Dense Representations: Word Embeddings
CAP-6640: Computational Understanding of Natural Language
Spencer Lyon
Prerequisites
L03.01: Sparse Representations (BoW, TF-IDF, and their limitations)
Week 2: Tokenization and text normalization
Outcomes
Explain why dense representations address the fundamental limitations of sparse vectors
Describe the Word2Vec Skip-gram and CBOW training objectives at an intuitive level
Contrast GloVe’s co-occurrence approach with Word2Vec’s predictive approach
Perform word embedding arithmetic and similarity queries using pre-trained vectors
Load and use pre-trained word embeddings in SpaCy and Gensim
References
J&M Chapter 5: Embeddings — Sections 5.1–5.7 (download)
spaCy Course Chapter 2: Word Vectors and Semantic Similarity
The Synonym Problem¶
In the last lecture we built up from binary presence to TF-IDF — each step was a genuine improvement. But we ended with a sobering demonstration: two sentences that mean nearly the same thing can have almost zero cosine similarity if they use different words. Let’s recall exactly why.
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.metrics.pairwise import cosine_similarity
docs = [
"I am happy and joyful today",
"I am glad and cheerful today",
]
vec = TfidfVectorizer()
X = vec.fit_transform(docs)
print(f"Cosine similarity: {cosine_similarity(X[0], X[1])[0][0]:.3f}")
print(f"Features: {vec.get_feature_names_out()}")Cosine similarity: 0.432
Features: ['am' 'and' 'cheerful' 'glad' 'happy' 'joyful' 'today']
The similarity is well below 1.0 despite the sentences being near-synonyms. The reason is structural: in a sparse representation, every word occupies its own dimension. “Happy” and “glad” are orthogonal — they share no dimensions at all. No amount of clever weighting can fix this because the representation itself has no notion of meaning.
What if we could place words in a space where proximity reflects meaning? Where “happy”, “glad”, “joyful”, and “cheerful” all live in the same neighborhood? That’s exactly what word embeddings give us — and it’s one of the most important ideas in modern NLP.
The Distributional Hypothesis¶
Before we see how to build embeddings, we need to understand why they should work at all. The answer comes from a deceptively simple observation about language.
The linguist John Rupert Firth wrote in 1957:
“You shall know a word by the company it keeps.”
This idea — known as the distributional hypothesis — says that words appearing in similar contexts tend to have similar meanings. Consider the following sentences:
“I had a delicious [???] for breakfast”
“She ordered a fresh [???] from the bakery”
“The [???] was warm and crispy”
What words could fill the blank? Probably things like “croissant”, “muffin”, “bagel”, “pastry”. These words aren’t synonyms in the dictionary sense, but they share a distributional profile — they appear in similar linguistic contexts. And that similarity in context reflects a real similarity in meaning.
Now here’s the key insight: if two words consistently appear near the same neighbors, we can learn vectors for them that end up close together. This is the foundation of all word embedding methods.
Context Windows¶
To make this concrete, we define a context window — a fixed-size region around a target word. For a window size of 2, the contexts of “sat” in “the cat sat on the mat” would be {the, cat, on, the}.
Different words accumulate different context profiles. Words with similar profiles — like “cat” and “dog”, which both appear near “the”, “sat”, “chased”, “on” — end up with similar representations. Words like “cat” and “algorithm” rarely share contexts, so their representations will be far apart.
Word2Vec: Learning Embeddings from Context¶
Word2Vec, introduced by Mikolov et al. at Google in 2013, was a breakthrough: a simple, fast method for learning high-quality word embeddings from large amounts of text. It comes in two flavors.
Skip-gram: Predict Context from Word¶
The Skip-gram model asks: given a target word, can we predict the words that appear nearby?
For example, given the sentence “the cat sat on the mat” with window size 2 and target word “sat”:
Training pairs: (sat → the), (sat → cat), (sat → on), (sat → the)
The model learns a vector for each word such that the target word’s vector can predict its context words. Words that predict similar contexts end up with similar vectors.
Concretely, the model has two sets of vectors:
Target embeddings (the ones we ultimately keep)
Context embeddings (used during training, then discarded)
Training adjusts these vectors so that the dot product between a target word and its actual context words is high, while the dot product with random “negative sample” words is low. That’s the entire learning signal — no hand-labeled data required.
