Are you tired of playing keyword whack-a-mole? Does it feel like you’re stuck in a semantic maze when trying to optimize pages for search? Perhaps it’s time to go beyond keywords and tap into the true potential of language modeling for SEO.
This extensive guide will explain how modern linguistic analysis techniques used by sophisticated AI systems can take search engine comprehension to the next level. We’ll cover 63 different methods spanning semantics, syntax, sentiment, entities, knowledge graphs, and much more. We don’t know if Google, Bing, and other search engines use all the large language models (LLM) covered here,but as LLM takes on a greater role in the SERPs, understanding LLM becomes more important for every SEO. This is a primer designed for you to expand upon, inspire ideation, and hopefully report back what you discovered if you implemented an action based on a particular model.
Fair warning: This is not a quick tip listicle. The full article spans over 13,000 words and multiple sections. We’ll be digging deep into advanced NLP capable of decoding meanings from text through machine learning.
But the investment of your time and brainpower will pay off with unique insights you can apply to enhance on- and off-page optimization. You’ll learn how search engines may leverage these techniques under the hood today and where future relevance ranking might be headed. We even included some practical things to try, but we fully expect you to extrapolate and try your own optimizations based on the given model.
Arm yourself with linguistics fundamentals to create content ready for the next generation of semantic search. Build pages that don’t just match keywords but actually communicate knowledge. Move from targeting only search bots to delivering value for humans.
The details matter when it comes to comprehension. And details are what this guide delivers. So grab a nice beverage, get comfortable, and level up your optimization strategy with the techniques savvy AI engineers use to extract meaning from language. The effort will give you a valuable competitive edge.
We are presenting these models in somewhat of a prioritized manner rather than alphabetically. Note the word, “somewhat,” as endless arguments between SEOs might emerge between the actual order of this list. This is simply a semi-prioritized list based on how we are seeing things currently.
Each model follows the following pattern:
This looks at how concepts, word meanings, and vocabulary connect. For example, it relates the concept of “transportation” to words like “car” and “train”.
Search engines rely heavily on understanding the conceptual, semantic, and lexical relationships within web pages to properly interpret their content. Analyzing these different dimensions of meaning can reveal the key topics, entities, and themes that characterize a page. For example, mapping out lexical relations between words can uncover synonyms and related terms that indicate core concepts. Identifying semantic relations between entities mentioned on a page provides insight into what real-world objects and events the content describes. Modeling conceptual relations gives a broader view of the main ideas and abstractions that underly the text.
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This analyzes how different parts of speech, like nouns and verbs, link together in sentences. For example, it looks at how nouns connect to verbs.
Analyzing the relationships between parts of speech used on a webpage can reveal important clues about its information content. Research shows distinctive patterns of nouns, verbs, adjectives and other grammatical classes within text of differing subject matter, style and sentiment. Leveraging these cross-part-of-speech relationships allows search engines to categorize and compare pages based on their structural composition. For instance, some topical areas feature heavier use of nouns versus verb constructions. Pages with a higher incidence of adjectives may suggest more subjective or promotional content. Detecting the frequency and co-occurrence of POS tags provides useful input to better understand a document’s focus and quality.
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This figures out how each word in a sentence depends on or relates to other words. For example, it links the noun “dog” to the verb “barked”.
Analyzing the syntactic dependency relationships in the sentences of a webpage provides clues about its information content. Recursively identifying which words depend on or modify other words reveals the underlying predicate-argument structure of the text. This allows search engines to glean key semantic aspects, like subjects, objects, and actions described by a document. It also aids in resolving modifiers and qualifiers so that core factual statements can be extracted from the text. Overall, dependency parsing helps search engines move beyond just matching keywords to understanding pages’ meaning and propositional content so that results can better match user intent.
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This involves tagging how words and concepts relate to each other with role labels. For example, it could link “Steve Jobs” to “Apple” with a “founded” label.
Annotating semantic relationships between entities mentioned in a webpage’s content can augment search engines’ understanding of its topics and meaning. Techniques like semantic role labeling can identify the roles different noun phrases play, like agents, patients, instruments, etc. Extracting subject-predicate-object triples can capture core factual statements made in the text. And labeling relations like “person X founded company Y” can extract precise details about key entities. By parsing out and cataloging these semantic relations, search algorithms can look beyond keyword matching to better model pages’ focus, events described, and reliability.
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This studies networks of words that appear together in texts to model connections. For example, it maps out that “pancake” and “syrup” often occur together.
Modeling the networks of co-occurring words and entities on webpages can provide useful clues about their semantic themes and topics. Constructing graphs where nodes are vocabulary and edges show statistical co-occurrence allows search algorithms to map out the key concepts contained within the text. Community detection can identify densely linked nodes that likely correspond to topic clusters. Analyzing the centrality of nodes in the network can uncover dominant themes. Comparing the co-occurrence networks across pages can help segment documents into related groups based on shared concepts. By moving beyond just matching keywords to modeling semantic connections, search engines can better organize and rank pages for relevance.
