How Google Uses NLP

We explain how Google uses NLP, what it is, and how you can use NLP to improve your content.

In the realm of online search, natural language processing (NLP) has paved the way for a more sophisticated approach to understanding user queries. As a leading search engine, Google has been at the forefront of this evolution, embracing the shift to entity-based search. This transformation is considered the future of search, with major implications for search engine optimisation specialists.

Deeply exploring the realm of NLP, this article sheds light on how Google interprets search queries and content. It delves into the intricacies of entity mining and highlights the potential impact of this change on search strategies.

Key Takeaways

• NLP enhances search engines’ understanding of user queries
• Entity-based search is the future, with Google leading the charge
• Stakeholders must adapt to the evolving landscape of search behaviour and strategies

What is Natural Language Processing?

Natural Language Processing (NLP) is a field within artificial intelligence that aims to understand and interpret human languages. It enables computers to grasp the meaning of words, sentences, and text to generate valuable insights, knowledge, or even new content.

NLP incorporates two main aspects: Natural Language Understanding (NLU), which focuses on the semantic interpretation of text and languages, and Natural Language Generation (NLG), which creates coherent narratives from structured data.

NLP is employed in a wide range of applications, such as:

• Speech recognition, responsible for converting speech to text and vice versa.
• Segmenting spoken language into individual words, sentences, or phrases.
• Identifying basic word forms and acquiring grammatical knowledge.
• Recognition of word functions within a sentence, such as subject, verb, object, and more.
• Extracting the meaning of sentences and syntactic components, like adjective phrases, prepositional phrases, and noun phrases.
• Understanding sentence context, relationships, and entities.
• Linguistic text analysis, sentiment analysis, translations, chatbots, and the foundation of Q&A systems.

NLP comprises several core components to process and analyse language effectively:

• Tokenisation: Separates sentences into individual terms or tokens.
• Word type labelling: Categorises words as object, subject, predicate, adjective, and so on.
• Word dependencies: Establishes relationships between words based on grammatical rules.
• Lemmatization: Identifies different word forms and normalises them to their base form, such as changing “cars” to “car”.
• Parsing labels: Marks words according to the relationship between two connected words with a dependency.

Moreover, NLP capabilities extend to other functions like:

• Named entity analysis and extraction: Identifies words with known meanings and allocates them to groups of entity types, such as organisations, people, products, places, and other nouns.
• Salience scoring: Evaluates the extent to which a text is related to a specific topic, often determined by co-citations and relationships between entities in databases like Wikipedia and Freebase.
• Sentiment analysis: Recognises a text’s opinions, views, or attitudes about entities or topics.
• Text categorisation: Classifies text into broad content categories, which helps determine the subject matter.
• Text classification and function: NLP can also discern content’s intended function or purpose, enabling search intent to match with documents.
• Content type extraction: Using structural patterns, context, and data type, NLP can determine a text’s content type without needing structured data or markups.
• Identify implicit meaning based on structure: The formatting of a text can influence the perceived meaning, and elements like headings, line breaks, lists, and proximity can express an alternate understanding of the text.

By utilising these techniques, NLP helps computers process and comprehend the complexities of human language, improving our interaction with technology and providing powerful tools for analysis and understanding.

The Use of NLP in Search

Google continuously refines its language understanding capabilities by training models like BERT and MUM, utilising natural language processing (NLP) to interpret text, search queries, and even video and audio content.

The primary applications of NLP in Google search involve:

• Interpreting search queries allows Google to comprehend users’ search intent better.
• Classifying document subject matter and purpose, enabling more tailored SERP results.
• Undertaking entity analysis in documents, search queries, and social media posts contributes to optimising search results.
• Generating featured snippets and voice search answers by accurately extracting relevant information from web content.
• Deciphering video and audio content, ensuring that non-textual information remains discoverable by users.
• Expanding and improving the Knowledge Graph connects users with authoritative and relevant documents.

In October 2019, Google emphasised the significance of natural language understanding in search with the release of the BERT update. This leap forward allowed the search engine to process complex or conversational queries better, reducing the need for users to submit strings of keywords in an unnatural, query-focused manner. By using NLP, Google aims to streamline the search experience and provide more accurate and contextually relevant results, significantly benefiting users and enhancing overall search efficiency.

BERT & MUM: NLP for Interpreting Search Queries and Documents

BERT, a significant development in Google search following RankBrain, employs Natural Language Processing (NLP) to enhance the interpretation of search queries. Impacted initially by 10% of all search queries, BERT’s influence extends to ranking, compiling featured snippets, and interpreting text questionnaires in documents, thus delivering more relevant information to users.

MUM, announced at Search On ’21, is another NLP-based update designed to be multilingual and capable of handling complex search queries with multimodal data. MUM processes various media formats, including text, images, video, and audio files. By combining multiple technologies, MUM makes Google search more semantic and context-based, resulting in a superior user experience.

Both BERT and MUM work towards a better semantic understanding and a more user-centric search engine. This NLP foundation results in a shift from “strings” to “things”, which focuses on understanding search queries and content via entities rather than individual words. By identifying entities in search queries, the meaning and intent become clearer as words are considered within the context of the entire search query.

The magic of interpreting search terms happens through query processing, in which several crucial steps are involved:

• Identifying the thematic ontology: By understanding the context of the query, Google selects potentially suitable search results from various content formats, such as text documents, videos, and images. This can be challenging in cases of ambiguous search terms.
• Identifying entities and their meaning in the search term (named entity recognition).
• Understanding the semantic meaning of a search query.
• Identifying the search intent.
• Semantic annotation of the search query.
• Refining the search term.

