This document provides an overview of Google BERT and what it means for SEOs and marketers. Some key points:
- BERT uses bidirectional transformers to better understand the context of words in search queries and content. It helps Google resolve ambiguity and understand nuanced language.
- BERT was first introduced as an academic research paper in 2018 and was quickly adopted by Google and other major tech companies to improve natural language understanding.
- While BERT only impacts around 10% of queries, it represents a major improvement in Google's ability to understand user intent and has important implications for SEO, international search, and conversational search.
What is BERT? It is Google's neural network-based technique for natural language processing (NLP) pre-training. BERT stands for Bidirectional Encoder Representations from Transformers. It was opened-sourced last year and written about in more detail on the Google AI blog. In this presentation we look at what Google BERT means for SEOs and marketers and how Google BERT is and will continue to impact the search landscape. We also look at the back story to Google BERT, including transformers and natural language understanding and computational linguistics.
Google BERT and Family and the Natural Language Understanding Leaderboard RaceDawn Anderson MSc DigM
Natural Language Understanding and Word Sense Disambiguation remains one of the prevailing challenges for both conversational and written word. Natural language understanding attempts to untangle the 'hot mess' of words between more structured data in content, but the challenge is not trivial, since there is so much polysemy in language. Some recent developments in machine learning have seen significant leaps forward in understanding more clearly the context (and therefore user intent and informational need at time of query). Here we will explore these developments, and some of their implementations and seek to understand what this means for search strategists and the brands they support both now and into the future.
Natural Language Processing and Search Intent Understanding C3 Conductor 2019...Dawn Anderson MSc DigM
This talk looks at the ways in which search engines are evolving to understand further the nuance of linguistics in natural language processing and in understanding searcher intent.
As the volume of content continues to grow exponentially helping search engines to understand context and the topical themes within your site is increasingly important. Understanding some of the concepts are covered and also ways to utilise these in your marketing strategy.
Talk from Tech SEO Boost 2019 by Dawn Anderson on the move to the just in time predictive personalised search experience for search engines and users. Exploring recommender systems, collaborative filtering, temporal and location based queries and the rise of predictive, personal dynamic search. Exploring the work of information retrieval researchers and Google Discover.
The document discusses various types of plagiarism such as copy-and-paste plagiarism, word switching, using another's ideas without citation, and more. It provides examples of plagiarized content and the proper ways to cite sources, including using quotation marks for verbatim quotes and citing the author and year. The last section notes that one should use their own words as much as possible, always give credit to sources, and either quote or paraphrase and cite when using others' work to avoid plagiarism.
Natural Language Processing (NLP) & Text Mining Tutorial Using NLTK | NLP Tra...Edureka!
** NLP Using Python: - https://www.edureka.co/python-natural-language-processing-course **
This Edureka PPT will provide you with a comprehensive and detailed knowledge of Natural Language Processing, popularly known as NLP. You will also learn about the different steps involved in processing the human language like Tokenization, Stemming, Lemmatization and much more along with a demo on each one of the topics.
The following topics covered in this PPT:
1. The Evolution of Human Language
2. What is Text Mining?
3. What is Natural Language Processing?
4. Applications of NLP
5. NLP Components and Demo
Follow us to never miss an update in the future.
Instagram: https://www.instagram.com/edureka_learning/
Facebook: https://www.facebook.com/edurekaIN/
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Natural language processing (NLP) involves developing systems that allow computers to understand and communicate using human language. NLP aims to understand syntax, semantics, and pragmatics. It addresses challenges like ambiguity, where a sentence can have multiple possible meanings. Syntactic parsing is the process of analyzing a sentence's structure using a context-free grammar to produce a parse tree. Top-down and bottom-up parsing are two approaches to syntactic parsing where top-down starts with the start symbol and bottom-up starts with the sentence's terminal symbols.