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This labels words in the text that are names of things like people, places, and companies. For example, it identifies “Barack Obama” as a person’s name.
Identifying named entities mentioned on webpages like people, organizations, locations, dates, etc. provides useful signals about page content that go beyond keyword analysis. The prominence and relations of these real-world entities can reveal the focus and provenance of the page text. For example, a high incidence of locations may indicate geo-specific content. Identifying authoritative sources like public figures or experts mentioned could bolster page trustworthiness. Analyzing trends in entity types over time can factor into assessments of freshness as well. By leveraging these entity-based insights, search engines can filter and rank pages in ways that better match entities of interest to the user.
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This labels what role words play in a sentence, like “agent” or “theme”. For example, it tags “boy” as the “agent” and “cookie” as the “theme” in “The boy ate the cookie.”
Semantic role labeling involves detecting the roles played by entities within predicates describing events or actions. This provides useful insights for search engines into the nature of the content described on a webpage. For example, identifying agents, patients, instruments, etc. allows search algorithms to categorize pages based on key participants and actions they mention. Distinguishing pages that focus on certain desired roles, like highly relevant agents or patients, enables more intent-based matching. Semantic roles can reveal details about events, like magnitude, duration, frequency, etc. to aid assessments of importance and relevance. Overall, semantic role labeling provides another lens for understanding the meaning of text that can ultimately improve search retrieval and ranking.
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This analyzes tables structured with entities, their attributes, and attribute values. For example, products with prices and sizes in tables or breast cancer (E); Treatments (A); chemotherapy, mastectomy, lumpectomy (V).
For webpages containing structured data in an entity-attribute-value format, analyzing these tables provides shortcuts for search algorithms to directly interpret the page’s content. The entities, their associated descriptive attributes, and the attribute values provide a pre-parsed view of key page information. This avoids the need for more complex NLP techniques to extract facts buried in unstructured text. Analysis of structured data also allows the segmentation of pages based on the types of entities they contain and their related attributes and values. This supports a more granular, intent-based search and ranking of results.
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This represents word meaning through statistical patterns of how words occur together. For example, the meaning of “pancake” based on words like “syrup” and “breakfast” that are often nearby.
Modeling the distributional semantics of words on a webpage, based on patterns of co-occurrence across large corpora, provides powerful contextual clues for search algorithms. By representing terms as high-dimensional vectors encoding their meaning, search engines can effectively measure semantic similarity beyond literal keyword matches. Comparing distributional profiles allows search algorithms to connect pages using related concepts, synonyms, analogies, and more. Distributional semantics also enables the categorization of pages based on their conceptual focus. This provides the basis for improved relevance ranking, query expansion, and more intelligent information retrieval overall.
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This looks at part-whole relationships between concepts and words. For example, it links “room” as part of “house”.
Analyzing meronymic part-whole relations expressed on webpages can provide search engines with useful hierarchical information about mentioned entities. Identifying pages that describe component parts of larger wholes enables better matching for navigational queries trying to drill down by attributes. For example, a page about “wheel alignment” may be highly relevant for a search seeking information on “auto maintenance”. Modeling part-whole hierarchies also provides overall categorical context about pages’ entities, like a “fin” being part of a “shark”. This allows search engines to leverage inferred connections between pages based on their related place within an ontological hierarchy.
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This looks at broad-narrow relationships between concepts, like “animal” and “dog”. It links general terms to more specific ones.
Identifying super-subordinate relationships between entities referenced on a webpage, including hypernyms, hyponyms and ISA relations, can help search algorithms better organize and categorize page content. Recognizing that a page mentioning “golden retrievers” also relates to “dogs” and “animals” categorizes it into a hierarchical taxonomy. This allows search engines to connect that page to broader, more general queries seeking information about “dogs”, even if that exact term is not present. Analyzing distribution of superordinates and subordinates discussed also provides signals about the contextual specificity of pages. This additional topical and categorical understanding enables improved relevance ranking.
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This connects different manners of doing something to the action itself. For example, “whispering” and “shouting” are troponyms of “speaking”.
Modeling troponymic relations, which connect manner of action to actions, provides useful contextual understanding for search algorithms. Identifying pages detailing specific “ways” of doing things allows search engines to better match them to queries seeking that type of information at an appropriate level of granularity. For example, pages discussing the troponyms “whispering” or “shouting” may be highly relevant for queries about “talking”. Analyzing troponyms also enables categorization and clustering of pages based on focus on common actions. This can power better recommendation of pages covering different methods and sub-topics around a high-level issue.
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This analyzes statistical language patterns across large collections of texts called corpora. For example, studying word frequencies across newspaper articles.
