Lexical Semantics, Semantic Similarity and Relevance for SEO

This document summarizes how Google search results are evolving to include more semantic data through direct answers, structured snippets, and rich snippets. It provides examples of direct answers being extracted from authoritative sources using natural language queries and intent templates. It also discusses how including structured data like tables, schemas, and markup can help search engines understand and display page content in a more standardized way. While knowledge-based trust is an interesting concept, current search ranking still primarily relies on link analysis and does not consider factual correctness.

A look at search-related patents from Google that people who do SEO may be interested in learning about

The document describes a Python script that can automatically generate new subcategories for an ecommerce website based on clustering product names. It discusses: - Using NLTK to generate n-grams from product names to cluster related products - Filtering the n-grams to keep only those with commercial value by checking for search volume and CPC data - Running the script on a large home improvement site to identify over 1,650 new subcategory opportunities with a total search volume of over 13 million - Sharing the script so others can automate subcategory identification for their own sites to scale up an important SEO tactic.

1) Knowledge graphs are structured databases that represent real-world entities and their relationships to each other. They help search engines like Google understand topics at a deeper level. 2) Entities (topics) are becoming more important than keywords for search engines to understand content. Google's entity understanding can be checked using their natural language processing tool. 3) Semantic SEO techniques like tightly linking topics both internally and to relevant external pages can help improve how search engines understand and represent the entities within a website through their knowledge graphs.

What percentage of an Inbound marketer's day doesn't involve working with spreadsheets? How much of this work is time-consuming and repetitive? In this interactive session, you will learn how to manipulate Google Sheets to automate common data analysis workflows using Python, a very easy to use programming language.

Bill Slawski presented a webinar on analyzing patents related to search engines and SEO. He discussed 12 Google patents covering topics like PageRank, Google's news ranking algorithm, analyzing images to detect brand penetration, and building user location history. The patents described Google's work in building knowledge graphs from web pages, ranking entities in search results, question answering, and determining quality visits to local businesses.

This document provides SEO metrics and comparisons for the website hangikredi.com over several time periods between April 2019 and September 2019. It shows substantial increases in key metrics like organic traffic, clicks, impressions, and average position after Google algorithm updates in May, June, July, and September. However, it also shows significant drops in these metrics during a server outage in early August. Overall the data demonstrates the site's strong SEO performance and organic growth over the 6-month period analyzed.

This document discusses digital marketing strategies focused on establishing authority through valuable, timeless content. It recommends creating content such as articles, videos, and academic papers on topics that will remain relevant for years to establish expertise. Creating a steady stream of high-quality content over time builds an online presence and credibility without major risks of losses, and may lead to job offers, clients, or other opportunities. It provides examples of interactive dashboards and open-source software that gained popularity and users by continuously publishing improvements and documentation without needing to rely on things like resumes or company profiles.

My talk from BrightonSEO 2021; focusing on using Google's image category labels (glancing into the Knowledge Graph and Google's image annotation processes) for better topic research and content optimization.

Semantic Publishing is publishing a page on the Internet by adding a semantic layer (i.e., semantic enrichment) in the form of structured data that describes the page itself.

This document provides an overview of entity SEO, including: - What an entity is and why entity SEO is important as search engines have evolved from information engines to knowledge engines - How search algorithms like Panda, Penguin, and Hummingbird helped drive this transition by prioritizing high-quality content over low-quality sites - Techniques for entity SEO including entity research, topical maps, schema, internal linking, and case studies - Tools like Google's Knowledge Graph that can help with entity research and understanding how entities are ranked

How to approach SEO in a world where Google has moved from strings and keywords to things, topics and entities. Dixon JOnes is the CEO of InLinks, who have build a proprietory NLP algorithm and Knowledge Graph designed for the SEO Industry.

1) Google uses various techniques to extract structured information like entities, relationships, and properties from unstructured text on the web and databases. This extracted information is then used to generate knowledge graphs and provide augmented responses to user queries. 2) One key technique is to identify patterns in which tuples of information are stored in databases, and then extract additional tuples by repeating the process and utilizing the identified patterns. 3) Google also extracts entities from user queries and may generate a knowledge graph to answer questions by providing information about the entities from sources like its own knowledge graph and information extracted from the web.

