Word Embedding
A word embedding is a numerical vector representation of words used in Natural Language Processing (NLP). The key idea is to map words into a continuous vector space where semantically similar words are located close to each other. For instance, in a well-trained embedding space, king and queen vectors will be near each other, just like Paris and France.
Why are word embeddings important?
Earlier methods like bag-of-words or one-hot encoding treated words as discrete symbols, without any sense of semantic relationships. For example, dog and cat would be as unrelated as dog and car. Word embeddings solve this by placing words in a geometric space where semantic and syntactic relationships emerge naturally.
Popular approaches
- Word2Vec (Mikolov et al., 2013): Introduced CBOW and Skip-Gram architectures to learn embeddings from word contexts.
- GloVe (Pennington et al., 2014): Combines global word co-occurrence statistics with matrix factorization.
- FastText (Bojanowski et al., 2017): Extends Word2Vec by considering subword information, making it effective for morphologically complex languages.
Limitations and evolution
Static embeddings assign the same vector to a word regardless of its context (bank = riverbank / financial institution). Contextual models like ELMo, BERT, and GPT overcome this by generating embeddings that depend on surrounding words.
Word embeddings marked a paradigm shift in NLP by allowing models to capture not only individual word meanings but also relationships between words. Famous analogies such as king – man + woman ≈ queen illustrate how vector arithmetic in embedding spaces reflects semantic and syntactic regularities. This property enables transfer learning, where embeddings trained on large corpora can be reused across tasks like sentiment analysis, question answering, or machine translation.
However, embeddings are not without challenges. They tend to inherit biases present in training data: for instance, associating gender stereotypes with professions. This has raised concerns about fairness and ethics in AI applications. Researchers have proposed methods such as debiasing algorithms and controlled training corpora to mitigate these issues, though perfect solutions remain elusive.
From a computational standpoint, embeddings also improve efficiency and scalability. Compared to sparse representations, they drastically reduce dimensionality while preserving semantic richness, enabling faster training and inference. Modern contextual embeddings like BERT go further by embedding entire sentences and tokens in context, which boosts accuracy in downstream NLP tasks such as summarization or natural dialogue generation.
Overall, word embeddings remain a cornerstone of NLP. They laid the groundwork for today’s transformer-based language models, and even though contextual embeddings now dominate, static embeddings continue to serve as lightweight, interpretable, and resource-efficient tools in many practical applications.
