Understanding Word Embeddings in NLP
Key Points
- Word embeddings turn words into numeric vectors that encode semantic similarity and contextual relationships, enabling machine‑learning models to process text.
- They are a core component in NLP applications such as text classification (e.g., spam detection), named‑entity recognition, word‑analogy and similarity tasks, question‑answering, document clustering, and recommendation systems.
- Embeddings are learned by training on large corpora (e.g., Wikipedia) after preprocessing (tokenization, stop‑word/punctuation removal) using a sliding context window that predicts target words from surrounding context and minimizes prediction error.
- The resulting continuous vector space positions semantically related words close together, as illustrated by a toy example where “apple” and “orange” have nearby three‑dimensional vectors.
- Even low‑dimensional vectors (e.g., 3‑D) can capture meaningful relationships, demonstrating how the geometry of the embedding space reflects word meanings.
Sections
- Word Embeddings: Concepts and Applications - The passage explains how word embeddings transform words into numeric vectors to capture semantic similarity, why such numeric representations are essential for machine‑learning models, and outlines their common NLP uses like text classification and named entity recognition.
- Understanding Word Vectors and TF‑IDF - The excerpt explains how words are encoded as multi‑dimensional vectors that reflect semantic similarity, then introduces frequency‑based embeddings—specifically TF‑IDF—as a method that quantifies word importance based on how often a term appears in a document versus across the whole corpus.
- Word Embeddings: From CBOW to Contextual Transformers - The passage contrasts static word2vec models (CBOW and skip‑gram) and GloVe with modern transformer‑based contextual embeddings that vary a word’s representation according to its surrounding context.
Full Transcript
# Understanding Word Embeddings in NLP **Source:** [https://www.youtube.com/watch?v=wgfSDrqYMJ4](https://www.youtube.com/watch?v=wgfSDrqYMJ4) **Duration:** 00:08:28 ## Summary - Word embeddings turn words into numeric vectors that encode semantic similarity and contextual relationships, enabling machine‑learning models to process text. - They are a core component in NLP applications such as text classification (e.g., spam detection), named‑entity recognition, word‑analogy and similarity tasks, question‑answering, document clustering, and recommendation systems. - Embeddings are learned by training on large corpora (e.g., Wikipedia) after preprocessing (tokenization, stop‑word/punctuation removal) using a sliding context window that predicts target words from surrounding context and minimizes prediction error. - The resulting continuous vector space positions semantically related words close together, as illustrated by a toy example where “apple” and “orange” have nearby three‑dimensional vectors. - Even low‑dimensional vectors (e.g., 3‑D) can capture meaningful relationships, demonstrating how the geometry of the embedding space reflects word meanings. ## Sections - [00:00:00](https://www.youtube.com/watch?v=wgfSDrqYMJ4&t=0s) **Word Embeddings: Concepts and Applications** - The passage explains how word embeddings transform words into numeric vectors to capture semantic similarity, why such numeric representations are essential for machine‑learning models, and outlines their common NLP uses like text classification and named entity recognition. - [00:03:04](https://www.youtube.com/watch?v=wgfSDrqYMJ4&t=184s) **Understanding Word Vectors and TF‑IDF** - The excerpt explains how words are encoded as multi‑dimensional vectors that reflect semantic similarity, then introduces frequency‑based embeddings—specifically TF‑IDF—as a method that quantifies word importance based on how often a term appears in a document versus across the whole corpus. - [00:06:13](https://www.youtube.com/watch?v=wgfSDrqYMJ4&t=373s) **Word Embeddings: From CBOW to Contextual Transformers** - The passage contrasts static word2vec models (CBOW and skip‑gram) and GloVe with modern transformer‑based contextual embeddings that vary a word’s representation according to its surrounding context. ## Full Transcript
A word on word embeddings.
Maybe a few words.
Word embeddings represent words as numbers,
specifically as numeric vectors,
in a way that captures the semantic relationships and contextual information.
So that means words with similar meanings are positioned
close to each other, and the distance and direction between vectors
encode the degree of similarity between words.
But why do we need to transform words into numbers?
The reason vectors are used to represent words is that
most machine learning algorithms are just incapable of processing
plain text in its raw form.
They require numbers as input to perform any task,
and that's where word embeddings come in.
So let's take a look at how word embeddings are used and the model is used to create them.
And let's start with a look at some applications.
Now what embeddings have become a fundamental tool
in the world of NLP.
That's natural language processing.
Natural language processing helps machines understand human language.
Word embeddings are used in various NLP tasks, so for example,
you'll find them used in text classification very frequently.
