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Creating quality content that’s relevant to the keywords you want to rank for is essential. Content relevance, on the other hand, is harder to gauge. Gone are the days when you could trick a search engine into thinking your content was related to a topic by stuffing pages and tags with as many keywords, co-occurrences, and synonyms as possible.
Google is continually improving its ability to understand human behavior and language. The search engine’s ability to understand and analyze user search intent is better than ever. Therefore, it’s critical to improve our ability to measure relevance so we can create content that Google perceives as truly useful, and therefore more worthy of ranking.
Our content relevance checker tool
Artificial intelligence at the service of content. In 2018, a new generation of AI models appeared, allowing a representation of words not only according to their general context (the words with which they are frequently used in the training corpus) . But also according to their local context (in a particular sentence). This includes the model called ELMo .
ELMo is not, however, designed to produce lexical embeddings of sentences ; we had to evolve this technology.
Based on this model, we have developed a content the filiate program is set up for relevance assessment . Tool that allows us to obtain an accurate measurement of relevance to a given keyword, topic, or concept.
What are lexical embeddings?
In 2013, a team at Google published a paper describing a model training process to . Teach an algorithm to understand how words are represented in a vector space. Representing words or phrases in a vector space in this way is what we mean by word embedding .
Since the publication of the article, the concept has quickly become a very popular way to represent textual content for any machine learning task in the field of natural language. It has helped push the boundaries in this regard. The improvement in the capabilities of virtual personal assistants such as Alexa and Google Assistant is also linked to the publication of this technology.
The term “vector space” is a mathematical approach to language processing, here we understand it as a multidimensional system of coordinates that allows us to model the relationship of words according to the conceptual context .
For example, let’s imagine that we want to measure the similarity between the following five words:
- Banana
- Kiwi
- Orange
- Bell pepper
- Crab
Measuring semantic similarity when the conceptual context is very narrow can be done intuitively.
So, if we evaluate the similarities between these foods, based on their nature, we can consider that the banana is very similar to the orange and the kiwi , because all three foods are fruits . On the other hand, the pepper and the crab can be considered less similar (salty foods).
The visual representation of their similarity based on their nature looks like this:
The three fruits are close to each cn leads other while the vegetable is further away in one direction and the crab in the opposite direction.
However, when we measure the similarity of these foods in a different context, the representation changes completely. If we look at their vitamin C composition rather than their nature, kiwi and bell pepper are similar. On the other hand, bananas and crab are not rich in vitamin C.