Core Concepts (AI/ML)
Semantic Representation
In shortA numerical form of text that captures its meaning so machines can compare it, such as a bag of words, a TF-IDF vector or an embedding.
A semantic representation is a form of text, usually numerical, that captures what the text means so a machine can compare, cluster or score it. It can be as simple as a bag of words or a histogram of words and phrases, or as rich as an embedding from a transformer model. MLforSEO meets the term mainly through Google’s information gain patent, where pages are turned into semantic representations before being scored.
How it works
Representations sit on a spectrum:
- Bag of words and histograms count which words or phrases appear, ignoring order.
- TF-IDF vectors weight those counts so words rare across the collection count more.
- Topic distributions, from models like LDA, describe a document as a mix of topics.
- Embeddings from Word2Vec, Doc2Vec or transformer encoders place texts in a space where similar meanings sit close together, even with different words.
- Entity data, the salient entities extracted from a text, is another representation of what it is about.
Once texts are represented the same way, you can measure the distance between them, typically with cosine similarity. In the information gain patent, each page is represented as a semantic feature vector, a model compares documents the user has already viewed with those they have not, and it outputs a score for how much new information a candidate page adds.
How it’s used in SEO and marketing
- Information gain and uniqueness checks against competing pages.
- Clustering keywords and pages by meaning, and topic modelling.
- Internal link suggestions based on how close pages are.
- Near-duplicate and cannibalisation detection.
- Understanding AI search retrieval, which compares embeddings of passages, not whole pages.
Example
“Cheap flights to Rome” and “low-cost airfare Rome” share only one word, so a bag-of-words representation rates them as barely related. A sentence embedding places them close together because it captures that cheap and low-cost, flights and airfare mean the same thing. Which representation you choose decides what your analysis counts as similar.
Limitations and things to watch
- Simple counts miss synonyms and context; embeddings capture meaning but are hard to inspect.
- Embeddings from different models are not comparable with each other.
- Most models have input limits, so long pages are chunked or truncated.
- A patent describing information gain does not tell you how, or whether, it is weighted in ranking today.
Learn it on MLforSEO
Sources and further reading
Other terms from Core Concepts (AI/ML)
- AlgorithmA defined set of steps for solving a problem; in machine learning, the procedure that learns patterns from dat…
- Artificial Intelligence (AI)The broad field of building systems that appear to reason, learn and act intelligently; machine learning is on…
- Bag of WordsA simple way to turn text into numbers by counting how often each word appears in a document, ignoring grammar…
- Bias (AI/ML)When a model systematically favours some outcomes or groups over others because of skewed data, labels, design…
- Data Labelling (Annotation)Adding the correct answer (a category, entity, sentiment or score) to examples, so a supervised model can lear…
- Data Preprocessing (Cleaning)Cleaning, fixing and reshaping raw data so a machine learning model or analysis can use it, from removing dupl…
- Deep LearningA branch of machine learning that trains many-layered neural networks on large datasets; it powers modern lang…
- Dimensionality ReductionCompressing data with many features, such as embeddings, into fewer dimensions while keeping the important str…
- EmbeddingA list of numbers (a vector) that represents the meaning of a word, text, image or entity, so similar things s…