What is BERT? It is Google's neural network-based technique for natural language processing (NLP) pre-training. BERT stands for Bidirectional Encoder Representations from Transformers. It was opened-sourced last year and written about in more detail on the Google AI blog. In this presentation we look at what Google BERT means for SEOs and marketers and how Google BERT is and will continue to impact the search landscape. We also look at the back story to Google BERT, including transformers and natural language understanding and computational linguistics.
Google BERT and Family and the Natural Language Understanding Leaderboard RaceDawn Anderson MSc DigM
Natural Language Understanding and Word Sense Disambiguation remains one of the prevailing challenges for both conversational and written word. Natural language understanding attempts to untangle the 'hot mess' of words between more structured data in content, but the challenge is not trivial, since there is so much polysemy in language. Some recent developments in machine learning have seen significant leaps forward in understanding more clearly the context (and therefore user intent and informational need at time of query). Here we will explore these developments, and some of their implementations and seek to understand what this means for search strategists and the brands they support both now and into the future.
Natural Language Processing and Search Intent Understanding C3 Conductor 2019...Dawn Anderson MSc DigM
This talk looks at the ways in which search engines are evolving to understand further the nuance of linguistics in natural language processing and in understanding searcher intent.
As the volume of content continues to grow exponentially helping search engines to understand context and the topical themes within your site is increasingly important. Understanding some of the concepts are covered and also ways to utilise these in your marketing strategy.
Talk from Tech SEO Boost 2019 by Dawn Anderson on the move to the just in time predictive personalised search experience for search engines and users. Exploring recommender systems, collaborative filtering, temporal and location based queries and the rise of predictive, personal dynamic search. Exploring the work of information retrieval researchers and Google Discover.
The document discusses various types of plagiarism such as copy-and-paste plagiarism, word switching, using another's ideas without citation, and more. It provides examples of plagiarized content and the proper ways to cite sources, including using quotation marks for verbatim quotes and citing the author and year. The last section notes that one should use their own words as much as possible, always give credit to sources, and either quote or paraphrase and cite when using others' work to avoid plagiarism.
Natural Language Processing (NLP) & Text Mining Tutorial Using NLTK | NLP Tra...Edureka!
** NLP Using Python: - https://www.edureka.co/python-natural-language-processing-course **
This Edureka PPT will provide you with a comprehensive and detailed knowledge of Natural Language Processing, popularly known as NLP. You will also learn about the different steps involved in processing the human language like Tokenization, Stemming, Lemmatization and much more along with a demo on each one of the topics.
The following topics covered in this PPT:
1. The Evolution of Human Language
2. What is Text Mining?
3. What is Natural Language Processing?
4. Applications of NLP
5. NLP Components and Demo
Follow us to never miss an update in the future.
Instagram: https://www.instagram.com/edureka_learning/
Facebook: https://www.facebook.com/edurekaIN/
Twitter: https://twitter.com/edurekain
LinkedIn: https://www.linkedin.com/company/edureka
Natural language processing (NLP) involves developing systems that allow computers to understand and communicate using human language. NLP aims to understand syntax, semantics, and pragmatics. It addresses challenges like ambiguity, where a sentence can have multiple possible meanings. Syntactic parsing is the process of analyzing a sentence's structure using a context-free grammar to produce a parse tree. Top-down and bottom-up parsing are two approaches to syntactic parsing where top-down starts with the start symbol and bottom-up starts with the sentence's terminal symbols.
This document provides an introduction to natural language processing (NLP) through a presentation given by Rutu Mulkar-Mehta. The presentation covers understanding natural language, common NLP tasks like text categorization and sentiment analysis, and challenges like ambiguity. It also discusses part-of-speech tagging and linguistic resources. The overall goal is to introduce attendees to the field of NLP and some of its applications.
The document discusses various types of plagiarism including copy-paste plagiarism, word switch plagiarism, style plagiarism, idea plagiarism, metaphor plagiarism, and summarizes the correct methods for citing sources to avoid plagiarism such as using quotations and citing authors. It also provides examples of each type of plagiarism and the appropriate way to reference sources using in-text citations and bibliographies.