Analyzing statistical patterns in word usage across entire text corpora provides useful signals for search engines about the focus and meaning of pages. Techniques like distributional semantics modeling, word embedding, and lexical chaining rely on observing relationships between terms across a large body of texts. This enables search algorithms to interpret pages based on usage profiles of terms they contain, even in the absence of obvious keyword matches. Corpus-based analysis also facilitates categorization of pages using semantic similarity measures instead of just surface word matches. Incorporating these corpus insights enables search systems to retrieve and rank pages in ways more aligned with their true relevant meaning.
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This finds chains of related words that occur together in text. For example, a sequence like “house”, “room”, “kitchen” relates concepts.
Constructing lexical chains from webpages provides useful insights for search engines into key themes, concepts and relations described by the content. Tracing sequences of semantically related terms strung together throughout the text acts as a summary of important page topics and entities. This enables matching and ranking pages for queries seeking certain concepts or themes, even in absence of exact keyword matches. Length and centrality of lexical chains also helps search engines gauge the prominence and cohesiveness of topics contained within pages. Overall, lexical chain analysis provides a view into semantic essence of pages that goes beyond superficial keyword statistics.
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This uses statistics to find abstract topics that occur across documents. For example, discovering themes like “cooking” across recipes and blogs.
Applying topic modeling processes like LDA reveals the underlying semantic themes present within webpages based on aggregated word usage patterns. By clustering words and terms into probabilistically derived topics, search engines can overcome reliance on just keyword matching. Instead they can categorize and connect pages based on their composition of latent semantic topics, even without obvious term matches. Analyzing distributions over these learned topics provides insight into the primary foci of pages. Topic modeling thus enables more meaningful relevance ranking, categorization, and recommendation of conceptually related content.
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This groups things like documents or words based on shared attributes using algorithms. For example, clustering articles by topic words they contain.
Applying cluster analysis techniques enables search engines to categorize and group webpages based on shared characteristics. This provides an alternative to traditional keyword-based indexing that relies on surface term matching. Clustering algorithms can leverage various features like word usage, semantics, entities, structure, etc. to detect pages about related concepts. These clusters essentially create topic-based groupings of content, useful for serving users exploratory discovery of subjects related to their queries. Cluster membership also provides additional signals for ranking pages within search results through algorithms like cluster-based retrieval.
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This finds interesting associations between pieces of information like products purchased together. For example, people who buy cereal often also buy milk.
Mining association rules from webpage content provides insights about commonly co-occurring entities, attributes, and relationships. This detection of patterns like “product X is often purchased with product Y” within web documents enables new relevance signals for search ranking. Pages exhibiting associations strongly tied to user query terms can be prioritized due to implicitly related content. Associations also aid semantic query expansion to surface pages using related terminology. Analyzing evolution of association rules over webpage corpora timelines also informs assessments of freshness and importance.
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This looks at sequences of multiple words together, like pairs (bigrams) or triplets (trigrams). For example, studying the frequency of “strong tea”.
Examining n-gram use and frequencies within webpages provides useful signals about content topics and quality. Uncovering commonly used phrases acts as an additional metric beyond keyword analysis for understanding topical focus. Statistics on n-gram makeup also enable useful comparisons between pages to identify outliers with unusual phrasing. Normalizing metrics like term frequency-inverse document frequency help assess the informative value provided by certain n-grams based on their specificity. Analyzing n-gram evolution over time also provides clues into content freshness and importance of pages.
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This finds which words tend to appear together in phrases, like “strong tea”. It looks at these linguistic collocations.
Identifying characteristic collocated words and phrases in webpages provides useful signals about content topics and style. Statistics on common term combinations indicate semantic associations between concepts covered. Comparing collocation patterns between pages can also help segment content based on subject matter through techniques like similarity clustering. Analyzing collocations additionally provides cues about tone and level of formality based on phrasing choices. Unusual or atypical collocations may also indicate lower quality or autogenerated content worth demoting. Overall collocation analysis enables search engines to interpret pages beyond isolated keywords.
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This studies groups of words related by meaning, like different sports terms. For example, analyzing how words like “soccer”, “goalie”, and “foul” are related.
Examining clusters of related words and concepts present on pages provides a useful view into their themes and topics. Semantic field analysis goes beyond isolated terms to model meaningful groups of related vocabulary. This enables search engines to interpret pages based on which semantic fields they exhibit, even without direct keyword matches. Comparing distribution over fields also allows search algorithms to classify pages by subject matter and genre. Users likewise can browse or filter search results based on semantic field composition matching their interests and goals.
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This monitors outside information like news, data, and social media relevant to an organization. For example, scanning industry reports and competitors’ tweets.
Monitoring signals from a web page’s broader ecosystem, like social shares, inbound links, and platform interactions, provides useful contextual clues for search ranking beyond just on-page content analysis. Metrics on virality, external validation, and user engagement act as indicators of popularity and trustworthiness. Trends in referrer patterns also give clues about shifting attention and hot topics. Considering these environmental factors in scoring provides a more dynamic, responsive model of relevance compared to static content analysis alone.
