Functionality and Applications of Natural Language Processing in Modern Search Engines
Posted: Wed Feb 19, 2025 8:49 am
With the introduction of BERT in 2018, Google officially confirmed that they were using natural language processing for Google search. Natural language processing, as a sub-area of machine learning, is about better understanding human language in written and spoken form and converting unstructured information into machine-readable structured data. Subtasks of NLP include translating languages and answering questions. It quickly becomes clear how important this technology is for modern search engines like Google.
In general, the functionality of NLP can be roughly broken down into the following process steps:
data provision
data preparation
text analysis
text enrichment
The core components of NLP are tokenization, part of speech tagging, lemmatization, dependency parsing, parse labeling, named entity recognition, salience scoring, sentiment analysis, categorization, text classification, content type extraction, and identification of implicit meaning based on structure.
Tokenization: Tokenization is the process of dividing a sentence into different terms.
Marking words according to parts of speech: Part of speech marking classifies words according to parts of speech such as subject, object, predicate, adjective…
Word dependencies: Word dependencies create relationships between words based on grammar rules. This process also maps “jumps” between words.
Lemmatization: Lemmatization determines whether a word has different forms and normalizes variations to the base form. For example, the base form of animals is animal or of playful is game.
Parsing Labels: The label classifies the dependency or the type of relationship between two words that are connected by a dependency.
Analysis and extraction of named entities: This aspect should be familiar to us from previous posts. This attempts to identify words with a "known" meaning and assign them to classes of entity types. In general, named entities are people, places and things (nouns). Entities can also contain product names. These are generally the words that trigger a knowledge panel. But even terms that do not trigger their own knowledge panel can be an entity.
Example of a syntax analysis from the NLP API demo, source: Google
Natural language processing can be used to identify entities in search queries, sentences and text sections, and the individual components can be broken down into so-called tokens and put into relation with each other. Grammatical understanding can also be developed algorithmically using NLP.
With the introduction of Natural Language Processing, Google is also able to interpret more than just nouns for the interpretation of search queries, texts and language. Since BERT, verbs, adverbs and adjectives have also been important for determining the context. By identifying the relationships between the tokens, references can be made and personal pronouns can also be interpreted.
An example:
"Olaf Kopp is Head of SEO at Aufgesang . He has been involved in online marketing since 2005. "
In the time before natural language processing, Google could not do anything with the personal pronoun "he" because no reference to the entity "Olaf Kopp" could be made. For indexing and ranking, only the terms Olaf Kopp, Head of SEO, Aufgesang, 2005 and Online Marketing were taken into account.
Natural Language Processing can not only identify entities in spain cell phone number list search queries and content, but also their relationship to each other.
The grammatical sentence structure as well as references within entire paragraphs and texts are taken into account. Nouns or subjects and objects in a sentence can be identified as potential entities. Relationships between entities can be established using verbs. A sentiment (mood) around an entity can be determined using adjectives.
Natural Language Processing via Vectors
Natural language processing also makes it easier to answer specific questions, which represents a significant development in the use of voice search.
Natural Language Processing also plays a central role in the Passage Ranking introduced by Google in 2021.
Since the introduction of BERT in 2018, Google has been using this technology in Google Search. The passage ranking introduced in 2021 is based on natural language processing, as Google can better interpret individual text passages thanks to the new possibilities.
In general, the functionality of NLP can be roughly broken down into the following process steps:
data provision
data preparation
text analysis
text enrichment
The core components of NLP are tokenization, part of speech tagging, lemmatization, dependency parsing, parse labeling, named entity recognition, salience scoring, sentiment analysis, categorization, text classification, content type extraction, and identification of implicit meaning based on structure.
Tokenization: Tokenization is the process of dividing a sentence into different terms.
Marking words according to parts of speech: Part of speech marking classifies words according to parts of speech such as subject, object, predicate, adjective…
Word dependencies: Word dependencies create relationships between words based on grammar rules. This process also maps “jumps” between words.
Lemmatization: Lemmatization determines whether a word has different forms and normalizes variations to the base form. For example, the base form of animals is animal or of playful is game.
Parsing Labels: The label classifies the dependency or the type of relationship between two words that are connected by a dependency.
Analysis and extraction of named entities: This aspect should be familiar to us from previous posts. This attempts to identify words with a "known" meaning and assign them to classes of entity types. In general, named entities are people, places and things (nouns). Entities can also contain product names. These are generally the words that trigger a knowledge panel. But even terms that do not trigger their own knowledge panel can be an entity.
Example of a syntax analysis from the NLP API demo, source: Google
Natural language processing can be used to identify entities in search queries, sentences and text sections, and the individual components can be broken down into so-called tokens and put into relation with each other. Grammatical understanding can also be developed algorithmically using NLP.
With the introduction of Natural Language Processing, Google is also able to interpret more than just nouns for the interpretation of search queries, texts and language. Since BERT, verbs, adverbs and adjectives have also been important for determining the context. By identifying the relationships between the tokens, references can be made and personal pronouns can also be interpreted.
An example:
"Olaf Kopp is Head of SEO at Aufgesang . He has been involved in online marketing since 2005. "
In the time before natural language processing, Google could not do anything with the personal pronoun "he" because no reference to the entity "Olaf Kopp" could be made. For indexing and ranking, only the terms Olaf Kopp, Head of SEO, Aufgesang, 2005 and Online Marketing were taken into account.
Natural Language Processing can not only identify entities in spain cell phone number list search queries and content, but also their relationship to each other.
The grammatical sentence structure as well as references within entire paragraphs and texts are taken into account. Nouns or subjects and objects in a sentence can be identified as potential entities. Relationships between entities can be established using verbs. A sentiment (mood) around an entity can be determined using adjectives.
Natural Language Processing via Vectors
Natural language processing also makes it easier to answer specific questions, which represents a significant development in the use of voice search.
Natural Language Processing also plays a central role in the Passage Ranking introduced by Google in 2021.
Since the introduction of BERT in 2018, Google has been using this technology in Google Search. The passage ranking introduced in 2021 is based on natural language processing, as Google can better interpret individual text passages thanks to the new possibilities.