How is mobile technology and search engine tuning evolving to meet the needs of users? Here we look at recent developments in research, implementations by search engines, and how to look at reach users can adapt their strategies to take into account these next-level changes.
Stemming And Lemmatization Tutorial | Natural Language Processing (NLP) With ...Edureka!
( **Natural Language Processing Using Python: - https://www.edureka.co/python-natural... ** )
This PPT will provide you with detailed and comprehensive knowledge of the two important aspects of Natural Language Processing ie. Stemming and Lemmatization. It will also provide you with the differences between the two with Demo on each. Following are the topics covered in this PPT:
Introduction to Big Data
What is Text Mining?
What is NLP?
Introduction to Stemming
Introduction to Lemmatization
Applications of Stemming & Lemmatization
Difference between stemming & Lemmatization
Follow us to never miss an update in the future.
Instagram: https://www.instagram.com/edureka_learning/
Facebook: https://www.facebook.com/edurekaIN/
Twitter: https://twitter.com/edurekain
LinkedIn: https://www.linkedin.com/company/edureka
Microblog-genre noise and its impact on semantic annotation accuracyLeon Derczynski
This document discusses challenges in applying natural language processing pipelines to microblog texts like tweets. Key challenges include non-standard language use, brevity, and lack of context. The document evaluates performance of typical NLP tasks on microblogs, like part-of-speech tagging and named entity recognition, and proposes approaches to address noise, such as customizing tools to the microblog genre and applying normalization techniques. It concludes that while performance is lower on microblogs, targeted approaches can provide gains and that leveraging additional context from metadata may further help analyze microblog language.
The document provides an overview of natural language processing (NLP) and its related areas. It discusses the classical view of NLP involving stages of processing like syntax, semantics, pragmatics, etc. It also discusses the statistical/machine learning view of NLP, where NLP tasks are framed as classification problems and cues from language help reduce uncertainty. Finally, it provides examples of lower-level NLP tasks like part-of-speech tagging that can be viewed as sequence labeling problems.
Natural language processing (NLP) is a subfield of artificial intelligence that aims to allow computers to understand human language. NLP involves analyzing and representing text or speech at different linguistic levels for applications like question answering or machine translation. Challenges for NLP include ambiguities in language like lexical, syntactic, semantic, and anaphoric ambiguities. Common NLP tasks include part-of-speech tagging, parsing, named entity recognition, and sentiment analysis. Applications of NLP include text processing, machine translation, speech processing, and converting text to speech.
Adam Bittlingmayer is a technical co-founder at Signal N (http://signaln.com), where he works on sentence-level quality estimation of machine translation.
More about our meetup:
http://www.meetup.com/de-DE/Berlin-Language-Technology/events/228344365/
Introduction to Natural Language ProcessingPranav Gupta
the presentation gives a gist about the major tasks and challenges involved in natural language processing. In the second part, it talks about one technique each for Part Of Speech Tagging and Automatic Text Summarization
This document outlines the key components of an effective data science project using a case study of a project completed for Procter & Gamble. The four key components are: 1) human-centered design involving stakeholders at all stages, 2) a hypothesis-driven approach to stay focused, 3) rapid iteration to test options without fixating on one, and 4) a modular approach to encourage collaboration. The document then walks through the specific steps of the Procter & Gamble project as an example of applying these components.
SearchLove London 2019 - Rory Truesdale - Using the SERPs to Know Your AudienceDistilled
It’s easy to get swept away by monthly search volume and to forget that behind every search there is a person with a specific motivation and set of needs to fulfil. This talk will look at how you can use Google’s algorithmic rewriting of the SERPs to help you identify those motivations so you can effectively optimise for intent and query context to improve the ranking performance of your landing pages. This talk will also help you understand how you can use this information to create more tailored online experiences for your prospective customers and how the same workflows can be applied for more general business intelligence insights.
Datascope: Designing your Data Viz - The (Iterative) ProcessMollie Pettit
This talk was given to a Data Visualization course, which is part of the Masters of Science in Analytics program at the Northwestern School of Engineering.