CBOW: Predict Word from Context¶
The Continuous Bag of Words (CBOW) model flips the task: given the surrounding context words, predict the target word in the middle.
For the same example with target “sat”:
Input: {the, cat, on, the} → predict “sat”
CBOW averages the context word vectors and uses that average to predict the center word. It tends to be faster to train and works well for frequent words, while Skip-gram performs better on rare words.
What Gets Learned?¶
The remarkable thing is what emerges from this simple training process. The model isn’t told that “cat” and “dog” are both animals — it discovers this from the fact that they appear in similar contexts. It isn’t told that “Paris” is to “France” as “Tokyo” is to “Japan” — but this relationship gets encoded in the geometry of the learned vectors.
We don’t need to train Word2Vec ourselves to use it. Pre-trained vectors, learned on billions of words of text, are freely available and capture a rich model of word meaning. Let’s load some up and explore.
Loading Pre-trained Embeddings¶
There are two main ways we’ll work with pre-trained word vectors: through SpaCy’s built-in vectors and through Gensim’s KeyedVectors.
SpaCy: Vectors Built Into the Pipeline¶
SpaCy’s medium and large models come with 300-dimensional word vectors built in. Let’s load one up.
import spacy
# The medium model ("md") includes word vectors
# If you need to install: python -m spacy download en_core_web_md
nlp = spacy.load("en_core_web_md")
# Every token has a vector
doc = nlp("The cat sat on the mat")
token = doc[1] # "cat"
print(f"Token: '{token.text}'")
print(f"Vector shape: {token.vector.shape}")
print(f"First 10 values: {token.vector[:10].round(4)}")
print(f"Has vector: {token.has_vector}")Token: 'cat'
Vector shape: (300,)
First 10 values: [-0.7248 0.4254 0.0255 -0.3981 0.0375 -0.2981 -0.2828 0.2933 0.5778
1.2205]
Has vector: True
SpaCy makes similarity comparisons easy — you can compare tokens, spans, or entire documents:
# Token-level similarity
cat = nlp("cat")
dog = nlp("dog")
banana = nlp("banana")
algorithm = nlp("algorithm")
print("Similarity comparisons:")
print(f" cat ↔ dog: {cat.similarity(dog):.3f}")
print(f" cat ↔ banana: {cat.similarity(banana):.3f}")
print(f" cat ↔ algorithm: {cat.similarity(algorithm):.3f}")Similarity comparisons:
cat ↔ dog: 1.000
cat ↔ banana: 0.233
cat ↔ algorithm: 0.056
“Cat” and “dog” are more similar to each other than either is to “banana” or “algorithm”. The embeddings have captured something about the structure of meaning — and we never told the model that cats and dogs are both animals.
Now let’s revisit our synonym problem from the opening:
happy = nlp("happy")
glad = nlp("glad")
joyful = nlp("joyful")
sad = nlp("sad")
print("Synonym similarity:")
print(f" happy ↔ glad: {happy.similarity(glad):.3f}")
print(f" happy ↔ joyful: {happy.similarity(joyful):.3f}")
print(f" happy ↔ sad: {happy.similarity(sad):.3f}")Synonym similarity:
happy ↔ glad: 0.446
happy ↔ joyful: 0.734
happy ↔ sad: 0.480
With dense representations, synonyms actually look similar — the exact problem that sparse representations couldn’t solve.
Gensim: Dedicated Embedding Library¶
For deeper exploration of word vectors, Gensim provides more control. Let’s load a set of pre-trained vectors.
