What's in a name? Well, for search engines, it's complicated. While we, as humans, have a naturally nuanced understanding of language, search engines need a little extra help sorting things out. Let's take a look at one example: the mechanics of named entity recognition. It's the mechanism used by search engines to determine what on earth we, as humans, are trying to say.

What is named entity recognition?

Named entity recognition (NER), sometimes called entity chunking or entity extraction, is a component of information retrieval in natural language processing (NLP). It involves identifying and classifying key parts of a text into predefined categories such as people, organizations, locations, dates, and other specific groups.

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For example, in the sentence "NASA launched the Mars Rover on July 30, 2021, from Cape Canaveral," NER would identify:

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• "NASA" as an organization
• "Mars Rover" as a specific project or mission name
• "July 30, 2021" as a date
• "Cape Canaveral" as a location

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This type of recognition helps in categorizing and extracting relevant information from text for better analysis and understanding.

What is the role of NER in search engine algorithms?

Named entity recognition (NER) is crucial to search algorithms because it helps to improve the textual understanding of bots and the relevance of search results. When you consider the capabilities of AI in contrast to human understanding, it's easy to see how the ambiguity and nuances of language can make categorization difficult — and necessary. Look at this sentence:

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China (group of athletes) won 48 gold medals at the 2008 Olympics in China (location).

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NER enables computers to understand that the same word used twice in one sentence can have different meanings: a group of athletes who share a nationality and a geographic location.

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If you went to Google and asked, "how many gold medals did China win at the 2008 Olympics?" Google wouldn't be scratching its head wondering how a location could win a medal in diving. Instead, NER allows the Google algorithms to know that you're asking about how many gold medals the athletes representing China won at the 2008 Olympics in China.

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Now that you have an example, here’s how NER further contributes to search engine algorithms:

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• Better natural language understanding. NER helps search engines identify and categorize key people, places, and organizations in web content. By recognizing these entities, search engines can better understand the context and subject matter of a page through deep learning, leading to more accurate indexing and retrieval.
• Improved query matching. When users enter search queries, NER helps search engines match those queries with relevant content by identifying and interpreting the named entities in both the query and the indexed pages. NER also helps search engines understand the context and user intent in which named entities are mentioned. This improves the ability to differentiate between entities with similar names. Another term for this is "semantic search."
• Rich snippets and structured data. Search engines use NER to improve the display of search results with rich snippets and structured data. By recognizing entities like products, events, and organizations, search engines can present additional details such as reviews, ratings, or event dates directly in the search results, adding value to the results.
• Content categorization. Search engines use NER to categorize content more effectively. By identifying and tagging entities, search engines can organize content into relevant categories and topics, which helps improve content recommendations and personalization.
• Text summarization. NER enables text summarization to focus on the most important entities and interactions so it can capture the core factual content of the text, rather than just the keywords. NLP tools like GPT and Google's Gemini use this method of text processing.

How does NER work?

We're so glad you asked.

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The process begins with preprocessing, where the text is cleaned and prepared for analysis. This involves removing unnecessary characters, correcting typos, and sometimes normalizing text.

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Next, the text is tokenized, meaning it is broken down into smaller units like words or phrases. Tokenization helps analyze the structure and meaning of the text more effectively. In the sentence,  "John Lennon co-founded the Beatles," it would be split into tokens, "John," "Lennon," "co-founded" and "Beatles."

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Next, the NER system applies feature extraction. This step involves identifying and creating features or characteristics of the text that might be relevant for recognizing named entities. Features can include part-of-speech tags, word shapes, or even surrounding context. In this case, it would be the capital 'L' in Lennon and 'B' in Beatles, which indicates that these are proper nouns.

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Now, we're at the core of NER: entity classification. At this stage, the system uses pre-trained models, which may be based on machine learning algorithms or deep learning networks, to classify each token or sequence of tokens into predefined categories like "Person," "Organization," or "Location." In this case, "John Lennon" would be classified as "person" and the Beatles, an "organization."

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Once the entities are classified, the system proceeds to post-processing. This step involves refining and validating the results to ensure accuracy. It may include resolving ambiguities, such as distinguishing between a person's name and a common noun. This kind of quality control prevents "Beatles" from being classified as group of insects instead of talented rock stars.

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Finally, the system outputs the recognized entities along with their categories, making it easier for users to extract meaningful information from the text. Thanks to NER, you'll receive a wealth of information from a search engine about a group of talented young men from Liverpool who formed one of the most popular bands of all time.

How can I use NER to improve my SEO?

Understanding NER gives you a better grasp of how search engines assess your content and generate SERPs. Your best option is leveraging the insights gained from NER to improve content relevance and quality, creating a better user experience on your website. Here are some more specific strategies to help you incorporate NER into your content strategy and SEO efforts:

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• Optimize for specific entities.  Do some content marketing research. Are there specific companies, products, or locations that are big players in your industry? Make sure that your content includes these entities naturally in the text.
• Create targeted content. Create content around topics that are already doing well in your industry. This should help you align your content with what users and search engines find valuable. Rellify is the expert in content optimization, using deep machine learning to cluster topics and develop keywords that will naturally rank well on SERPs.
• Improve your technical SEO. Include relevant entities in your metadata (title tags, meta descriptions, and headers). This can improve the relevance of your pages for specific queries related to those entities. You should also implement structured data (schema markup) for entities identified through NER.
• Internal linking. Identify important entities in your content and use them to create internal links to related pages on your site. This helps search engines understand the context and relationships between your pages, improving your site's SEO.
• Voice search optimization. Voice search often involves questions about specific entities. Use NER to identify common entities and incorporate them into content designed to answer voice search queries. This improves your chances of being featured in voice search results.

Let Rellify use NER to boost your returns

Rellify is no stranger to the value of NER and natural language processing. We strategically use it in the context of deep learning to better understand your brand and your audience. If you're ready for a content revamp, or looking for ways to boost your digital marketing returns, a custom Relliverse™ from Rellify is the way to go. With a Relliverse™, you can use artificial intelligence with deep machine learning to cluster topics, find keywords, and build content that's relevant and naturally ranks well on search engine results pages. Ready to find out more? Schedule a brief demo with one of our experts today!