It walks through:
- Why to visualize data
- A common (linear) approach to data problems
- A look at a problem in an ambiguos world, and why the linear approach does not always get one to their ideal end point
- A better (iterative) approach
- how to get started on a project through the important practice of brainstorming
-An informal project example. In this example, an iterative approach to the visualization helped the creator to gain new insights which changed her story's focus all-together.
-A case study of a project done for Procter & Gamble. In this example, an iterative approach redirected us from a more complicated network graph of the company (which we initially assumed would be an end-result) to displaying data in a simpler way (e.g. bar charts), which was more ideal for the client.
-Another case study. In this example, an iterative approach led us to create a less obvious / more creative visualization that stressed the things that were most important to the client. Nearly every single iteration step (all of which were shown to the client) are shown in the slides.
It ends with a reminder that doing is better than planning. You really can't learn what your ideal end-product will be until you get started; while working, one must constantly ask questions and gain feedback, and refine the approach accordingly.
The document discusses different approaches to generating biographies through natural language processing, including information extraction and language modeling. It describes using information extraction patterns learned from Wikipedia to extract fields like date of birth and place of birth, and bouncing between Wikipedia and Google search results to learn patterns for other fields with less structured data. It also proposes selecting and ranking sentences from search results to improve recall when information extraction may miss relevant sentences. The goal is to build biographies by combining these techniques for high precision on structured fields and better recall on more complex fields.
One of the biggest challenges in the data age is overcoming the problematic belief that data has all the answers. The truth is – data is a resource, not a solution. In order to extract valuable and actionable insights, it is necessary to ask and re-ask certain questions. This talk is about figuring out what these questions are and exposes some of the limitations of common, and seemingly intuitive, approaches to data problems. As an alternative, I introduce the concept of using human-centered design principles and an iterative process to approach what you do with Big (and small) Data. As exemplars, I will walk-through a quick informal example and a real Datascope client project to highlight the flexibility and speed of these techniques.
The document discusses changes in the English language and technologies used for teaching English. It notes how computers and corpora have changed dictionary making by providing large datasets of real language. New words like "blog" and "podcast" have emerged for new technologies. The meaning of words like "web" and "site" have shifted based on evidence from corpora. Classroom technologies have advanced from blackboards to include computers, internet access, and interactive whiteboards. Overall, the core English vocabulary remains stable while teaching tools and student expectations continue to evolve.
Natural Language Processing: L01 introductionananth
This presentation introduces the course Natural Language Processing (NLP) by enumerating a number of applications, course positioning, challenges presented by Natural Language text and emerging approaches to topics like word representation.
Metaphic or the art of looking another way.Suresh Manian
For all intents and purposes, we are our words. And verbs and adjectives capture actions and sentiments better than any other tool. Metaphic is premised on the belief that a grammar book and a calculator are all you really need to make sense of web search and social media chatter, apart from all text, in general.
Using Corpus Linguistics to Teach ESL PronunicationRebecca Allen
This study analyzed lexical bundles (4-word phrases) in the Michigan Corpus of Academic Spoken English to understand pronunciation patterns actually used on a university campus. The most common bundles were participant-oriented and included contractions like "I don't know", showing students frequently qualify their speech. Analyzing bundle syntax, semantics and phonology has implications for teaching connected speech and how contractions convey meaning and attitudes for English language learners. While limited by decontextualized data, this corpus analysis provides a starting point for further applied classroom research on pronunciation and how non-native speakers communicate meaning.
This document summarizes a presentation on terminology trends from a blogger's perspective. It discusses how language lovers use social networks like blogs, Facebook, and Twitter to communicate about terminology by researching, asking questions, answering questions of followers, reporting on conferences, and providing helpful tips, news, and job opportunities. Social networks produce large amounts of text data that can be used for terminology research to analyze evolving language and identify neologisms. Tools like the Global Language Monitor use natural language processing of social media to track new terms and their usage in real-time.
