This week we'll be looking at word embeddings. Word embeddings represent a fundamental change in natural language processing (NLP), transforming words into dense vector representations that capture their semantic and syntactic meaning. Beyond sparse, context-independent techniques such as bag-of-words and one-hot encoding, modern embedding techniques (from Word2Vec to transformers) enable machines to capture the linguistic relationships and nuances essential to advanced NLP applications.
Basics of word embeddings
A word embedding is a vector representation of a word. These help you understand the semantic and syntactic meaning of phrases and sentences. Word embeddings play an important role in natural language processing (NLP) tasks.
Bag-of-words and one-hot encoding are traditional representation methods. Bag-of-Words (BoW) models transform text documents into numerical representations for machine learning by treating the document as an unordered collection of words and encoding their frequencies. One-hot converts a categorical variable into a numerical form suitable for machine learning algorithms by creating a binary vector representation in which exactly one bit is “hot” (set to 1) and all other bits are “cold” (set to 0). Traditional methods have two major limitations. First, we were unable to determine semantic similarity. Second, a very sparse dataset was generated. Many solutions to these limitations are currently emerging.
Word embedding relies on three mathematical foundations. First, the vector space model. Second, the principle of dimensionality reduction. Third, distance and similarity metrics such as Euclidean distance and cosine similarity. Word embedding techniques and models
There are three prediction-based models: Word2Vec, GloVe (Global Vectors for Word Representation), and FastText. Word2Vec can use Continuous Bag-of-Words (CBOW) or Skip-Gram architectures. CBOW (Continuous Bag of Words) is a three-layer shallow neural network that learns word embeddings by predicting a central target word from surrounding context words. Skip-gram is a learning neural network architecture within the Word2Vec framework.
Word embedding by predicting surrounding context words given a target central word. GloVe is an unsupervised learning algorithm for obtaining vector representations of words. FastText extends Word2Vec by representing a word as a sum of character n-gram vectors. This subword approach captures morphological structure and allows the model to generate meaningful embeddings for rare or non-lexical words.
There are two types of contextualized word embeddings: ELMo (embeddings from language models) and transformer-based models (BERT, GPT). The predictive model assigns a fixed vector to each word, regardless of context. Contextualized models address this by assigning vector representations of words based on their context. I don't think there is any need to explain BERT (made by Google) and GPT (made by OpenAI).
Challenges and limitations
First, there may be bias in the generated embeddings. This bias may be due to the training dataset used to train these embedding models. In such cases, the impact on downstream applications is negative and even greater.
It's no surprise that techniques for bias detection and mitigation are important. Second, word embeddings lack interpretability and explainability. Embedded dimensions are not easy to understand. Therefore, research on visualization and interpretability tools for word embeddings is popular. Third, training and inference of embedded models has resource and computational constraints.
Therefore, it is difficult to deploy them to edge devices. Fourth, there are limits to adaptation to specialized domains and words outside the vocabulary. Therefore, innovative methods for domain-specific fine-tuning and extension of embedded models are needed. Fifth, as mentioned earlier, static predictive models lack context.
Word embeddings have revolutionized natural language processing by allowing machines to capture semantic relationships and contextual meaning in ways not possible with traditional methods. However, to realize its full potential, critical challenges must be addressed, including reducing bias in training data, increasing interpretability through visualization tools, optimizing the computational efficiency of edge deployment, and developing domain-specific adaptation strategies. Although contextualized models such as BERT and GPT have shown significant progress, they still have limitations in processing non-lexical words and specialized domains. Who knew that large-scale language models (LLMs) would one day be possible? Why not give Embedding credit for making the LLM possible?
Disclaimer
The views expressed above are the author's own.
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