基于Word2Vec、GloVe等词向量的文档向量化常用方法咨询

Great question! Since you already know Doc2Vec, let's dive into other practical, widely-used approaches to convert documents into dense vectors using pre-trained word embeddings like Word2Vec or GloVe. Here are the most common methods, along with their pros and use cases:

1. Simple Mean Pooling (Averaging) #

This is the most straightforward method. You take every word in the document, fetch its pre-trained embedding, then compute the element-wise average of all these vectors. For example, if your document has words with vectors v1, v2, v3, the document vector becomes (v1 + v2 + v3) / 3.

• Pros: Extremely fast, easy to implement, works well for quick prototyping.
• Cons: Ignores word order, frequency, and importance—all words are treated equally.
• Pro tip: Always filter out stopwords (like "the", "and") first; their embeddings add little meaningful signal and can dilute the document's core features.

2. TF-IDF Weighted Averaging #

A step up from simple averaging, this method weights each word's embedding by its TF-IDF score (Term Frequency-Inverse Document Frequency). TF-IDF measures how important a word is to a document relative to a corpus: words that are frequent in the document but rare across the corpus get higher weights.

The formula looks like this:
document_vector = sum(w_i * v_i) / sum(w_i)
where w_i is the TF-IDF score of word i, and v_i is its embedding.

• Pros: Accounts for word importance, significantly better than simple averaging for most tasks.
• Cons: Still ignores word order, and TF-IDF weights are static (not adaptive to the document's specific context).

3. Max Pooling #

Instead of averaging, you take the maximum value across all word embeddings for each dimension. For example, if your embeddings are 100-dimensional, you check the 1st dimension of every word vector in the document, take the highest value, then repeat for the 2nd dimension, and so on until you have a 100-dimensional document vector.

• Pros: Captures the most prominent features in the document (e.g., strong emotional words in sentiment analysis).
• Cons: Throws away most of the document's information—only the extreme values are retained.

4. Concatenated Pooling (Mean + Max + Min) #

To get the best of multiple worlds, you can concatenate the results of mean pooling, max pooling, and even min pooling into a single vector. For example, if your embeddings are 100-dimensional, this would give you a 300-dimensional document vector (100 from mean + 100 from max + 100 from min).

• Pros: Combines global trends (from mean) and extreme signals (from max/min), making it a robust choice for many text classification or clustering tasks.
• Cons: Increases the vector dimensionality, which might require more compute for downstream tasks.

5. Attention-Based Weighting #

Unlike static TF-IDF weights, attention mechanisms learn dynamic, context-aware weights for each word in the document. You can build a simple attention layer that takes the sequence of word embeddings as input, computes a weight for each word based on how relevant it is to the document's overall meaning, then averages the embeddings using these learned weights.

For example, in a document about "rabbit chasing", the attention layer might assign higher weights to words like "chase", "rabbits", "lose" than to stopwords or less relevant terms.

• Pros: Adaptively captures the most critical information in a document, often outperforming static weighting methods.
• Cons: Requires training the attention layer on your dataset, which adds a bit more complexity than pooling methods.

6. Graph-Based Document Embeddings #

Treat the document as a graph where nodes are words, and edges represent co-occurrence relationships (how often words appear together). You can then use graph embedding techniques (like Node2Vec) to generate embeddings for each word node, then average these node embeddings to get the document vector. Alternatively, you can first extract key terms using algorithms like TextRank, then average their embeddings.

• Pros: Captures relational patterns between words, which is useful for long documents or technical texts where word relationships matter.
• Cons: More computationally intensive than pooling methods, especially for large documents.

Quick Notes for Implementation #

• Out-of-Vocabulary (OOV) Words: If a word isn't in your pre-trained embedding's vocabulary, you can either use a random initialized vector, the average embedding of all words in your corpus, or simply skip the word (depending on how many OOV words you have).
• Preprocessing: Always standardize your text (lowercase, remove punctuation, tokenize consistently) before generating embeddings—consistency here directly impacts the quality of your document vectors.

内容的提问来源于stack exchange，提问作者Isaac Sim