Why spaCy Word Vectors Show Unexpected Similar Words

Zartom
2 days ago
12 min read

When working with spaCy's pre-trained word vectors, you might encounter results that seem semantically unexpected. This isn't necessarily an error, but rather a reflection of how these vectors are trained. They capture statistical patterns from vast amounts of text, meaning words that frequently appear in similar contexts, even if their literal definitions differ, can end up with similar vector representations. Understanding this underlying mechanism is key to interpreting the outputs correctly and leveraging word embeddings effectively for your NLP tasks.

This lesson addresses a common issue encountered when using spaCy's pre-trained word vectors: obtaining semantically unexpected similar words. We will delve into the underlying reasons for these discrepancies and provide strategies to interpret and potentially improve the results. Understanding how word embeddings are trained and how they represent meaning is crucial for effective natural language processing tasks.

The spaCy Word Vector Similarity Puzzle

Users often report that spaCy's pre-trained word vectors, particularly those from models like en_core_web_md, return similar words that seem semantically disconnected from the query word. For instance, asking for words similar to 'country' might yield terms like 'anti-poverty', 'SLUMS', or 'inner-city', which don't immediately align with the intuitive understanding of 'country'. This phenomenon can be puzzling and lead to incorrect assumptions about the model's capabilities or the data it was trained on.

The core of the problem lies in the nature of word embeddings themselves. These vectors are learned from massive text corpora by capturing statistical co-occurrence patterns. Words that appear in similar contexts, even if their literal meanings are different, can end up with similar vector representations. This is a powerful feature for uncovering latent semantic relationships but can also lead to results that appear counter-intuitive at first glance.

Investigating the Training Data Context

The specific training corpus used for a spaCy model significantly influences the resulting word vectors. Models like en_core_web_md are trained on large, general-purpose datasets (e.g., web text, news articles). If the word 'country' frequently appears in texts discussing social issues, urban poverty, or specific policy initiatives related to disadvantaged areas, its vector will naturally absorb some of that contextual meaning.

For example, if news articles frequently discuss 'country' in the context of national policies aimed at combating 'anti-poverty' programs or addressing 'inner-city' issues, the vector for 'country' will likely be pushed towards the vectors of these related terms. This is not an error but a reflection of the statistical patterns present in the training data.

The Nature of Vector Space Representation

Word vectors exist in a high-dimensional space where proximity indicates semantic similarity. However, 'semantic similarity' in this context is defined by co-occurrence and contextual usage, not necessarily by dictionary definitions or human intuition alone. Words can be similar in one aspect (e.g., discussing social policy) but dissimilar in another (e.g., geographical concept).

The 'unexpected' similarities often arise when the model captures a specific, perhaps less common, contextual relationship from the training data. For instance, 'country' might be associated with national-level social welfare discussions, leading to its vector being close to terms related to social issues, even if the primary association is geographical.

Strategies for Interpreting spaCy Word Vector Similarity

To make sense of these results, it's essential to consider the context in which the query word appears in the training data. We can also experiment with different pre-trained models or fine-tune vectors on domain-specific data.

Leveraging Contextual Information

When a word vector similarity search yields surprising results, the first step is to examine the corpus from which the vectors were derived. If you have access to the training data or a representative sample, search for instances of your query word. Understanding how 'country' is used in the specific corpus that generated the en_core_web_md vectors will illuminate why terms related to social issues appear as similar.

For instance, if the text primarily discusses national economic policies, social programs, and urban development, the word 'country' will be embedded within this semantic field. This is a common occurrence in NLP, where word meanings are heavily influenced by their usage context.

Exploring Different Pre-trained Models

SpaCy offers various pre-trained models, each trained on different datasets or with different architectures. Some models might be trained on more diverse or specialized corpora, potentially yielding different similarity results. For example, a model trained on a purely geographical dataset might associate 'country' more closely with 'nation', 'state', or 'region'.

It's also worth noting that the size of the vectors (e.g., md vs. lg models) and the training methodology can impact the nuances of semantic representation. Experimenting with en_core_web_lg, which has larger vectors and was trained on more data, might offer different insights into the similarity landscape.

