It is crucial for a company to have the necessary resources to analyze language in a digital era such as the one we are living in. No one should be satisfied with analyzing language in a superficial and distant way, since every comment or mention can have a considerable impact on corporate reputation. It is necessary to understand and analyze perfectly what is being communicated, how and when it is communicated..

The embedding

For this purpose, and thanks to artificial intelligence, there are systems, such as the embeddings that decipher the true meaning behind every communication.

The embeddings are language models that process natural languageand that once the data has been collected, it will be represent with high-definition number vectors.

They represent a true revolution in the text analysis the traditional model, since they make it possible to differentiate between to react to problems and anticipate to them. To be able to capturing the subtlest nuances of languageFrom the meaning of the words to the context in which they are used and the linguistic role they play in the message, embedding allows companies to visually reach their target audience. discover connections between different terms. 

What does a embedding?

The fact that the embedding from Enigmia has been extended to more than 1500 dimensions makes it easier for the AI, when analyzing a text, to do so with more precision and depth, and can reach the following areas:

Tone and emotional charge

The embedding is achieved, by means of different factors, capturing emotion and subjective tone that is embodied in the medium and reflect it clearly in the numerical vectors. 

The system will be able to illustrate in the vector distance that sentences the company reported a record increase'. y 'the company is facing a serious downturn' talk about performance, but each has a different tone. 

Structure and function 

The embedding is also capable of reflect the grammatical structure and syntactic function of the sentence. Reflects in the vector whether the text is a statement, a negation, a question, a command, etc., even though the token is similar.

Time, place and people

The embedding is capable of answering and reflecting the questions of who, when and where with the sentence data, regardless of whether they appear explicitly or implicitly. 

Before the prayers 'President announced measures in 2020' in the face of 'Minister suggested reforms in 2024' the embedding is able to detect that the time and the actors 

are different, even though the tone and action are similar. 

Compatibility and logic 

The embedding is capable of analyzing linguistic compatibility, i.e., whether two sentences deal with the same subject and how similar they are even though they have different words. It also takes into account the implicit logic behind each sentence. This is used in the entailment to see the intention of what is communicated and the logical meaning given to a sentence as soon as it is read.

The entailment is the logical relationship between two sentences, in which one statement necessarily implies the truth of the other. If we say that 'Maria bought a bicycle'. implies that Maria bought something'.but this last sentence does not have to imply that Maria bought (necessarily) a bicycle'.. 

Therefore, in order to reach a conclusion, such as Pedro has a car the prayer Pedro bought a new car'. is close to the first sentence, and even includes it implicitly in its meaning. On the other hand, the sentence Pedro has never owned a car is much further from reaching the conclusion, both because of the choice of words and the inference that can be drawn from it. 

Differentiation of synonyms 

A embedding is capable of differentiate types of synonyms depending on the word they are and the context in which they are placed. 

However, there is a classification of synonyms. To give an example, a flat synonym would respond to the common and generally known idea of a synonym, such as the word bank.

Now, it can be said that a flat synonym, when it is given in a sentence or in a context determined, is limited only with the meaning that gives it meaning in that particular sentence. Therefore, in the sentence 'bank customer the other meanings would be meaningless, just as in the sentence 'sitting on the park bench'. the other meanings will also not be taken into account. 

Complex semantic relationships

Some words and concepts are related, but may be more distant or closer depending, at times, on their connotation. Therefore, the embedding not only captures the synonymy and its relationships but also:

• Hyperonymy: when the meaning of a word is included in the meaning of other words. That is, the meaning of animal is also within the meaning of 'dog', cat o horseas these are also animals.

• Antonyms: words that express opposing ideas. These words also have a classification and the embedding is capable of identify these antonyms and relate them. Graduals, for example, despite being opposites, are not absolute opposites. The fact that 'bass is the opposite of 'high' does not cause all intermediate terms to be excluded, if any, as in this case, which would be medium. 

• Cause-effect relationshipsThis type occurs in sentences where first a cause is observed and then the effect that this cause has is described.. 'He failed because he didn't study' is an example of a sentence where the main verb is the cause of the following verb. 

• Thematic associations: These are the associations attributed to a wordThe words, within a given topic, by the use they have or by the way they sound. The words teacher, pupil, 'examination'., 'slate are all associated with the theme school. Other types of partnerships are also possible; climate change has a direct relationship with climate, but by giving it usually a negative connotation, it has more to do with carbon emissions that with 'sunny weather. 

Coherence in theme and discourse

Regardless of what is being analyzed, whether it is image, audio or text, a embedding is capable of to find and reflect the similarity of the themes. That is, although Investment in renewable energies increased'., 'Solar energy grew in Europe' y Green hydrogen gains ground in 2025'. are sentences of different texts, when dealing with the same subject matter, the embedding reflects it in the vector it generates. 

Abstraction or specification 

The embedding is capable of represent, understand and relate concepts either abstract or concreteand to relate them to other concepts. You will keep in mind that a concept such as democracy'. is abstract and is more closely related to the term abstract 'freedom of expression'. that with the concept of QR code.

Enigmia: pioneer in the use of embeddings

Therefore, a high-dimensional embedding with more than 1,500 elements, as those used by Enigmiarepresents a revolution in the analysis of human language. Each dimension enables the model to capture a latent semantic axis: context, syntactic role, polarity, tone, conceptual relations, etc. This technology converts informational noise into precise insights, through semantic searches and concept maps, offering a decisive competitive advantage where every comment can significantly impact corporate reputation in the digital age.