siamese networks to compare property images, helping users find houses with similar architectural styles or interior designs, thus improving the property search process.

ELMo (Embeddings from Language Models)

ELMo embeddings are context-dependent representations that capture syntax and semantics from different linguistic levels, providing deep representations for words.

ELMo in Content Recommendation Systems: An online streaming service can use ELMo embeddings to understand user reviews and preferences. This deep understanding of content and user feedback enables the service to make more accurate recommendations, increasing viewer satisfaction and retention.

Autoencoder Embeddings for Anomaly Detection

In this application, an autoencoder neural network is trained to compress data into a lower-dimensional space (embedding) and then reconstruct it. The autoencoder embeddings can be used to detect anomalies or outliers in the data.

Autoencoder Embeddings in Manufacturing Quality Control: In a manufacturing plant, autoencoder embeddings can be used to detect anomalies in product quality. By learning the normal patterns of product data, the system can identify defective products early in the manufacturing process, reducing waste and improving quality.

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How do Word Embeddings Work with Natural Language Processing (NLP)?

Word embeddings are crucial in natural language processing (NLP) for several reasons:

Handling High-Dimensional Data

Natural language is inherently high-dimensional, with a vast vocabulary and many ways to express the same idea. Traditional representations like one-hot encoding create sparse and large vectors. Word embeddings, however, provide a dense, lower-dimensional representation, making the data more manageable for algorithms.

Handling High-Dimensional Data in Online Retail: An online retail giant uses word embeddings to manage product descriptions and customer reviews. This condensed representation of text simplifies the process of recommending products based on textual similarities, improving the accuracy of suggestions.

Capturing Semantic and Syntactic Relationships in Word Embeddings

Word embeddings are designed to capture the nuances of language. Words with similar meanings are located closer in the embedding space. This helps in understanding context, synonyms, and even analogies, thereby enhancing the comprehension of language models.

Capturing Semantic and Syntactic Relationships in Search Engines: A search engine company employs word embeddings to understand user queries better. This helps in presenting search results that are semantically relevant, even if the exact words in the query aren’t present in the results, enhancing user experience.

How Embeddings Improve Model Performance

Models using embeddings generally perform better in tasks like text classification, sentiment analysis, and translation. This is because embeddings provide a richer representation of words than simple numerical identifiers.

Improving Model Performance in Customer Support Chatbots: A telecommunications company uses word embeddings in its customer service chatbots. This leads to more accurate responses to customer inquiries, as the bot understands the context better, leading to increased customer satisfaction.

How Embeddings Improve Generalization

Embeddings allow models to generalize better to new, unseen data. Since embeddings encapsulate semantic meanings, a model can make reasonable assumptions about words it hasn’t encountered before, based on their position in the embedding space.

Generalization in Sentiment Analysis for Market Research: A market research firm uses word embeddings for sentiment analysis on social media posts. The embeddings help in accurately gauging public sentiment about products or brands, even when new slang or phrases are used.

Handling Context with Embeddings

Many words have multiple meanings depending on the context. Contextual embeddings (like those from BERT or ELMo) address this by providing different vector representations for the same word in different contexts.

Handling Context in Financial News Aggregation: A financial analytics firm uses contextual word embeddings to differentiate the meanings of words in different contexts within financial news articles, ensuring that its news aggregation and summarization tool correctly interprets market-relevant news.

How Embeddings Improve Efficiency in Training

By using pre-trained embeddings, you can significantly reduce the training time for NLP models. These embeddings have already captured a vast amount of linguistic information from large corpora, and transferring this learning can be very efficient.

Efficiency in Training for Healthcare Documentation Analysis: A healthcare data analytics company employs pre-trained embeddings to quickly train its models to analyze patient records and medical notes. This allows for efficient extraction of relevant medical information, improving patient care and research.

How Embeddings Assist in Language Transferability

Embeddings can assist in transferring knowledge between languages in multilingual models, making it easier to build NLP applications for languages with less available training data.

Language Transferability in Multilingual Customer Service: An international hotel chain uses multilingual embeddings in its customer service systems to provide support in multiple languages. This assists in delivering high-quality service to customers worldwide, despite language barriers.

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How to Prepare Data for Embeddings

Preparing data for embeddings in machine learning, especially for text data, involves several key steps:

1. Data Collection: Gather a relevant dataset for your specific task. For text data, this could mean collecting documents, sentences, or other textual data from various sources.
2. Cleaning and Preprocessing:

  • Remove Noise: Strip out irrelevant elements like HTML tags, special characters, or extraneous punctuation.
  • Normalization: Convert the text to a consistent format, such as lowercasing all letters, to reduce redundancy in the data.
  • Tokenization: Split the text into smaller units (tokens), typically words or subwords.
  • Removing Stop Words: Sometimes, removing common words (like ‘the’, ‘is’, etc.) that offer little value in analysis can be beneficial.
  • Stemming/Lemmatization: Reduce words to their base or root form. Example, “running” becomes “run”.

3. Vectorization: The process of converting text tokens into numerical form. Common techniques include:

  • One-Hot Encoding: Represent each word as a vector in a high-dimensional space where each dimension corresponds to a word in the vocabulary.
  • TF-IDF (Term Frequency-Inverse Document Frequency): Reflects how important a word is to a document in a collection, balancing its frequency in the document against the number of documents it appears in.

4. Using Pre-Trained Embeddings: For many applications, especially those involving deep learning, it’s common to use pre-trained embeddings like Word2Vec, GloVe, or BERT. In this case, you’ll map your tokens to the corresponding vectors from these pre-trained models.
5. Creating Custom Embeddings: If you’re training your own embeddings (using algorithms like Word2Vec or FastText), you’ll need a large corpus of text data. The corpus should be relevant to your domain to capture the specific nuances of the language used.
6. Sequence Padding (for Neural Networks): If you’re feeding the embeddings into a neural network for tasks like classification or sentiment analysis, you need to ensure that all sequences (sentences, documents) are of the same length. This can be achieved through padding (adding zeros to shorter sequences) or truncation (cutting off longer sequences).
7. Data Splitting: Divide your dataset into training, validation, and test sets. This is crucial for evaluating the performance of your model and preventing overfitting.
8. Quality Check and Validation: Finally, it’s important to manually check and validate a subset of your data to ensure the preprocessing steps are correctly applied.

Proper data preparation is essential for the effective use of embeddings in machine learning models, as it directly impacts the quality and performance of the results.

The Transformative Power of Embeddings

Embeddings represent a transformative approach in machine learning that reduces data complexity and increases the understandability and utility of data.

Whether improving product recommendations, delivering quality customer service, retrieving and analyzing information, supporting cyber-security, identifying user preferences, finding defects, or many other applications, embeddings play a critical role.

We hope this article has helped you better understand embeddings, and how they might help you and your organization be more successful in fulfilling your mission and attaining your goals.

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