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This measures how important a word is by comparing its frequency in a document vs. the collection. For example, “predator” has higher TF-IDF in an article about panthers compared to the term “fur.”
Leveraging statistics like term frequency-inverse document frequency (TF-IDF) provides useful signals for search engines to evaluate the informative significance of words within webpages. TF-IDF measures the relevance of terms based on their frequency within a document compared to the inverse of their prevalence across all documents. This helps surface distinctive keywords strongly associated with a page but rarely seen elsewhere. High TF-IDF terms often indicate the core informative content. Prioritizing pages with higher TF-IDF scores for their keyword matches thus helps retrieve results tightly focused on relevance to the query.
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This simply looks at how often words or concepts appear. For example, analyzing how many times “elephant” appears on a page about elephants.
Examining the raw term and entity frequencies observed within webpages enables useful insights for search ranking and classification. The degree of repetition of keywords and concepts provides clues about their relative importance to the content. Comparing frequency distributions can help segment pages discussing common vs niche topics based on relative differences. Changes in word usage rates over time also inform assessments of content freshness and attention trends. Overall, straightforward term frequency analysis provides a simple yet effective method for gauging page aboutness.
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This figures out the intended meaning of ambiguous words based on context. For example, determining if “bat” refers to the animal or baseball bat from context.
Resolving ambiguous words and phrases found on webpages based on intended meaning and context improves search engines’ comprehension. Techniques like word sense disambiguation leverage surrounding semantics to determine appropriate sense. Discovering pages using terms in senses closely matching the user intent enables better relevance matching. Semantic disambiguation also reduces noise from retrieving pages that happen to share ambiguous keywords but exhibit unrelated usage. Machine learning models can additionally leverage disambiguation to reduce dimensionality of semantic features.
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This examines attributes like formality, complexity, and tone of writing style. For example, detecting whether the language is professional versus casual.
Examining attributes related to writing style, tone and readability of webpages provides useful signals about content type, quality and target audience. Statistics on vocabulary complexity, sentence structure, formality, and media use classify pages across stylistic dimensions. Search engines can use these linguistic style insights to retrieve results better matching user preferences, like formal registers for scholarly queries versus casual language for pop culture searches. Style also provides proxies for assessing expertise level of pages. And highly atypical styles may indicate autogenerated or copied content of lower quality.
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This detects attitudes, opinions, and emotions expressed in text. For example, analyzing whether a movie review is positive or negative.
Detecting sentiment, opinions and attitudes expressed on webpages provides search engines useful contextual understanding beyond just topical facts. The subjective nature of content factors into relevance for many search intents seeking reviews, commentary, or critique. Sentiment analysis also enables filtering results to match a user’s desired affective stance, whether positive endorsement or critical perspectives. Trends in sentiment levels among pages on a topic provide clues about attention cycles and shifting opinions. This additional emotional layer expands search engines’ comprehension of meaning and purpose.
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This examines meaning in real-world context, like goals and use cases. For example, studying how language is used to instruct versus just inform.
Modeling real-world context and pragmatics represented in webpage content improves search engines’ ability to match relevance to user situations. Techniques like named entity recognition, knowledge graph integration, and Wikification add concrete entities, events, and background knowledge connected to page topics. This grounds the semantic interpretation in tangible details closer to the end application context. Analyzing pragmatics also aids disambiguation of intents like instructions versus informational queries. Incorporating pragmatics expands systems’ comprehension beyond just abstract language statistics.
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This compares patterns across different languages, like word associations. For example, analyzing how words group together differently in English and Spanish.
Leveraging insights across languages provides additional signals for search engines to connect, categorize and comprehend webpages. Statistical associations between translated vocabulary aid discovery of pages discussing similar topics across languages. Detecting aligned entities and facts in multilingual content, through techniques like cross-lingual entity linking, also help merge signals from different linguistic sources covering the related information. Analyzing divergent term usage and frequencies across languages enables filtering of results by regional relevance. Overall, cross-lingual signals enhance understanding of page content within its cultural and geographic context.
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This looks at text together with images, audio, and video. For example, analyzing articles together with their pictures.
Incorporating features from multimedia elements like images, videos, and structured data coupled with webpage text provides a more complete semantic interpretation. Computer vision techniques identify objects, scenes, and actions in visual media that give additional context to the textual content. Audio analysis extracts speech, tone, and objects like music to add supplemental acoustic signals. Structured data provides direct factual knowledge around page entities and relationships. Composing these disparate modalities enables more detailed modeling of the full information environment conveyed by webpages.
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This studies structure and meaning within dialogues and texts as wholes. For example, looking at how logically ideas flow from sentence to sentence.
Modeling the coherence, cohesion and discourse structure of webpages provides useful insights for assessing topical focus, quality and readability. Analyzing elements like anaphora resolution, lexical chains, entity transitions, syntactic patterns, argument structure and more reveals logical connections tying together a document’s themes. Pages exhibiting disjointed or fragmented discourse likely lack clear thematic focus. Discourse signals also inform text complexity metrics for targeting content to reader levels. Overall, discourse modeling provides a view into how successfully pages convey meaningful, coherent content.
