{"id":17521,"date":"2025-06-30T16:35:57","date_gmt":"2025-06-30T14:35:57","guid":{"rendered":"https:\/\/enigmia.es\/?p=17521"},"modified":"2025-07-28T13:31:09","modified_gmt":"2025-07-28T11:31:09","slug":"embedding","status":"publish","type":"post","link":"https:\/\/enigmia.es\/en\/noticias\/embedding\/","title":{"rendered":"Embedding: the key tool for analyzing human language"},"content":{"rendered":"

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.<\/strong>.<\/p>\n\n\n\n

The <\/strong>embedding<\/em><\/strong><\/h2>\n\n\n\n

For this purpose, and thanks to artificial intelligence, there are systems, such as the embeddings <\/em>that decipher the true meaning behind every communication.<\/strong><\/p>\n\n\n\n

The embeddings <\/em>are language models that process natural language<\/strong>and that once the data has been collected, it will be represent with high-definition number vectors<\/strong>.<\/p>\n\n\n\n

They represent a true revolution in the text analysis<\/strong> the traditional model, since they make it possible to differentiate between to react to problems and anticipate<\/strong> to them<\/a>. To be able to capturing the subtlest nuances of language<\/strong>From 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<\/strong> between different terms.\u00a0<\/p>\n\n\n\n

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What does a <\/strong>embedding<\/em><\/strong>?<\/strong><\/h2>\n\n\n\n

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

Tone and emotional charge<\/strong><\/h3>\n\n\n\n

The embedding <\/em>is achieved, by means of different factors, capturing emotion and subjective tone<\/strong> that is embodied in the medium and reflect it clearly in the numerical vectors. <\/p>\n\n\n\n

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

Structure and function <\/strong><\/h3>\n\n\n\n

The embedding <\/em>is also capable of reflect the grammatical structure and syntactic function of the sentence<\/strong>. Reflects in the vector whether the text is a statement, a negation, a question, a command, etc., even though the token<\/em><\/a> is similar.<\/p>\n\n\n\n

Time, place and people<\/strong><\/h3>\n\n\n\n

The embedding <\/em>is capable of answering and reflecting the questions of who, when and where<\/strong> with the sentence data, regardless of whether they appear explicitly or implicitly<\/strong>. <\/p>\n\n\n\n

Before the prayers 'President announced measures in 2020'<\/em> in the face of 'Minister suggested reforms in 2024'<\/em> the embedding <\/em>is able to detect that the time and the actors <\/p>\n\n\n\n

are different, even though the tone and action are similar. <\/p>\n\n\n\n

Compatibility and logic <\/strong><\/h3>\n\n\n\n

The embedding<\/em> 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<\/strong>. It also takes into account the implicit logic behind each sentence. This is used in the entailment<\/em> to see the intention of what is communicated and the logical meaning given to a sentence as soon as it is read.<\/p>\n\n\n\n

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

Therefore, in order to reach a conclusion, such as Pedro has a car<\/em> the prayer Pedro bought a new car'.<\/em> 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<\/em> is much further from reaching the conclusion, both because of the choice of words and the inference that can be drawn from it. <\/p>\n\n\n\n

Differentiation of synonyms <\/strong><\/h3>\n\n\n\n

A embedding<\/em> is capable of differentiate<\/strong> types of synonyms depending on the word they are and the context in which they are placed. <\/p>\n\n\n\n

However, there is a classification of synonyms<\/strong>. To give an example, a flat synonym<\/strong> would respond to the common and generally known idea of a synonym, such as the word bank<\/em>.<\/p>\n\n\n\n

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

Complex semantic relationships<\/strong><\/h3>\n\n\n\n

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