import gensim.downloader as api
# Load pre-trained GloVe vectors (this may take a moment the first time)
# These are 100-dimensional vectors trained on Wikipedia + Gigaword
model = api.load("glove-wiki-gigaword-100")
print(f"Vocabulary size: {len(model.key_to_index):,}")
print(f"Vector dimensionality: {model.vector_size}")[--------------------------------------------------] 0.0% 0.0/128.1MB downloaded[--------------------------------------------------] 0.0% 0.0/128.1MB downloaded[--------------------------------------------------] 0.0% 0.0/128.1MB downloaded[--------------------------------------------------] 0.0% 0.0/128.1MB downloaded[--------------------------------------------------] 0.0% 0.0/128.1MB downloaded[--------------------------------------------------] 0.0% 0.0/128.1MB downloaded[--------------------------------------------------] 0.0% 0.0/128.1MB downloaded[--------------------------------------------------] 0.0% 0.1/128.1MB downloaded[--------------------------------------------------] 0.0% 0.1/128.1MB downloaded[--------------------------------------------------] 0.1% 0.1/128.1MB downloaded[--------------------------------------------------] 0.1% 0.1/128.1MB downloaded[--------------------------------------------------] 0.1% 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downloaded[==------------------------------------------------] 5.2% 6.7/128.1MB downloaded[==------------------------------------------------] 5.2% 6.7/128.1MB downloaded[==------------------------------------------------] 5.2% 6.7/128.1MB downloaded[==------------------------------------------------] 5.2% 6.7/128.1MB downloaded[==------------------------------------------------] 5.2% 6.7/128.1MB downloaded[==------------------------------------------------] 5.2% 6.7/128.1MB downloaded[==------------------------------------------------] 5.2% 6.7/128.1MB downloaded[==------------------------------------------------] 5.2% 6.7/128.1MB downloaded[==------------------------------------------------] 5.2% 6.7/128.1MB downloaded[==------------------------------------------------] 5.3% 6.7/128.1MB downloaded[==------------------------------------------------] 5.3% 6.7/128.1MB downloaded[==------------------------------------------------] 5.3% 6.7/128.1MB downloaded[==------------------------------------------------] 5.3% 6.8/128.1MB downloaded[==------------------------------------------------] 5.3% 6.8/128.1MB downloaded[==------------------------------------------------] 5.3% 6.8/128.1MB downloaded[==------------------------------------------------] 5.3% 6.8/128.1MB downloaded[==------------------------------------------------] 5.3% 6.8/128.1MB downloaded[==------------------------------------------------] 5.3% 6.8/128.1MB downloaded[==------------------------------------------------] 5.3% 6.8/128.1MB downloaded[==------------------------------------------------] 5.3% 6.8/128.1MB downloaded[==------------------------------------------------] 5.3% 6.8/128.1MB downloaded[==------------------------------------------------] 5.3% 6.8/128.1MB downloaded[==------------------------------------------------] 5.3% 6.8/128.1MB downloaded[==------------------------------------------------] 5.3% 6.8/128.1MB 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downloaded[===-----------------------------------------------] 6.4% 8.2/128.1MB downloaded[===-----------------------------------------------] 6.4% 8.2/128.1MB downloaded[===-----------------------------------------------] 6.4% 8.2/128.1MB downloaded[===-----------------------------------------------] 6.4% 8.2/128.1MB downloaded[===-----------------------------------------------] 6.4% 8.2/128.1MB downloaded[===-----------------------------------------------] 6.4% 8.2/128.1MB downloaded[===-----------------------------------------------] 6.4% 8.2/128.1MB downloaded[===-----------------------------------------------] 6.4% 8.2/128.1MB downloaded[===-----------------------------------------------] 6.4% 8.2/128.1MB downloaded[===-----------------------------------------------] 6.4% 8.2/128.1MB downloaded[===-----------------------------------------------] 6.4% 8.2/128.1MB downloaded[===-----------------------------------------------] 6.4% 8.2/128.1MB 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downloaded[=========-----------------------------------------] 18.2% 23.3/128.1MB downloaded[=========-----------------------------------------] 18.2% 23.4/128.1MB downloaded[=========-----------------------------------------] 18.2% 23.4/128.1MB downloaded[=========-----------------------------------------] 18.2% 23.4/128.1MB downloaded[=========-----------------------------------------] 18.3% 23.4/128.1MB downloaded[=========-----------------------------------------] 18.3% 23.4/128.1MB downloaded[=========-----------------------------------------] 18.3% 23.4/128.1MB downloaded[=========-----------------------------------------] 18.3% 23.4/128.1MB downloaded[=========-----------------------------------------] 18.3% 23.4/128.1MB downloaded[=========-----------------------------------------] 18.3% 23.4/128.1MB downloaded[=========-----------------------------------------] 18.3% 23.4/128.1MB downloaded[=========-----------------------------------------] 18.3% 23.4/128.1MB downloadedIOPub message rate exceeded.