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This examines stories, plots, and characters. For example, studying roles and arcs for characters in a novel.
For webpages presented as stories or containing narrative elements, analyzing characteristics like plot structure, characters, voices, themes and chronology provides useful contextual signals. Modeling narratives allows search engines to retrieve pages matching desired story attributes, like pages discussing certain character archetypes. Narrative role labeling categorizes page content by functional roles like hero, villain, moral, etc. Sentiment analysis over character mentions provides clues about their portrayal. Identifying setting details aids understanding of context. Overall, narrative modeling facilitates deeper comprehension of story-based page content and its connections to user intent.
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This relates texts to their historical context like eras and events. For example, connecting newspaper articles to the time periods they discuss.
For pages discussing past events, artifacts, people and eras, analyzing temporal signals provides key context for assessing relevance. Techniques like timestamping event mentions, modeling chronological order, and geopolitical entity linking create timelines of historical details described by content. This allows search engines to retrieve pages contextually matching desired time periods, even without direct keyword matches on dates or eras. Connecting pages mentioning aligned historical entities also enables discovery based on shared context. Overall, historical modeling facilitates temporally-aware matching.
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This identifies location-based patterns like regional terms or addresses. For example, a search query using British English might return different localized results than one done in American English.
Modeling geography-related information on webpages, like locations, distances, boundaries, and geopolitical entities, provides useful contextual understanding for search engines. Techniques like geotagging, geocoding and gazetteer entity linking associate mentions of places with real-world geographic coordinates and regions. This enables location-aware retrieval of pages localized to users’ current or target geographic context. Comparing concentration of geospatial mentions also helps filter pages by regional relevance. Incorporating geography provides grounding that aids relevance matching to locale-specific user needs.
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This relates language to its cultural context like values and traditions. For example, studying how holiday greetings differ between cultures.
Modeling cultural context provides useful signals for search engines to identify pages aligned with users’ societal perspectives and needs. Techniques like analyzing demographics, values, customs, trends, and social institutions mentioned in content enable culturally-aware search. Identifying pages exhibiting user preferences for individualism vs collectivism, power distance, uncertainty avoidance and other cultural dimensions facilitates personalization. Tracking diffusion of cultural concepts like idioms and identities over time also informs assessments of shift and importance. Overall, cultural modeling helps search algorithms select results resonating better with users’ situated cultural worldviews.
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This examines patterns within cultures/communities through language. For example, studying terminology used in medicine across different hospitals.
Examining patterns in topics, relationships, settings and artifacts depicted on webpages enables models of the cultural communities implicitly represented by the content. Network analysis of interactions and roles, conceptual topic extraction, and entity linking uncover social structures and environments associated with pages. Search engines can use these computational ethnographic insights to retrieve pages aligned with particular subcultures or fields of interest to users, even without explicit keywords. Analyzing emergent communities over time also reveals evolving affiliations, values and zeitgeists.
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This evaluates how accessible content is for people with disabilities. For example, checking if images have text descriptions.
Evaluating webpage accessibility based on inclusive design principles provides useful quality signals for search ranking and Improving accessibility promotes inclusion, enlarges reachable audience, and enhances overall user experience. Techniques like checking color contrast ratios, parsing document structure, and assessing complexity of language quantify the degree of accessibility supported by page design and content. Search engines can factor accessibility scores into relevance rankings to promote pages exhibiting good practices, essential for users with disabilities. Accessibility analysis also gives site owners feedback to guide improvements.
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This interprets visual symbols, signs, and meanings. For example, analyzing what a red traffic light communicates.
Interpreting visual signals on webpages, like images, layout, color, shapes, and videos, provides additional contextual clues for search engines beyond just text. Visual semiotics analysis extracts meanings associated with signs and symbols commonly used in different cultures and contexts. This facilitates topical categorization of pages based on their design elements and image contents. For example, particular graphic symbols strongly associated with a concept may indicate related content without keyword matches. Analyzing alignment of visual signals with text also measures consistency and provides checks for manipulation. Overall, visual semiotics modeling enables richer comprehension of pages.
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This evaluates how well content follows laws, regulations, and policies. For example checking if privacy policies follow legal requirements.
Evaluating legal, regulatory, and policy implications described on webpages provides useful signals about their reliability, goals, and target users. Reference extraction, entity analysis, and text summarization can identify key laws, rules, norms, and compliance features indicated in page content. Search engines can leverage these insights to retrieve government pages adhering to transparency and ethics regulations when those values are sought. Compliance analysis also enables filtering results appropriate for minors in educational contexts. This facilitates search experiences meeting user needs within societal legal and ethical constraints.
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This examines the psychology of how language is produced and processed. For example, studying how long it takes people to read certain sentences.