Google's search results now include entities and concepts. Entities refer to people, places, things, and 20-30% of queries are for name entities. Google uses meta data like Freebase to build a taxonomy of entities and their relationships. This supports features like the Knowledge Graph, which provides information panels, and allows querying of nearby entities which may soon be available in search results.

The document discusses using Python for SEO applications such as data extraction, preparation, analysis, machine learning and deep learning. It provides an agenda and examples of using Python to solve challenging SEO problems from site migrations and traffic losses. Methods demonstrated include pulling data from Google Analytics, storing in DataFrames, regular expression grouping, and training machine learning models on page features to classify page groups and identify losses. Later sections discuss using deep learning with computer vision models to classify web pages from screenshots.

This document discusses internal linking strategies and techniques. It begins by explaining the benefits of connecting entities within content, rather than just words, and translating those connections into internal links. It then provides an overview of technologies like PageRank, the reasonable surfer algorithm, topical PageRank, chunking, and natural language processing that search engines use to understand contexts and how those ideas can be applied to internal linking at scale. Specific options for approaches to internal linking existing pages are also outlined.

Whilst passage indexing may seem like a small tweak to search ranking, it is potentially much more symptomatic of the beginning of a fundamental shift in the way that search engines understand unstructured content, determine relevance in natural language, and rank efficiently and effectively. It could also be a means of assessing overall quality of content and a means of dynamic index pruning. We will look at the landscape, and also provide some takeaways for brands and business owners looking to improve quality in unstructured content overall in this fast changing landscape.

English dictionaries since 1755 have attempted to present succinct statements of the meaning(s) of each word. A word may have more than one meaning but, so the theory goes, each meaning can in principle be summarized in a neat paraphrase that is substitutable (in context) for the target word (the definiendum). Such paraphrases must be so worded that the the substitution can be made without changing the truth of what is said – salva veritate, in Leibniz’s famous phrase. Building on Leibniz, philosophers of language such as Anna Wierzbicka have argued that the duty of the lexicographer is to “seek the invariant”. In this presentation, I argue that this view of word meaning and definition may be all very well as a principle for developing stipulative definitions of terminology in scientific discourse, but it has led to serious misunderstandings about the nature of meaning in natural language, creating insuperable obstacles for the understanding of how word meaning works. As a result, linguists from Bloomfield to Chomsky and philosophers of language from Leibniz to Russell – great thinkers all – have been unable to say anything true or useful about meaning in language. I argue that, instead, lexicographers should aim to discover patterns of word use in large corpora, and associate meanings with patterns instead of (or as well as) words in isolation. They should also distinguish normal uses of each word from exploitations of norms.

This lectures provides students with an introduction to natural language processing, with a specific focus on the basics of two applications: vector semantics and text classification. (Lecture at the QUARTZ PhD Winter School (http://www.quartz-itn.eu/training/winter-school/ in Padua, Italy on February 12, 2018)

study or concern about what kinds of things exist what entities there are in the universe. the ontology derives from the Greek onto (being) and logia (written or spoken). It is a branch of metaphysics , the study of first principles or the root of things.

Exploring the US 2010 Plain Language Act and other countries are exploring options. Paul Danon, UK compares guides and discusses what's out there, the need for collaboration and ethical implications.

folksonomy, social tagging, tag clouds, automatic folksonomy construction, word clouds, wordle,context-preserving word cloud visualisation, CPEWCV, seam carving, inflate and push, star forest, cycle cover, quantitative metrics, realized adjacencies, distortion, area utilization, compactness, aspect ratio, running time, semantics in language technology

The document summarizes discovery services adoption rates among libraries. EBSCO's discovery service, EDS, has the most subscribers with over 5,600 libraries. OCLC reports over 1,700 libraries have access to WorldCat Local, though fewer use it as their primary interface. Ex Libris has licensed Primo to over 1,400 libraries, and ProQuest reports 673 libraries using Summon. The document also discusses features of EDS, including integration with library catalogs and course management systems, relevance ranking, and development of applications using the EDS API.