The Jupyter server will temporarily stop sending output
to the client in order to avoid crashing it.
To change this limit, set the config variable
`--ServerApp.iopub_msg_rate_limit`.
Current values:
ServerApp.iopub_msg_rate_limit=1000.0 (msgs/sec)
ServerApp.rate_limit_window=3.0 (secs)
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message rate exceeded.
The Jupyter server will temporarily stop sending output
to the client in order to avoid crashing it.
To change this limit, set the config variable
`--ServerApp.iopub_msg_rate_limit`.
Current values:
ServerApp.iopub_msg_rate_limit=1000.0 (msgs/sec)
ServerApp.rate_limit_window=3.0 (secs)
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downloadedIOPub message rate exceeded.
The Jupyter server will temporarily stop sending output
to the client in order to avoid crashing it.
To change this limit, set the config variable
`--ServerApp.iopub_msg_rate_limit`.
Current values:
ServerApp.iopub_msg_rate_limit=1000.0 (msgs/sec)
ServerApp.rate_limit_window=3.0 (secs)
Vocabulary size: 400,000
Vector dimensionality: 100
Gensim gives us powerful tools for querying the embedding space:
# Find the most similar words to a target
print("Most similar to 'king':")
for word, score in model.most_similar("king", topn=5):
print(f" {word}: {score:.3f}")Most similar to 'king':
prince: 0.768
queen: 0.751
son: 0.702
brother: 0.699
monarch: 0.698
# Compare specific pairs
pairs = [("happy", "glad"), ("happy", "sad"), ("cat", "dog"), ("cat", "car")]
print("Pairwise similarity:")
for w1, w2 in pairs:
print(f" {w1} ↔ {w2}: {model.similarity(w1, w2):.3f}")Pairwise similarity:
happy ↔ glad: 0.783
happy ↔ sad: 0.680
cat ↔ dog: 0.880
cat ↔ car: 0.311
Embedding Arithmetic: The Geometry of Meaning¶
Perhaps the most famous property of word embeddings is that they support vector arithmetic — algebraic operations on word vectors that produce semantically meaningful results.
The canonical example:
This works because the vector from “man” to “king” captures something like “royalty”, and adding that same offset to “woman” lands us near “queen”. The relationship isn’t stored as a rule — it emerges from the geometry of the learned space.
Let’s verify this with real vectors:
# The famous analogy: king - man + woman ≈ ?
result = model.most_similar(positive=["king", "woman"], negative=["man"], topn=3)
print("king - man + woman ≈")
for word, score in result:
print(f" {word}: {score:.3f}")king - man + woman ≈
queen: 0.770
monarch: 0.684
throne: 0.676
# More analogies
analogies = [
("paris", "france", "tokyo", "country → capital"),
("slow", "slower", "fast", "comparative form"),
("man", "doctor", "woman", "gender in professions"),
]
for w1, w2, w3, label in analogies:
# w2 - w1 + w3 = ?
result = model.most_similar(positive=[w2, w3], negative=[w1], topn=1)
answer, score = result[0]
print(f"{label}: {w2} - {w1} + {w3} ≈ {answer} ({score:.3f})")country → capital: france - paris + tokyo ≈ japan (0.886)
comparative form: slower - slow + fast ≈ faster (0.799)
gender in professions: doctor - man + woman ≈ nurse (0.774)
Why Does This Work?¶
The analogy task reveals that word embeddings encode relational structure as consistent vector offsets. The “gender” direction, the “royalty” direction, the “country-capital” direction — these are all approximately linear in the embedding space.
Mathematically, if we define the relationship vector , then for any pair that shares the same relationship, we expect .
This is quite remarkable: a model trained only to predict which words appear near each other has learned structured knowledge about the world.