Analyzing the spaCy Word Vector Code

The provided Python code snippet uses spaCy's en_core_web_md model to find words similar to a given query word. The process involves loading the model, processing a text document, and then querying the vocabulary vectors.

Code Breakdown and Functionality

The code initializes spaCy, loads the medium-sized English model (en_core_web_md), which includes word vectors. It then reads a text file ('data/us.txt'), processes it into a spaCy Doc object, and extracts the first sentence. The core operation is finding the most similar words to 'country' using nlp.vocab.vectors.most_similar. This function takes the vector of the target word (obtained via nlp.vocab.vectors[nlp.vocab.strings[your_word]]) and returns the top N similar words and their distances.

The output shows that for 'country', the similar words are ['anti-poverty', 'SLUMS', 'inner-city', 'Socioeconomic', 'INTERSECT', 'Divides', 'handicaps', 'dropout', 'drop-out', 'Crime-Ridden']. This indicates that in the context of the 'us.txt' file and the en_core_web_md model's training data, 'country' has strong contextual associations with socio-economic issues and urban environments.

Interpreting the Output

The consistent output across different runs, regardless of the input word, suggests that the 'us.txt' file likely contains a strong thematic focus on social issues, urban poverty, or related topics. When you query for 'country', the model is finding words that co-occurred frequently with 'country' within this specific text or the broader training corpus that influenced the 'us.txt' processing. This demonstrates how the context of the input text can influence the interpretation of word vector similarities.

If the 'us.txt' file itself is heavily focused on these themes, then the results are not unexpected for that specific context. However, if you expected geographical or political similarities, it highlights the importance of understanding the training data's biases and the specific corpus being analyzed.

Why spaCy Word Vectors Show Unexpected Similarities

The unexpected similarities arise from how word vectors are trained: they capture statistical relationships from large text corpora. Words appearing in similar contexts, regardless of their literal meanings, will have similar vectors.

Contextual Embeddings vs. Static Embeddings

SpaCy's en_core_web_md and en_core_web_lg models use static word embeddings. This means each word has a single, fixed vector representation, irrespective of its context in a given sentence. In contrast, contextual embedding models (like BERT or ELMo) generate word vectors that change based on the surrounding words. The static nature of spaCy's vectors means they represent an average of all contexts in which a word appeared during training.

Therefore, if 'country' frequently appeared in discussions about national social policies or urban development in the training data, its static vector will reflect these associations, leading to similarities with terms like 'slums' or 'anti-poverty'. This is a feature, not a bug, reflecting the data's statistical properties.

Corpus Bias and Domain Specificity

The training corpus for en_core_web_md, while large, may have biases or a particular thematic focus that influences word associations. If the corpus disproportionately features texts discussing social inequality, urban issues, or specific national policies, words like 'country' might become contextually linked to these themes.

To get results aligned with your specific needs (e.g., geographical or political similarities), you might need to fine-tune the word vectors on a domain-specific corpus that better represents your intended usage. Alternatively, using a different pre-trained model that was trained on a corpus more aligned with your desired semantic space could yield different results.

Practical Solutions for spaCy Word Vector Issues

To address the unexpected similarities, consider fine-tuning models, using different pre-trained embeddings, or analyzing the context of your input data.

Fine-tuning spaCy Word Vectors

If you have a specific domain or task where 'country' should relate to geographical or political entities, fine-tuning spaCy's vectors on a relevant corpus is a powerful approach. This involves training the existing vectors on your custom data, allowing them to adapt to the specific semantic patterns of your domain. This process can help override or de-emphasize less relevant associations learned from general corpora.

The process typically involves using libraries like Gensim or spaCy's own training utilities. You would load the pre-trained vectors and then train them further on your domain-specific text data, adjusting parameters like learning rate and the number of epochs to achieve optimal results for your task.