This document provides an introduction to natural language processing (NLP) through a presentation given by Rutu Mulkar-Mehta. The presentation covers understanding natural language, common NLP tasks like text categorization and sentiment analysis, and challenges like ambiguity. It also discusses part-of-speech tagging and linguistic resources. The overall goal is to introduce attendees to the field of NLP and some of its applications.
The document discusses various types of plagiarism including copy-paste plagiarism, word switch plagiarism, style plagiarism, idea plagiarism, metaphor plagiarism, and summarizes the correct methods for citing sources to avoid plagiarism such as using quotations and citing authors. It also provides examples of each type of plagiarism and the appropriate way to reference sources using in-text citations and bibliographies.
How is mobile technology and search engine tuning evolving to meet the needs of users? Here we look at recent developments in research, implementations by search engines, and how to look at reach users can adapt their strategies to take into account these next-level changes.
Stemming And Lemmatization Tutorial | Natural Language Processing (NLP) With ...Edureka!
( **Natural Language Processing Using Python: - https://www.edureka.co/python-natural... ** )
This PPT will provide you with detailed and comprehensive knowledge of the two important aspects of Natural Language Processing ie. Stemming and Lemmatization. It will also provide you with the differences between the two with Demo on each. Following are the topics covered in this PPT:
Introduction to Big Data
What is Text Mining?
What is NLP?
Introduction to Stemming
Introduction to Lemmatization
Applications of Stemming & Lemmatization
Difference between stemming & Lemmatization
Follow us to never miss an update in the future.
Instagram: https://www.instagram.com/edureka_learning/
Facebook: https://www.facebook.com/edurekaIN/
Twitter: https://twitter.com/edurekain
LinkedIn: https://www.linkedin.com/company/edureka
Microblog-genre noise and its impact on semantic annotation accuracyLeon Derczynski
This document discusses challenges in applying natural language processing pipelines to microblog texts like tweets. Key challenges include non-standard language use, brevity, and lack of context. The document evaluates performance of typical NLP tasks on microblogs, like part-of-speech tagging and named entity recognition, and proposes approaches to address noise, such as customizing tools to the microblog genre and applying normalization techniques. It concludes that while performance is lower on microblogs, targeted approaches can provide gains and that leveraging additional context from metadata may further help analyze microblog language.
The document provides an overview of natural language processing (NLP) and its related areas. It discusses the classical view of NLP involving stages of processing like syntax, semantics, pragmatics, etc. It also discusses the statistical/machine learning view of NLP, where NLP tasks are framed as classification problems and cues from language help reduce uncertainty. Finally, it provides examples of lower-level NLP tasks like part-of-speech tagging that can be viewed as sequence labeling problems.
Natural language processing (NLP) is a subfield of artificial intelligence that aims to allow computers to understand human language. NLP involves analyzing and representing text or speech at different linguistic levels for applications like question answering or machine translation. Challenges for NLP include ambiguities in language like lexical, syntactic, semantic, and anaphoric ambiguities. Common NLP tasks include part-of-speech tagging, parsing, named entity recognition, and sentiment analysis. Applications of NLP include text processing, machine translation, speech processing, and converting text to speech.
Adam Bittlingmayer is a technical co-founder at Signal N (http://signaln.com), where he works on sentence-level quality estimation of machine translation.
More about our meetup:
http://www.meetup.com/de-DE/Berlin-Language-Technology/events/228344365/
Introduction to Natural Language ProcessingPranav Gupta
the presentation gives a gist about the major tasks and challenges involved in natural language processing. In the second part, it talks about one technique each for Part Of Speech Tagging and Automatic Text Summarization
This document outlines the key components of an effective data science project using a case study of a project completed for Procter & Gamble. The four key components are: 1) human-centered design involving stakeholders at all stages, 2) a hypothesis-driven approach to stay focused, 3) rapid iteration to test options without fixating on one, and 4) a modular approach to encourage collaboration. The document then walks through the specific steps of the Procter & Gamble project as an example of applying these components.
