Last year, Google opened the door to semantic and entity -based search , which represents a paradigm shift in the way content is positioned on this search engine. Understanding this change is essential for marketers. Therefore, in this article we are going to see what natural language processing is and how Google is using it in its searches.
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How Google uses NLP to understand searches and content
What is natural language processing and how is it applied in search?
Natural language processing (NLP) makes it possible to understand the meaning of words, sentences and texts in order to generate new information or texts . It includes natural language understanding (NLU) and natural language generation (NGL).
Natural language processing has multiple applications , such as:
Speech recognition (text-to-speech and speech-to-text).
Segment speech into individual words, sentences and phrases.
Recognize basic word forms and obtain grammatical information.
Recognize the functions of individual words within a sentence (subject, verb, object, article, etc.).
Extract meaning from sentences and parts of sentences, such as “too long,” “toward the highway,” or “the long run.”
Recognize the context in which sentences appear, the relationships between them and the entities present.
Conduct linguistic and opinion analysis of texts.
Translations (including translations for voice assistants).
Chatbots and other question and answer systems.
Key components of natural language processing include :
Tokenization: Breaks a sentence into different elements.
Word type labeling: object, subject, predicate, adjective, etc.
Word dependencies: identifies relationships argentina phone number resource between words based on grammatical rules.
Stemming: Determines whether a word has different forms and normalizes the variations. For example, the base form of “cars” is “coche.”
Entity analysis and extraction: identifies words with a known meaning and assigns them to entities. Entities typically include organizations, people, products, places, and things (nouns). In a sentence, subjects and objects are identified as entities.
Salience score: determines how connected a text is to a given topic. Salience is usually determined by commonly mentioned words and relationships between entities in databases such as Wikipedia.
Analysis of opinions and attitudes expressed in a text.
Text categorization: identifies what the text is about in general.
Classification of texts according to functions: identifies the function or purpose of the text.
Content Type Extraction: Search engines can determine the content type of text without structured data, based on elements such as HTML, formatting, and data type.
Identifying implicit meanings from formatting: For example, we can deduce the importance of text based on font size, presence of lists, etc.
For years, Google has trained its language models, such as BERT or MUM , to interpret text, search queries, and even video and audio content. These models are powered by natural language processing.