This document discusses strategies for legal reading, research, and writing. It begins by exploring how people read texts, maps, and music, and how these insights could apply to reading law. It then addresses organizing legal research using citation managers. Finally, it provides guidance on academic legal writing, including different forms of writing, strategies for writing within constraints, planning approaches, and addressing introductions and problem-solving writing specifically. Throughout, it draws on research and references various scholars to support its discussion.

The document discusses natural language and natural language processing (NLP). It defines natural language as languages used for everyday communication like English, Japanese, and Swahili. NLP is concerned with enabling computers to understand and interpret natural languages. The summary explains that NLP involves morphological, syntactic, semantic, and pragmatic analysis of text to extract meaning and understand context. The goal of NLP is to allow humans to communicate with computers using their own language.

This document discusses information retrieval and describes its three main phases: 1) asking a question to define an information need, 2) constructing an answer by matching queries to documents, and 3) assessing the relevance of the retrieved answers. It also covers several important information retrieval concepts like keywords, indexing documents, stemming words, calculating TF-IDF weights, and evaluating system performance using recall and precision.

RECURSOS, HERRAMIENTAS Y NUEVAS TECNOLOGÍAS PARA LOS ESTUDIOS INGLESES Practice session 3 Group B. Pascual Pérez-Paredes

Pragmatics is the study of language use and context. It examines how the context, both situational and linguistic, affects the meaning of utterances. An utterance is the smallest unit of speech studied in pragmatics. Pragmatics focuses on the speaker's intended meaning rather than just the grammatical form. The interpretation of an utterance depends on its semantic content and environment. Contextual factors like the social and situational background condition both the production and understanding of utterances.

This document discusses natural language processing (NLP) and language modeling. It covers the basics of NLP including what NLP is, its common applications, and basic NLP processing steps like parsing. It also discusses word and sentence modeling in NLP, including word representations using techniques like bag-of-words, word embeddings, and language modeling approaches like n-grams, statistical modeling, and neural networks. The document focuses on introducing fundamental NLP concepts.

This document discusses several key concepts in linguistics including: 1. The autonomy of syntax - the theory that syntax operates independently of meaning and pragmatics. 2. Compositionality - the principle that the meaning of a phrase or sentence can be derived from the meanings of its parts and their structure. 3. Conservative vs innovative forms - conservative forms change little over time while innovative forms undergo more recent changes. 4. Prescriptivism - the belief that there are correct and incorrect ways to use language based on explicit rules imposed on speakers. 5. Methods of linguistic research include collecting primary and secondary data using tools like interviews, observations, and questionnaires for qualitative and quantitative analysis.

This presentation was provided by John G. Dove of Credo Reference during the NISO event "Next Generation Discovery Tools: New Tools, Aging Standards," held March 27 - March 28, 2008.

This document provides an overview of the legal research process. It defines what law is and discusses the different types of legal authorities, including statutes passed by Congress, regulations by executive agencies, and case law interpretations by courts. It then outlines the steps to conduct legal research, including developing search terms, choosing appropriate research tools like legal databases and books, searching strategically, evaluating sources, and refining searches. The document provides examples of searching legal databases like LexisNexis and Westlaw and managing citations. The overall process is iterative, beginning with forming a research question and repeating searches across different tools and terms until enough information is found.

The document discusses knowledge representation (KR) and different approaches to KR, including: 1) KR provides a surrogate for reasoning about the world by representing knowledge in a computable format. It determines how an agent thinks about the world. 2) Logics like propositional and predicate/first-order logic use symbols and rules to represent knowledge unambiguously, though they have limitations in expressiveness. 3) Semantic networks, frames, and conceptual graphs are other non-logical KR that focus on expressiveness, simplicity, and formality over logic-based representations. They provide flexible ways to represent objects, attributes, and relationships.