Alternative Pre-trained Models

Explore other pre-trained word embedding models available for spaCy or other NLP libraries. Models trained on different corpora (e.g., Wikipedia dumps, news archives, scientific papers) will have different strengths and biases. For instance, a model trained on a corpus focused on international relations might associate 'country' more with 'sovereignty', 'borders', or 'diplomacy'.

Additionally, consider larger models like en_core_web_lg. These models often have more dimensions and are trained on more extensive data, potentially leading to more nuanced and comprehensive semantic representations. Comparing results from different models is a key part of diagnosing and resolving unexpected similarity outputs.

Contextualizing Input Data

Always consider the nature of the text you are processing. If your input document 'data/us.txt' is itself heavily focused on social issues, the word vector similarities will naturally reflect that context. Understanding your data's thematic focus is as important as understanding the word vectors.

If you are using spaCy for a specific application, ensure that the input text aligns with the semantic expectations of your task. If not, preprocessing or selecting different input data might be necessary before applying word vector analysis.

Worked Examples: Testing Different Queries

Let's test the en_core_web_md vectors with a few more words to observe the patterns and understand the contextual influences.

Querying for 'government'

If we query for 'government', we might expect words like 'state', 'policy', 'nation', or 'administration'. However, depending on the corpus, we might also see terms related to specific governmental functions or social impacts.

For example, if the 'us.txt' data frequently discusses government programs for poverty reduction, 'government' might show similarities to terms like 'welfare', 'aid', or 'social services'. This reinforces the idea that context is king in word embeddings.

Querying for 'city'

A query for 'city' might typically yield words like 'urban', 'metropolis', 'town', or 'municipality'. If the corpus emphasizes urban challenges, 'city' could appear alongside terms like 'neighborhood', 'district', 'poverty', or 'infrastructure'.

The specific results depend heavily on the statistical co-occurrences captured by the model. Analyzing these results critically requires an understanding of both the word embedding space and the characteristics of the text data being analyzed.

Key Takeaways: Understanding spaCy Word Vector Similarity

The unexpected similar words from spaCy word vectors are a result of their training on statistical co-occurrences in large text corpora, reflecting contextual usage rather than just dictionary definitions. The en_core_web_md model, like others, embeds words based on these patterns.

To manage these results: examine your input data's context, consider alternative pre-trained models (like en_core_web_lg), or fine-tune vectors on domain-specific corpora for more targeted semantic relationships.

Additional Code Illustrations for spaCy Word Vectors

These examples demonstrate alternative ways to work with spaCy's word vectors and related concepts.

Loading and Inspecting Vectors

import spacy

# Load a model with vectors
nlp = spacy.load('en_core_web_md')

# Check if vectors are available
if nlp.vocab.vectors.size > 0:
    print(f"Model has {nlp.vocab.vectors.shape[0]} vectors.\n")
    # Get vector for a specific word
    word = "king"
    if word in nlp.vocab:
        vector = nlp.vocab.vectors[nlp.vocab.strings[word]]
        print(f"Vector for '{word}': {vector[:5]}...") # Print first 5 dimensions
    else:
        print(f"'{word}' not found in vocabulary.")
else:
    print("No vectors found in this model.")

This code snippet shows how to load a spaCy model that includes vectors and how to access the vector for a specific word, printing a portion of it.

Finding Similar Words with Different Models

import spacy

nlp_lg = spacy.load('en_core_web_lg') # Load a larger model

word = "country"

# Get similar words from the medium model
if word in nlp.vocab:
    ms_md = nlp.vocab.vectors.most_similar(
        np.asarray([nlp.vocab.vectors[nlp.vocab.strings[word]]]), n=5
    )
    words_md = [nlp.vocab.strings[w] for w in ms_md[0][0]]
    print(f"Similar to '{word}' (md): {words_md}")

# Get similar words from the large model
if word in nlp_lg.vocab:
    ms_lg = nlp_lg.vocab.vectors.most_similar(
        np.asarray([nlp_lg.vocab.vectors[nlp_lg.vocab.strings[word]]]), n=5
    )
    words_lg = [nlp_lg.vocab.strings[w] for w in ms_lg[0][0]]
    print(f"Similar to '{word}' (lg): {words_lg}")
else:
    print(f"'{word}' not found in lg model vocabulary.")

