A Guide to Word Embedding NLP

Word embeddings have revolutionized natural language processing (NLP) by enabling machines to better understand and process human language. By converting words into numerical vector representations, word embeddings capture the meanings, relationships, and contexts of words. This allows machines to perform complex linguistic tasks, such as sentiment analysis, text classification, and semantic understanding.

Explore the fundamentals of word embeddings, popular techniques like Word2Vec and GloVe, and their applications in NLP, as well as learn about key concepts, challenges, and modern solutions to word embeddings in NLP. 

What is word embedding in NLP?

Word embedding in NLP is a technique that translates words into numerical vectors. These vectors allow machines to understand linguistic patterns and relationships. As a machine learning method, word embedding enables the positioning of words with similar meanings close to each other in a vector space, capturing nuanced semantic and syntactic information. 

Because machine learning algorithms can't process raw text, they convert it into numerical data, which leads to the creation of word embeddings. Word embedding is a technique in NLP that turns words into vectors, or groups of numbers representing individual words, in a lower-dimensional space, making text easier for the machines to work with.

Training word embeddings involves feeding a machine large amounts of text data and adjusting the vector representations on an as-needed basis, depending on the context where the words appear. For NLP tasks, word embeddings allow machines to better and more deeply understand the semantic relationships between words. Some examples of word embedding tasks include sentiment prediction, such as deciding whether a social media post about a product is positive or negative, and article or email classification, such as determining if an email is personal, promotional, or spam. 

Word embedding techniques 

You must create word embedding techniques by teaching a model to analyze a large amount of text and learn the meanings and relationships between words. The model adjusts based on the context in which the words appear. 

Various methods exist for generating word embeddings. The technique you choose depends on factors like the type and size of the data and text sources. It also depends on the intended outcome and purpose of the word embeddings, for example, summarizing versus categorizing. 

Most older methods evaluate the quality of these word vectors by looking at the distance between them. However, newer prediction-based approaches use word analogies, like “sun is to day as moon is to night,” to check how well the vectors capture relationships between words.

You can use two main methods to create these vectors: global matrix factorization, such as global vectors for word representation (GloVe), and local context methods, such as Word2Vec. Global methods use statistical information well but struggle with word analogies, while local methods do better with analogies but don’t fully use the available data. Newer models aim to combine the strengths of both methods, producing more accurate word vectors for a variety of tasks.

GloVe (global vectors for word representation) 

Developed by a research team at Stanford in 2014, GloVe combines global matrix factorization and local context-based methods, aiming to provide a more comprehensive understanding of word relationships by leveraging co-occurrence statistics across the entire text corpus. Instead of focusing on local context or nearby words, GloVe uses a co-occurrence matrix to analyze word relationships across the entire text. As a result, the method performs particularly well on tasks involving understanding semantic relationships, such as named entity recognition and word analogies. 

Word2Vec (word to vector)

Developed by Google in 2013, Word2Vec is a method based on neural networks that learns word representations by predicting the likelihood of a word appearing in a particular context. Instead of just counting how often words appear together, Word2Vec tries to predict which words are likely to occur near each other. 

For word embedding in NLP, Word2Vec offers two neural architectures, or techniques: the continuous bag of words (CBOW) and the skip-gram. The CBOW technique predicts a target word based on its context, while the skip-gram technique does the opposite, enhancing the model's ability to understand word contexts and relationships. For example, the CBOW technique predicts the word “dog” if the surrounding words include “animal” or “pet,” while the skip-gram technique sees the word “dog” and then tries to predict words like “animal” or “pet” that may appear nearby. 

Word2Vec is a popular model because it effectively captures complex linguistic patterns and is more computationally efficient than other models. 

Utilizing word embeddings

You have various options for employing word embeddings in your projects. The option you choose depends on several factors, including your project, data set, and NLP tasks in which you’re working. 

Learn as embedding 

Learn as embedding is the process of having the machine learn word embeddings directly during the training process of a neural network instead of relying on a pre-trained embedding model, like GloVe or Word2Vec. You might opt to train a word embedding model specific to your data set, which requires substantial text data but allows for custom embeddings that are closely aligned with your task.

Reuse as embedding 

Reuse as embedding is the process of using pre-trained word embeddings from one model and applying them to another NLP model. Rather than training word embeddings from scratch, models reuse embeddings generated by methods such as GloVe and Word2Vec, which are pre-trained on large corpora text. 

Leveraging pre-trained embeddings, such as those from GloVe or Word2Vec, can save time and computational resources. Using these pre-trained embeddings as-is or fine-tuning them to better suit your specific NLP tasks has many benefits. However, in some settings, reusing NLP models can require additional steps, such as retraining or validating new data. 

