NLP中MWE上下文相似度计算：余弦与欧氏相似度选型问询

Hey there! Let's dive into the best algorithm choices for calculating contextual similarity of multi-word expressions (MWEs) like "put up"—I've worked through a lot of similar NLP problems, so here's a breakdown tailored to your needs:

1. Cosine Similarity (Baseline & Go-To Choice) #

From a mathematical standpoint, cosine similarity is ideal for this scenario because it measures the cosine of the angle between two vectors, which perfectly aligns with how we represent sparse text/context data. Here's how to apply it:

• Convert each word in the MWE's left/right context into pre-trained word vectors (e.g., Word2Vec, GloVe).
• Aggregate these vectors (average, weighted sum, or max pooling) to get a single vector representing the entire context. For MWEs, you can even include the MWE's own combined vector (since phrases like "put up" have unique semantics beyond individual words).
• Compute the cosine similarity between the two context vectors using the formula:
  similarity = (A · B) / (||A|| * ||B||)

Pros: Fast to compute, highly interpretable, works great for large-scale datasets. If your context lengths are consistent, it'll deliver reliable results.
Pro Tip: Weight words that are semantically tied to the MWE (e.g., "tent" for "put up" meaning "build") more heavily to avoid dilution from irrelevant terms.

2. Transformer-Based Semantic Similarity (Advanced, High-Performance) #

If you need to handle nuanced MWE ambiguity (like "put up" meaning "host someone" vs. "erect a structure"), pre-trained Transformers (BERT, RoBERTa, or DistilBERT for efficiency) are your best bet.

• Feed the full context snippet (including the MWE) into the model, then extract either the pooled vector of the entire context or the token-level vectors for the MWE itself.
• Calculate similarity using cosine distance between these vectors, or use a fine-tuned similarity head directly from the model.

Mathematically, Transformers use self-attention to model dependencies between the MWE and surrounding words, capturing subtle semantic relationships that traditional word vectors miss.

Pros: Outperforms baseline methods on ambiguous or complex contexts; can be fine-tuned on domain-specific MWE data for even better results.
Cons: Higher computational cost than cosine similarity; requires more compute resources, though lighter models like DistilBERT mitigate this.

3. Jaccard Similarity (Quick Baseline for Simple Cases) #

If you need a zero-training, ultra-fast baseline (or your contexts have heavy lexical overlap), Jaccard similarity works. It calculates the ratio of shared words between two context sets:
Jaccard(A,B) = |A ∩ B| / |A ∪ B|

Pros: No setup required, instant results. Great for initial filtering or resource-constrained environments.
Cons: Completely ignores semantic similarity—two contexts with synonymous words (e.g., ["build", "house"] vs. ["construct", "home"]) will have a Jaccard score of 0, even though their meaning is nearly identical.

Practical Recommendation #

Start with cosine similarity + pre-trained word vectors as your baseline—it's a solid, reliable starting point. If you find it's missing nuance (like failing to distinguish MWE senses), move to a Transformer model. Jaccard is only useful for quick sanity checks, not production-grade results.

A key note for MWEs: Always include the MWE's own vector in your context representation—phrases have unique semantics that don't just come from adding their individual word vectors.

内容的提问来源于stack exchange，提问作者Logan