Probing webpage text for psycholinguistic attributes reflective of the author provides useful signals regarding expertise, trustworthiness, and intentions. Deception prediction, reading ease metrics, and stylometry reveal insights about creators perceptible in language patterns. Search engines can thus filter pages based on psycholinguistic profiles indicating desired knowledge-level, frankness and objectivity qualities. Analyzing writing also gives clues about organizational or industry norms authors belong to. Additionally, unusual changes in psycholinguistics may signal impactful external events on authors.
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This studies the sounds and pronunciation patterns of spoken words. For example, analyzing how vowel sounds differ across languages.
Examining the phonetic and phonological patterns in spoken audio and video content associated with webpages provides additional signals for search engines beyond text alone. Speech recognition transcribes spoken words containing phonetic clues about topics, sentiment, accent, origin, and more. Distinct phoneme distributions indicate pronunciation shifts tied to demographics like regional dialects. Analyzing prosodic features in speech like tone, stress, and rhythm conveys emotion and meaning. Phonetics also aid speech normalization for automatic transcription. Overall, phonetic modeling enables richer comprehension from pages’ multimedia.
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This looks at the structure and forms of words. For example, studying prefixes like “un-” and suffixes like “-ness”.
Examining the internal structure and morphology of words appearing on webpages provides clues about language conventions, origins, topics, and semantics. Segmenting words into component morphemes like roots and affixes enables better vocabulary understanding and expansion. Identifying common morphological patterns aids search engines in grouping pages from similar language backgrounds. Analyzing morphological complexity also informs text difficulty metrics for result targeting and readability scoring. Overall, morphological modeling facilitates stronger comprehension of pages’ lexical composition and linguistic context.
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This identifies metaphorical figures of speech and analyzes their meaning. For example, determining what the metaphor “time is money” implies.
Detecting metaphorical expressions in webpage text and interpreting their implied meanings provides search engines additional signals regarding topics and reader experience. Metaphors creatively convey concepts by linking seemingly disconnected semantic domains. Identifying common source and target domains thus reveals abstractions and qualities associated with page contents. Differentiating literal versus figurative language also reduces incorrect parsing and aids text simplification. Furthermore, the prevalence of metaphors acts as a stylistic indicator of descriptive richness and reading complexity.
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This looks at relational adjectives that link entities, like “atomic” in “atomic physics”. For example, the pertainym “culinary” relates to cooking.
Analyzing pertainyms, or relational adjectives that characterize types of connections between entities, provides useful signals regarding key relationships and properties described on webpages. Pertainyms compactly encode contextual attributes and semantics. For example, phrases like “atomic physics” and “criminal lawyer” efficiently convey domain associations. Identifying pertinent adjective modifiers enables better comprehension of entity relations mentioned in text. Comparing pertainym co-occurrence patterns also informs similarity assessments between pages based on shared relational phrases. Overall pertains provide a concise lexical lens into semantic associations.
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This identifies similar words like synonyms. For example, it recognizes “happy” and “glad” as synonyms.
Accounting for synonymous lexical variations on webpages expands search engines’ topical comprehension beyond literal term matching. Recognizing pages using equivalent phrasing through different words counters vocabulary limitation. Analyzing distributions over synsets provides additional signals for categorizing pages by conceptual focus. Connecting documents based on shared synonyms also enables discovery across lexical boundaries. In general, incorporating synonymy facilitates retrieval of relevant pages despite surface linguistic variability.
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This identifies contrasting word relationships like “hot” vs “cold”. It looks at antonyms.
Accounting for antonymic oppositions and contrasts in the vocabulary of webpages improves search engines’ understanding of topics discussed from multiple perspectives. Identifying antonyms signals presence of competing or conflicting views around concepts. For queries seeking debate and comparison, prioritizing pages exhibiting high antonym density provides more balanced results. Contrastive metrics also help distinguish pages oriented towards positivity/negativity. And tracking changes in antonym usage over time reveals shifting opinions and attention cycles.
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This identifies whole-part relationships like “car” and “engine”. It analyzes these holonyms.
Modeling holonymic whole-part and whole-substance relations expressed on webpages provides additional hierarchical category knowledge. Identifying meronomies allows search engines to infer connections between pages describing related whole-part concepts, like product features. Holonyms also aid query understanding at different levels of abstraction or composition. Users can pivot results between wholes and parts based on shifting needs. Analyzing distribution across holonym levels further informs page specificity and scope. Overall, holonymy understanding adds nuanced vertical contextualization.
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This identifies causal relationships between events and concepts. For example, extracting cause-effect links like “rain causes flooding”.
Detecting and modeling causal relations expressed in webpage text provides useful signals about significant events, influencers, and explanatory connections. Recognizing causal statements enables search engines to better match pages discussing causes or effects of user-specified concepts. Causal chains also highlight impactful entities affecting downstream events. Prioritizing highly causal pages rewards informative explanations over isolated facts. Temporal analysis of causal directionality provides clues about precedent conditions vs outcomes. In general, causal reasoning strengthens search engines’ comprehension of critical relations between real-world events and entities.