In summary, BERT and MUM represent advancements in the realm of NLP, powering the interpretation of search queries, ranking, featured snippets, and content analysis. Their ultimate aim is to pave the way for a more contextual and user-centric search engine experience, creating a smoother and more accurate journey for users seeking information through multimedia.

NLP: A Vital Approach to Entity Mining

Natural language processing (NLP) holds a significant role in Google’s ability to identify and understand entities, making it possible to extract valuable information from unstructured data. Valuable insights can be gained by establishing connections between entities and the Knowledge Graph. Part-of-speech tagging aids this process to some extent.

Nouns typically represent potential entities, while verbs often indicate the relationship between these entities. Adjectives describe entities, and adverbs characterise their relationships. For now, Google has made limited use of unstructured data to populate the Knowledge Graph.

It is believed that:

• The current entities in the Knowledge Graph represent only a small portion of the information available.
• Google is also using an additional repository to collect data on long-tail entities.

NLP serves as a central component in populating this additional knowledge repository. While Google has made considerable progress in NLP, there is still room for improvement in assessing the accuracy of automatically extracted data.

Mining information from unstructured sources like websites for a knowledge base like the Knowledge Graph can be complex. Ensuring completeness and correctness of information is vital. Google achieves large-scale completeness through NLP, but guaranteeing correctness and accuracy remains a challenge.

This might explain Google’s cautious approach towards presenting information on long-tail entities directly in search engine results pages (SERPs).

Entity-Based Index versus Classic Content-Based Index

The advent of the Hummingbird update led to the development of semantic search and subsequently brought the Knowledge Graph and entities into prominence. The Knowledge Graph serves as Google’s entity index, organising all attributes, documents, and digital images, such as profiles and domains around the entity in an entity-based index.

The Knowledge Graph operates alongside the classic Google Index for ranking purposes. If Google detects an entity from the Knowledge Graph within a search query, information from both indexes is utilised, focusing on the entity and considering all related information and documents.

An interface or API is necessary to facilitate the exchange of information between the classic Google Index and the Knowledge Graph. This entity-content interface is responsible for determining:

• The presence of entities within the content
• The primary entity the content revolves around
• The ontologies the main entity belongs to
• The author or entity associated with the content
• The relationships between entities in the content
• Properties or attributes to be assigned to the entities

While the impact of entity-based search in the SERPs is gradually becoming more evident, Google takes time to comprehend the meaning of individual entities. Entities are understood from a top-down perspective based on social relevance, with the most significant ones recorded in Wikidata and Wikipedia.

The challenge lies in identifying and verifying long-tail entities and determining the criteria Google uses for including an entity in the Knowledge Graph. According to a German Webmaster Hangout in January 2019, Google’s John Mueller mentioned that they were working on an easier method to create entities for everyone.

Natural language processing (NLP) plays a crucial role in addressing this challenge. For instance, examples from the Diffbot demo demonstrate how NLP can be effectively employed for entity mining and constructing a Knowledge Graph.

NLP in Google Search is Here to Stay

Google’s search engine has integrated NLP (natural language processing) advancements such as RankBrain, BERT, and MUM to understand search queries and content deeply. These technologies significantly enhance the user experience and facilitate the evolution of semantic search in Google.

The substantial growth of the Knowledge Graph, a comprehensive knowledge base, is attributed to these NLP advancements. As a consequence, content marketers need to adapt to this newer entity-based approach to Google search results. This trend gradually replaces the traditional phrase-based indexing and ranking, paving the way for a more efficient and accurate search experience.

Google’s core updates are also heavily influenced by the advancements in NLP technologies like BERT and MUM. In the long run, this shift towards semantic search through natural language processing will stay and continue to shape the future of Google search.

Frequently Asked Questions

How does Google utilise NLP to enhance search outcomes?

Google uses NLP (Natural Language Processing) better to understand the semantic meaning of search queries and content. This enables the search engine to provide more accurate and context-based results, improving user experience. Algorithms are designed to work across various languages and domains for better understanding and relevance.

What significance does BERT hold in Google’s search algorithm?

BERT (Bidirectional Encoder Representations from Transformers) is critical to Google’s search algorithm. It helps Google understand the context of words in search queries by analysing them from both left and right directions. This allows the search engine to grasp the nuances of human language and deliver more relevant results.

How does semantic search function within Google’s NLP framework?

Semantic search in Google’s NLP framework focuses on understanding the meaning and context behind search queries, rather than merely matching keywords. NLP algorithms analyse the relationships between words, phrases, and the concepts they represent. This approach allows Google to provide more accurate search results that match the searcher’s intent.

What are the main aspects of Google’s NLP API?

Google’s NLP API includes features such as:

• Sentiment analysis: Assessing the sentiment of text, determining if it is positive, negative, or neutral.
• Entity recognition: Identifying and categorising entities within the text.
• Syntax analysis: Analysing sentence structure and relationships between words and phrases.
• Language detection: Automatically detecting the language used within the text.

These functionalities enable developers to integrate NLP capabilities into various applications and services easily.

How is NLP incorporated into Google’s document search?

Google utilises NLP to enhance document search by understanding the semantic meaning of words, phrases and statements within documents. This allows the search engine to provide more accurate and relevant results, making it easier for users to find the information they’re looking for.

Is it possible to employ NLP to improve email services?

Yes, NLP can be used to enhance email services by analysing the text of emails and identifying important phrases, entities or sentiments. This can lead to improved email organisation, classification, and filtering. Additionally, NLP can be used to suggest responses and generate summaries in order to save time and streamline communication.