Now in text classification, word embeddings are often used in tasks such as
spam detection and topic categorization.
Another common task is NER,
that's an acronym for named entity recognition,
and there is used to identify and classify entities in text.
And an entity is like a name of a person or a place or an organization.
Now, word embeddings can also help with tasks
related to word similarity and word analogy tasks.
So, for example, the king is the queen as man is to a woman,
and then another example is in Q&A.
So question and answering systems
they can benefit from word embeddings for measuring semantic
similarities between words or documents for tasks like
clustering related articles, or finding similar documents, or
recommending similar items.
Now, word embeddings are created by trained models on a large corpus of text.
So maybe, like all of Wikipedia,
the process begins with preprocessing the text,
including tokenization and removing stopwords and punctuation.
A sliding context window identifies target and context words,
allowing the model to learn word relationships.
Then the model is trained to predict based on their context.
Positioning semantically similar words close together
in the vector space and throughout the training,
the model parameters are adjusted to minimize prediction errors.
So what does this look like?
Well, let's start with a super small corpus of just six words.
Here they are.
Now we'll represent each word as a three dimensional vector.
So each dimension
has a numeric value creating a unique vector for each word.
And these values represent the word's position
in a continuous three dimensional vector space.
And if you look closely, you can see that words with similar
meanings or contexts have similar vector representations.
So, for instance, the vectors for apple and for orange
are close together, reflecting this semantic relationship.
Likewise, the vectors for happy and sad have opposite directions,
indicating their contrasting meanings.
Now, of course, in real life, it's not this simple.
A corpus of six words isn't going to be too helpful in practice,
and actual word embeddings typically have hundreds of dimensions, not just three.
To capture more intricate relationships and nuances in meaning.
Now, there are two fundamental approaches to how word
embedding methods generate effective representations for words.
So let's take a look at some of these embedding methods.
And we'll start with the first one which is frequency.
So frequency based embeddings.
Now frequency based embeddings are word
representations that are derived from the frequency of words in a corpus.
They're based on the idea that the importance or the significance of a word
can be inferred from how frequently it occurs in the text.
Now, one such embedding of frequency based is called TF-IDF
that stands for Term Frequency Inverse Document Frequency.
TF-IDF highlights words that are frequent
within a specific document, but are rare across the entire corpus.
So, for example, in a document about coffee, TF-IDF would emphasize words
like espresso or cappuccino, which might appear often
in that document, but rarely in others about different topics.
Common words like the or and, which appear frequently across all documents,
would receive low TF-IDF based scores.
Now another embedding type
is called prediction based embeddings
and prediction based embeddings.
They capture semantic relationships and contextual information between words.
So, for example, in the sentences, "the dog is barking loudly." and "the dog is wagging its tail."
A prediction based model would learn to associate dog
with words like bark, wag, and tail.
This allows these models to create a single fixed representation
for dog that encompasses various, well, dog related concepts.
Prediction based embeddings.
They excel at separating words with close meanings,
and can manage the various senses in which a word may be used.
Now there are various models for generating word embeddings.
One of the most popular is called word2vec
that was developed by Google in 2013.
Now word2vec has two main architectures.
There's something called c b, o, w
and there's something called skip-gram,
and CBOW, that's an acronym for Continuous Bag of Words.
Now CBOW predicts a target word based on its surrounding context words.
Well, skip-gram does the opposite,
predicting the context words given a target word.
Now another popular method is called GLOVE.
Also an acronym, that one stands for Global Vectors for Word Representation.
That was created at Stanford University in 2014
and that uses co-occurrence statistics to create word vectors.
Now, these models, they differ in their approach.
What's a vec that focuses on learning from the immediate context around each word?
While glove takes a broader view by analyzing
how often words appear together across the entire corpus,
then uses this information to create word vectors.
Now, while these two word embedding models continue to be valuable tools in NLP,
the field has seen some significant advances with the emergence
of new tech, particularly transformers.
While traditional word embeddings assign a fixed vector to each word,
transformer models use a different type of embedding,
and it's called a contextual based embedding.
Now, contextual based embeddings are where
the representation of a word changes based on its surrounding context.
So, for example, in a transformer model,
the word bank would have different representations in the sentence
I'm going to the bank to deposit money and I'm sitting on the bank of a river.
This context sensitivity allows these models to capture
more nuanced meanings and relationships between words, which has led to all sorts
of improvements in the various fields of NLP tasks.
So that's word embeddings,
from simple numeric vectors to complex representations.
Word embeddings have revolutionized how machines understand and process human language.
Proving that transforming words into numbers is indeed a powerful tool
for making sense of our linguistic world.