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This examines the functions, purposes, and uses of entities. For example, analyzing the functions of different website features.
Evaluating the functional roles, purposes, and applications described for entities on a webpage provides additional contextual cues for search relevance. For example, identifying key physical functions of objects or procedural goals of activities enables improved matching for intent-oriented queries. Functional knowledge also helps categorize pages based on use cases and practical domains, like tools for gardening. Analyzing user tasks and behaviors associated with functions provides signals about needs and goals. Generally, functional semantics expand comprehension beyond literal topics.
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This identifies hierarchical relationships like rankings and levels. For example, levels like “country > state > county”.
Modeling hierarchical properties, taxonomic structures, and nested categorization relationships expressed on webpages provides additional signals about the specificity, scope, and categorization of content. Identifying hypernymic/hyponymic is-a relations enables inference of pages discussing superclasses and subclasses of query topics. Recognizing hierarchical rankings, levels, and priorities conveys important differentiating ordering. And taxonomic trees aid query understanding and intent disambiguation at various granularities. Overall, hierarchy adds useful dimensional structure.
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This extracts conceptual models, types, and relationships. For example, analyzing concepts like “professor teaches course” in a university domain model.
Interpreting the ontological concepts, knowledge representations, and semantic abstractions encoded on webpages aids better comprehension of meaning for search engines. Ontology modeling formalizes significant types of entities, their attributes, classifications, and relationships within a domain. Matching these knowledge graphs enables conceptual query understanding. The ontology also provides an abstract vocabulary for comparing and categorizing pages at a higher semantic level. Overall, ontological analysis elevates modeling beyond superficial keywords and topics.
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This represents relationships between entities as network graphs. For example, linking related pages based on shared keywords.
Analyzing webpage content modeled as graphs and networks enables powerful semantic insights through topological techniques. Knowledge graphs convey conceptual relations between entities. Co-occurrence networks capture statistical keyword associations. Hyperlinks show interconnections between documents. Applying graph theory fosters analysis like clique detection, centrality ranking, clustering, and link prediction. These structural inferences supplement traditional NLP, providing signals about meaningful connections, key entities, and community discovery within the linked information network.
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This recognizes new concepts without example training data by transferring knowledge. For example, identifying new animal species based on their descriptions.
Zero-shot learning techniques allow search engines to recognize new semantic concepts, intents, and topics on webpages without explicit training examples. Knowledge transfer from known categories enables inference of relevant pages for queries about previously unseen subjects. For instance, word embeddings and semantic graphs can associate new class labels to proximate observed data points. This provides generalized adaptive comprehension to identify pages relevant to emerging or rare search topics with minimal to no direct signals. Zero-shot learning thereby expands search scope to better match obscure user information needs.
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This links pronouns and references to the entities they refer to. For example, resolving “she” refers to “Emily”.
Resolving anaphoric references and entity coreferences expressed on webpages clarifies key connections and improves comprehension coherence for search algorithms. Identifying the entities aligned to pronouns and abbreviated mentions enables clearer knowledge of significant page topics and their contextual relationships. This facilitates matching user intents seeking pages about specific entity roles and interactions. Improved coherence also aids assessments of topical focus within content. Overall anaphora resolution reduces ambiguity by tying disparate statements together into unified semantics.
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This figures out which meaning of a word is used in context. For example, determining if “bank” refers to a financial bank or river bank.
Discovering latent word senses and modeling meaning in context provides search engines more nuanced comprehension of webpage contents. Words can exhibit multiple senses based on usage, so inducting these meanings from data provides better vocabulary understanding compared to just dictionaries. Disambiguating intended sense then reduces inaccurate semantic matching. This enables search algorithms to retrieve results tuned to precise definitions rather than ambiguous keywords. Sense distributions also inform topic clustering. Overall sense induction and disambiguation yields improved lexical semantics.
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This represents words through numeric vectors encoding semantic meaning. For example, encoding the word “cat” as a list of numbers representing its meaning.
Representing webpage text via dense word embeddings provides search systems expressive semantic comprehension capabilities. Word vectors encoding similarity relations offer nuanced alternatives to exact keyword matching. Search relevance functions can compute vector similarity between query and document terms. Clusters within the embedding space reveal semantic topics and relationships between pages. Trends in vector usage over time indicate changing language. Overall, word embeddings supply rich semantic genome for meaning-based indexing and retrieval.
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This examines taxonomic classification relationships and hierarchies. For example, analyzing how animals are classified into groups like mammals and reptiles.