Key considerations 

Understanding and working with word embeddings involves several important factors. Word embeddings have become a crucial element in NLP, enabling the representation of language in a way that captures the true meanings of words and phrases in context. This approach aligns more closely with how humans understand language, making it an essential tool for advancing NLP.

Consider these key concepts that have contributed to the effectiveness of word embeddings, making them a powerful asset in the field of NLP:

Distributional hypothesis 

The distributional hypothesis is the underlying principle of word embeddings that suggests that words appearing in similar contexts tend to have related meanings. This concept drives the effectiveness of embedding models because it means that even if the machine does not understand an entire sentence, it may be able to use what it knows about the words in the sentence to decipher its overall meaning. In NLP, the idea that words appearing in similar contexts have similar meanings is a way machines use context to understand semantics.

Dimensionality and density 

Dimensionality represents the number of features, or values, that a vector uses to describe each word. For instance, a 300-dimensional vector for a word means that it uses 300 numbers to capture various aspects of the word’s meaning. 

Density refers to the number of vectors in a representation, either sparse or dense. Sparse vectors focus on the structure or syntax of sentences, making them more effective at capturing word order and, thus, more computationally efficient. Conversely, dense vectors represent words' meaning or semantics and are better at capturing word meaning and handling larger vocabularies.

Choosing the right dimensionality for word embeddings is a critical issue. A lower-dimensional embedding may fail to capture all word relationships, while a high-dimensional one can lead to overfitting, slow training, and increased computational costs. For example, Word2Vec became popular for using methods like CBOW and skip-gram to predict a word’s surrounding context and generate a dense vector representation. However, Word2Vec works well for single words, but it struggles with longer text. 

Challenges and solutions 

While word embedding allows computers to process text in deeper and more nuanced ways, some limitations and problematic challenges can occur. For example, working with word embeddings presents unique challenges, such as handling out-of-vocabulary (OOV) words, presenting learned biases, and capturing polysemy. 

Out-of-vocabulary (OOV) words

Handling out-of-vocabulary (OOV) words is a major challenge in certain NLP tasks because the model struggles to perform well when encountering words it has not seen before. 

Two solutions to OOV words are to assign a unique and random vector to the OOV word, such as a prefix or suffix, or to use a unified random vector to stand for all OOV words, relying on the context and the words surrounding where the OOV word appears to decipher its meaning. However, since the machines rarely encounter OOV words, it’s challenging to train them on OOV words. A potential solution for this issue involves a method based on the linguistic principle of distributed hypothesis; when encountering an OOV word, the model uses similar contexts to understand the unfamiliar word.

Biases 

Cultural, gender, and religious biases in word embeddings can lead to problematic results. Some methods used to address this issue involve creating a pre-made list of biased words, called seed words, to remove biases. However, new automated methods may be more efficient and accurate. 

Apple created the DD-GloVe model to reduce biases in word embedding by using dictionary definitions to guide the training process. The DD-GloVe model works by automating the process of finding biased words by starting with one pair of biased or seed words and then identifying more based on the definitions of those pairs of words. 

Polysemy 

Polysemy, which refers to a single word with multiple meanings, such as “lead” as a verb indicating guiding someone or something or “lead” as a noun denoting the heavy metal, is problematic in word embedding. Machines cannot always differentiate a word’s meaning based on its context. The same vector represents both meanings of polysemous words, causing confusion and issues predicting text in varying contexts in NLP tasks.

Some methods to address these problems have tried to create an embedding for each of a word’s different meanings, but it's difficult to determine how many senses a word has due to varying contexts. As a potential solution to this issue, the Adaptive Cross-Contextual Word Embedding (ACWE) method adapts word representations based on the context in which they appear, using topic modeling. ACWE generates both global and local word embeddings, updating the word’s vector dynamically depending on the context to capture polysemy.

Advancing your NLP skills with Coursera 

Consider exploring related courses on Coursera to delve deeper into word embeddings and their applications in NLP. You can find programs covering foundational concepts, practical implementations, and beginner and advanced techniques in NLP. These programs can enhance your understanding and skills in working with word embeddings in the context of NLP tasks and projects. You can gain a solid foundation in how these aspects of word embeddings function and how to apply them in various NLP tasks effectively. Consider enrolling in DeepLearning.AI’s Deep Learning Specialization, a five-course series available on Coursera, for a detailed exploration. Alternatively, consider earning IBM’s IBM Machine Learning Professional Certificate on Coursera for a comprehensive overview of machine learning and practical guidance.