Cosine Similarity: The Standard Metric¶
You may have noticed we keep using “similarity” scores. The standard metric for comparing word vectors is cosine similarity:
Cosine similarity measures the angle between two vectors, ignoring their magnitudes. It ranges from -1 (opposite directions) to 1 (same direction), with 0 meaning orthogonal (unrelated). For word embeddings, values typically fall between 0.1 and 0.9.
Why cosine rather than Euclidean distance? Because we care about the direction of a word’s vector (which encodes its meaning) rather than its magnitude (which can reflect word frequency or other artifacts).
GloVe: A Different Path to the Same Destination¶
Word2Vec learns embeddings by scanning through text with a sliding window — a predictive approach. GloVe (Global Vectors for Word Representation), developed by Pennington et al. at Stanford in 2014, takes a different route: it starts from global co-occurrence statistics.
The Co-occurrence Matrix¶
GloVe begins by constructing a matrix where entry counts how often word appears in the context of word across the entire corpus. This matrix captures the same distributional information that Word2Vec uses, but all at once rather than through sequential scanning.
The key insight of GloVe is that ratios of co-occurrence probabilities are more informative than raw counts. Consider two words: “ice” and “steam”. The word “solid” co-occurs much more with “ice” than “steam”, while “gas” co-occurs more with “steam”. But a neutral word like “water” co-occurs roughly equally with both. GloVe’s training objective is designed to preserve these ratios in the learned vectors.
Predictive vs. Count-Based¶
| Aspect | Word2Vec | GloVe |
|---|---|---|
| Approach | Predictive (neural) | Count-based (matrix factorization) |
| Input | Local context windows | Global co-occurrence matrix |
| Training | Stochastic, online | Batch optimization |
| Strengths | Scales well, good on rare words (Skip-gram) | Captures global statistics efficiently |
In practice, the two methods produce embeddings of comparable quality. The Gensim vectors we loaded earlier are actually GloVe vectors — and we used them alongside SpaCy’s Word2Vec-style vectors without any change in how we query them. This is an important point: the downstream interface is the same regardless of how the embeddings were trained. Similarity, arithmetic, nearest-neighbor queries — they all work identically.
Limitations of Static Embeddings¶
Word embeddings are a massive leap over sparse representations, but they have their own limitations. Understanding these helps us appreciate what comes later in the course.
One Vector Per Word (The Polysemy Problem)¶
Consider the word “bank”:
# "bank" has one vector — but multiple meanings
bank_words = ["bank", "river", "money", "financial", "riverbank"]
print("Similarity to 'bank':")
for w in bank_words[1:]:
print(f" bank ↔ {w}: {model.similarity('bank', w):.3f}")Similarity to 'bank':
bank ↔ river: 0.334
bank ↔ money: 0.572
bank ↔ financial: 0.678
bank ↔ riverbank: 0.055
The vector for “bank” is a single point — a compromise between its “financial institution” sense and its “river bank” sense. It can’t distinguish “I deposited money at the bank” from “I sat by the river bank.” This is the polysemy problem, and it’s a fundamental limitation of static embeddings.
The solution? dense representations that are contextual — where the vector for “bank” is different depending on the surrounding words. We’ll meet these in Week 7 when we study BERT and other transformer-based models.
Fixed Vocabulary¶
Static embeddings have a fixed vocabulary determined at training time. Words not in the vocabulary — misspellings, neologisms, domain-specific jargon — simply have no representation. This is the same out-of-vocabulary (OOV) problem we mentioned in the context of sparse representations, and it motivated the development of subword tokenization methods like BPE, which we’ll explore in the lab.
Encoding Bias¶
Because embeddings are learned from real text, they absorb the biases present in that text. Gender stereotypes, racial biases, and cultural assumptions all get encoded in the vector space. The same arithmetic that gives us king - man + woman ≈ queen can also produce troubling associations. We’ll examine this more carefully in Week 14 when we discuss ethics and bias in NLP systems.
Wrap-Up¶
Key Takeaways¶
What’s Next¶
In the next session, we’ll put these representations to work. We’ll explore subword tokenization (BPE, WordPiece, SentencePiece) and how it handles out-of-vocabulary words, build sentence and document embeddings, and then stage a head-to-head showdown: sparse vs. dense representations on a real dataset. Which representation wins — and when does the extra complexity of embeddings actually pay off?