Modeling taxonomic classifications and type hierarchies referenced in webpage content provides useful categorization signals. Identifying taxonomies and ontologies with is-a relationships enables search engines to infer connections between pages discussing subclass-superclass entities. Matching user queries to appropriate levels in a taxonomy disambiguates intent granularity. Comparing distribution across taxonomic branches also informs page specificity. Overall, leveraging taxonomies provides an organizing conceptual framework to enhance topical understanding.
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This evaluates patterns of connections in sequences and networks. For example, analyzing flows through webpage navigation paths.
Tracing and evaluating paths through networks representing relations between webpage contents provides useful insights. Paths may model sequences like document flows, transitions between entities and topics, hyperlinks, etc. Analyzing path shape, length, convergence, cycles and more reveals patterns within the information network. This facilitates queries about specific connections or flows. Path segmentation also identifies salient subsequences and endpoints. Overall, path-based techniques move beyond individual nodes to model trajectories through page content.
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This identifies when different text strings refer to the same real-world entity. For example, linking “NYC” and “New York City” as the same place.
Identifying equivalent entity mentions across webpages enables connecting information about real-world objects unambiguously. Different pages may reference the same entity using varying surface forms. Entity resolution clusters these lexical variations by the unique underlying entity. This allows aggregation of all relevant pages on a topic even when they lack consistent names or identifiers. More accurately consolidating signals improves ranking quality. It also helps identify authoritative entity representations for disambiguation.
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This links mentions of entities to knowledge bases about them. For example, linking “Barack Obama” to his Wikipedia article.
Linking entity mentions on webpages to knowledge bases provides unambiguous conceptual grounding and expanded contextual understanding. Recognizing real-world entities enables direct integration of factual knowledge. Linking together entities into graphs captures semantic relations and events extracted from the content. This rich network represents key actors, concepts and relationships described by pages. By integrating structured knowledge, search systems move beyond bags-of-words to represent salient entities and relations.
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This identifies multiple expressions that refer to the same entity. For example, linking “Mr. Obama” and “Barack” as referring to the same person.
Identifying multiple expressions and mentions referring to the same entities across webpage contents connects knowledge about real-world objects discussed. Different references to a shared entity strengthen signals about its contextual relevance. Connecting pronouns and abbreviated aliases back to full entity specifications also improves coherence and accuracy. Analyzing concentrations of entity mentions further highlights dominant page topics. Overall, co-reference resolution enhances understanding by tying disparate expressions together into unified semantics about key entities.
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This identifies the sources and provenance of entities. For example, analyzing who created a piece of data.
Analyzing statements of attribution, ownership, responsibility and influence associated with entities referenced in webpage text provides useful context. Google uses this in its Experience-Expertise-Authority-Trust quotient. Extracting source relations and provenance conveys important real-world perspectives. For example, identifying influential creators and authors provides authority clues for ranking and credibility. Sentiment expressed towards entities also enables perspective-based clustering. Overall, modeling attribution patterns helps search engines better situate page contents relative to significant sources and stakeholders.
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This categorizes entities into types and classes. For example, classifying people, places, organizations, etc.
Categorizing entities mentioned on webpages into a taxonomy class hierarchy enables better semantic comprehension for search compared to treating names as just strings. Recognizing class membership provides critical context – location entities behave differently than person entities. Classes also enable inheritance of attributes and relationships from more general types. Overall, entity classification gives structure to extracted knowledge which improves downstream reasoning. Classes additionally allow grouping pages discussing similar types of objects.
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This identifies the sentiment expressed about entities. For example, detecting positive or negative opinions about products.
Detecting sentiment expressed towards entities referenced in webpage text provides useful signals about opinions and attitudes. Pages containing positive or negative sentiment towards query entities can be prioritized to match desired perspectives. Clustering entities by sentiment patterns also groups perspectives. Comparative sentiment helps gauge controversy and discern disputing viewpoints. Overall, the contextual stances surrounding entities offer insights that complement factual knowledge for search.
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No. We do not know which of the language model analyses Google or Bing uses. And just because a patent is filed on a given model, doesn’t mean that model is actually being used in the ranking algorithm. However, from looking at existing patents, we can infer what might be in each of the search engine’s purview. Some of these models might be included in the future or could be in use without a patent existing for that usage The following Google patents relate to specific analysis types above.
I believe in giving credit where it’s due. My friend Ed Baker, whose website can be found at https://www.edwardabaker.com/, compiled this initial list of models and is currently developing a tool designed to aggregate the key advantages produced by each model for a specific keyword, search query, or entity. Ed has granted us access to beta-test the tool’s results, and it’s proving to be fantastic. This experience sparked my curiosity about how each model might be incorporated further into an SEO strategy. Hence, this long article. Once his still-unnamed tool is officially released, I’ll provide a link to it in this article. Our testing has shown that the tool is intriguing and useful for content mapping and creating article outlines. It eliminates the uncertainty in site structures and article outlines by assigning weight to related keywords/entities, based on the presentation of that term in relation to the primary keyword by each of the 63 models. Essentially, the tool identifies the nearest nodes based on the LLM models previously listed.