This example compares the similarity results for 'country' using both the medium (en_core_web_md) and large (en_core_web_lg) spaCy models, highlighting potential differences in vector representations.

Calculating Cosine Similarity Manually

import spacy
from numpy.linalg import norm

nlp = spacy.load('en_core_web_md')

word1 = "country"
word2 = "nation"

vec1 = nlp.vocab.vectors[nlp.vocab.strings[word1]]
vec2 = nlp.vocab.vectors[nlp.vocab.strings[word2]]

# Cosine similarity = dot(a, b) / (norm(a) * norm(b))
cosine_sim = np.dot(vec1, vec2) / (norm(vec1) * norm(vec2))

print(f"Cosine similarity between '{word1}' and '{word2}': {cosine_sim:.4f}")

This code demonstrates how to manually calculate the cosine similarity between two word vectors using NumPy, providing a fundamental understanding of how vector similarity is quantified.

Finding Similarities within a Processed Document

import spacy

nlp = spacy.load('en_core_web_md')
text = "The United States is a vast country with diverse regions."
doc = nlp(text)

# Find the vector for 'country'
country_token = [token for token in doc if token.text.lower() == 'country'][0]

# Find most similar tokens in the document
if country_token.has_vector:
    similar_tokens = country_token.similarity(doc, n=3)
    print(f"Tokens in the document most similar to '{country_token.text}':")
    for token, similarity in similar_tokens:
        print(f"- {token.text} (similarity: {similarity:.4f})")
else:
    print("'country' token does not have a vector.")

This example shows how to find words within a specific document that are most similar to a target word ('country' in this case), using spaCy's built-in similarity methods on document tokens.

Handling Words Not in Vocabulary

import spacy

nlp = spacy.load('en_core_web_md')

word_in_vocab = "president"
word_out_vocab = "prezident"

# Check if words are in vocabulary and get vectors
if word_in_vocab in nlp.vocab:
    vec_in = nlp.vocab.vectors[nlp.vocab.strings[word_in_vocab]]
    print(f"'{word_in_vocab}' has vector of shape: {vec_in.shape}")
else:
    print(f"'{word_in_vocab}' not in vocabulary.")

if word_out_vocab in nlp.vocab:
    vec_out = nlp.vocab.vectors[nlp.vocab.strings[word_out_vocab]]
    print(f"'{word_out_vocab}' has vector of shape: {vec_out.shape}")
else:
    print(f"'{word_out_vocab}' not in vocabulary. spaCy uses zero vectors for OOV words.")
    # spaCy's default for OOV is a zero vector, or a vector derived from subwords if available

This illustrates how spaCy handles words not present in its vocabulary. Typically, Out-of-Vocabulary (OOV) words receive a zero vector or a vector derived from subword information if the model supports it, which affects similarity calculations.

Concept	Explanation	Implication for spaCy Vectors
Statistical Co-occurrence	Word vectors are trained by analyzing which words appear together frequently in large text datasets.	Words like 'country' might be associated with social issues if they often appear in texts discussing national policies or urban development.
Contextual Meaning	The meaning of a word is heavily influenced by its usage context within a corpus.	SpaCy's static vectors represent an average of all contexts, potentially highlighting less common but statistically significant associations.
Training Corpus Bias	The specific data used to train the vectors can introduce biases or thematic focuses.	A corpus with a focus on social issues might lead 'country' to be similar to terms related to poverty or urban areas.
Static vs. Contextual Embeddings	Static embeddings (like spaCy's md/lg) assign a single vector per word, unlike contextual models (BERT, ELMo).	The fixed nature of spaCy vectors means they capture general associations, which might not always align with a specific sentence's context.
Mitigation Strategies	Fine-tuning, using different models, or analyzing input data context.	To achieve domain-specific similarities, consider fine-tuning on relevant data or switching to models trained on different corpora.