SearchLove London 2019 - Rory Truesdale - Using the SERPs to Know Your AudienceDistilled
It’s easy to get swept away by monthly search volume and to forget that behind every search there is a person with a specific motivation and set of needs to fulfil. This talk will look at how you can use Google’s algorithmic rewriting of the SERPs to help you identify those motivations so you can effectively optimise for intent and query context to improve the ranking performance of your landing pages. This talk will also help you understand how you can use this information to create more tailored online experiences for your prospective customers and how the same workflows can be applied for more general business intelligence insights.
Datascope: Designing your Data Viz - The (Iterative) ProcessMollie Pettit
This talk was given to a Data Visualization course, which is part of the Masters of Science in Analytics program at the Northwestern School of Engineering.
It walks through:
- Why to visualize data
- A common (linear) approach to data problems
- A look at a problem in an ambiguos world, and why the linear approach does not always get one to their ideal end point
- A better (iterative) approach
- how to get started on a project through the important practice of brainstorming
-An informal project example. In this example, an iterative approach to the visualization helped the creator to gain new insights which changed her story's focus all-together.
-A case study of a project done for Procter & Gamble. In this example, an iterative approach redirected us from a more complicated network graph of the company (which we initially assumed would be an end-result) to displaying data in a simpler way (e.g. bar charts), which was more ideal for the client.
-Another case study. In this example, an iterative approach led us to create a less obvious / more creative visualization that stressed the things that were most important to the client. Nearly every single iteration step (all of which were shown to the client) are shown in the slides.
It ends with a reminder that doing is better than planning. You really can't learn what your ideal end-product will be until you get started; while working, one must constantly ask questions and gain feedback, and refine the approach accordingly.
The document discusses different approaches to generating biographies through natural language processing, including information extraction and language modeling. It describes using information extraction patterns learned from Wikipedia to extract fields like date of birth and place of birth, and bouncing between Wikipedia and Google search results to learn patterns for other fields with less structured data. It also proposes selecting and ranking sentences from search results to improve recall when information extraction may miss relevant sentences. The goal is to build biographies by combining these techniques for high precision on structured fields and better recall on more complex fields.
One of the biggest challenges in the data age is overcoming the problematic belief that data has all the answers. The truth is – data is a resource, not a solution. In order to extract valuable and actionable insights, it is necessary to ask and re-ask certain questions. This talk is about figuring out what these questions are and exposes some of the limitations of common, and seemingly intuitive, approaches to data problems. As an alternative, I introduce the concept of using human-centered design principles and an iterative process to approach what you do with Big (and small) Data. As exemplars, I will walk-through a quick informal example and a real Datascope client project to highlight the flexibility and speed of these techniques.
The document discusses changes in the English language and technologies used for teaching English. It notes how computers and corpora have changed dictionary making by providing large datasets of real language. New words like "blog" and "podcast" have emerged for new technologies. The meaning of words like "web" and "site" have shifted based on evidence from corpora. Classroom technologies have advanced from blackboards to include computers, internet access, and interactive whiteboards. Overall, the core English vocabulary remains stable while teaching tools and student expectations continue to evolve.
Natural Language Processing: L01 introductionananth
This presentation introduces the course Natural Language Processing (NLP) by enumerating a number of applications, course positioning, challenges presented by Natural Language text and emerging approaches to topics like word representation.
Metaphic or the art of looking another way.Suresh Manian
For all intents and purposes, we are our words. And verbs and adjectives capture actions and sentiments better than any other tool. Metaphic is premised on the belief that a grammar book and a calculator are all you really need to make sense of web search and social media chatter, apart from all text, in general.
Using Corpus Linguistics to Teach ESL PronunicationRebecca Allen
This study analyzed lexical bundles (4-word phrases) in the Michigan Corpus of Academic Spoken English to understand pronunciation patterns actually used on a university campus. The most common bundles were participant-oriented and included contractions like "I don't know", showing students frequently qualify their speech. Analyzing bundle syntax, semantics and phonology has implications for teaching connected speech and how contractions convey meaning and attitudes for English language learners. While limited by decontextualized data, this corpus analysis provides a starting point for further applied classroom research on pronunciation and how non-native speakers communicate meaning.
