Introduction

A Word2Vec model does not understand raw bigrams automatically. If you want similarity for phrases such as new_york or machine_learning, you usually detect frequent bigrams first, convert them into single tokens, and then train Word2Vec on that transformed corpus. After that, bigram similarity works the same way as word similarity.

Detect bigrams before training Word2Vec

In Gensim, the usual workflow is to use Phrases or Phraser to merge common two-word phrases into one token.

1from gensim.models import Phrases
2from gensim.models.phrases import Phraser
3
4sentences = [
5    ["new", "york", "is", "busy"],
6    ["machine", "learning", "is", "useful"],
7    ["new", "york", "city", "is", "large"],
8]
9
10phrases = Phrases(sentences, min_count=1, threshold=1)
11bigram = Phraser(phrases)
12
13transformed = [bigram[sentence] for sentence in sentences]
14print(transformed)

Typical output will include tokens such as new_york and machine_learning when the phrase detector considers them meaningful.

Train Word2Vec on the transformed corpus

Once the phrases are joined, train Word2Vec on the transformed tokens rather than the original separated words.

1from gensim.models import Word2Vec
2
3model = Word2Vec(
4    sentences=transformed,
5    vector_size=50,
6    window=3,
7    min_count=1,
8    workers=1,
9    epochs=100,
10)

At this point, a bigram is just another token in the model vocabulary.

Query similarity using the merged bigram token

If the phrase made it into the vocabulary, you can query it with the underscore form.

print(model.wv.similarity("new_york", "machine_learning"))
print(model.wv.most_similar("new_york", topn=5))

This is the simplest and most reliable way to get phrase-level similarity in a Word2Vec model.

Check whether the bigram exists in the vocabulary

A common mistake is asking for similarity on a bigram that was never learned as a token. Always check the vocabulary first.

print("new_york" in model.wv.key_to_index)
print("machine_learning" in model.wv.key_to_index)

If the token is missing, either the phrase detector never merged it or the Word2Vec training filtered it out because of min_count.

Approximate a bigram vector when no merged token exists

Sometimes you already trained Word2Vec on plain tokens and do not want to retrain. In that case, you can approximate a phrase vector by averaging the component word vectors.

1import numpy as np
2
3phrase_vector = (model.wv["new"] + model.wv["york"]) / 2.0
4similarity = np.dot(phrase_vector, model.wv["city"]) / (
5    np.linalg.norm(phrase_vector) * np.linalg.norm(model.wv["city"])
6)
7print(similarity)

This is a fallback, not a perfect substitute for true phrase training. It ignores the fact that the phrase may carry meaning that differs from the sum of its parts.

Phrase quality depends on corpus and thresholds

Phrase detection is only as good as the corpus and its parameters. If threshold is too high, useful phrases will not be merged. If it is too low, you will create noisy phrase tokens that hurt embedding quality. This means bigram similarity is partly a corpus engineering problem, not just a similarity-query problem.

Common Pitfalls

• Training Word2Vec on plain tokens and then expecting it to know phrase tokens that were never created.
• Forgetting to check whether the bigram token actually exists in the model vocabulary.
• Using phrase-detection thresholds that either miss useful bigrams or create too many noisy ones.
• Treating averaged unigram vectors as equivalent to a properly trained phrase embedding.
• Interpreting similarity scores without enough corpus quality or phrase frequency.

Summary

• Detect and merge bigrams before Word2Vec training if you want real phrase embeddings.
• Query bigram similarity with the merged token such as new_york.
• Check the vocabulary before assuming a phrase exists in the model.
• Average component vectors only as a fallback when retraining is not possible.
• Tune phrase detection carefully because corpus quality drives phrase similarity quality.