This document summarizes a presentation on terminology trends from a blogger's perspective. It discusses how language lovers use social networks like blogs, Facebook, and Twitter to communicate about terminology by researching, asking questions, answering questions of followers, reporting on conferences, and providing helpful tips, news, and job opportunities. Social networks produce large amounts of text data that can be used for terminology research to analyze evolving language and identify neologisms. Tools like the Global Language Monitor use natural language processing of social media to track new terms and their usage in real-time.
This document provides an overview of the field of linguistics and why it is worth studying. It does this through a series of questions about language that are answered using different subfields of linguistics, such as semantics, syntax, phonetics, and language acquisition. The document demonstrates how linguistics can provide insights into language variation over time, irregular spelling patterns, sentence structure, meaning, grammar, and the human ability to acquire language from a young age. It also discusses career opportunities related to studying linguistics and examples of linguistics course content and structures.
Marriage of speech, vision and natural language processingYaman Kumar
Speech generally is considered to have three parts to it: vision, aural, and the social construct. In recent years, although the field has been moving at a dramatic pace, progress is being made in silos. The primary reason for this being that speech is considered "spoken text" by practitioners and researchers alike. Most open-source datasets due to their distance from real-world conditions help in spreading this false impression. In this condition, it is not surprising that common and important features of speech like intonation and disfluency do not get captured by this intent. This tutorial aims to provide an appreciation of the "full-stack" of speech - aural, vision and the textual (or social construct) parts with a special emphasis on aspects that may have significance for current and future research.
Linguistics is the scientific study of language. It involves studying many aspects of language including its history, sound system, structure, meaning and how it is acquired. A linguistics course would cover topics such as morphology, syntax, phonetics, phonology, semantics, pragmatics, sociolinguistics, psycholinguistics and historical linguistics. Studying linguistics provides valuable skills in logical thinking, problem solving, communication and understanding human behavior that are useful for a wide range of careers. Students find linguistics courses fascinating as they learn about language development and the workings of their own and other languages.
Engineering Intelligent NLP Applications Using Deep Learning – Part 1Saurabh Kaushik
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 describes an audio transcription and text-to-speech system that aims to help people with disabilities or difficulties typing. It outlines the problem of some users not being able to type quickly or see screens clearly. The proposed system would use speech recognition and text-to-speech to allow users to input and output text orally rather than through typing or reading. It would draw on large databases and accurate speech recognition to minimize errors. The system would benefit many users by making digital content more accessible. It provides background on similar existing technologies and discusses how the project would be developed using Python libraries for speech recognition, translation, and text-to-speech.
Learn the steps to making your scientific, technical information easy to read and mobile search-friendly. Identify your audience and write web content that is easy to understand.
BrightonSEO 2019 - Mining the SERP for SEO, Content & Customer InsightsRory Truesdale
Find out how you can use Python to analyse the language of the SERPs for valuable insights on what your customers want and how this can be applied to improve the performance of your SEO campaign.
This document provides an overview and agenda for an online webinar on literacy instruction. The webinar will include introductions, an overview of course materials and assignments, and a discussion of balanced literacy and key concepts like phonemic awareness, phonics, fluency, vocabulary and comprehension. It will also cover topics like guided reading lessons, the role of writing, and integrating digital literacies. Participants are encouraged to discuss these topics in the webinar chat and ask questions. The goals are for students to gain confidence with online tools and learn about best practices in teaching early literacy.
The document discusses different theories of linguistics that attempt to explain grammar, including transformational grammar, minimalism, and construction grammar. Transformational grammar proposes that words move between deep and surface structures to explain grammaticality, but critics argue that word movements are not plausible and that meaning should not be ignored. Minimalism aims to reduce unnecessary movements, but if all movement is unnecessary, then generative grammar has no means of explanation. Construction grammar and other theories argue that meaning is an inherent part of words and that language is embodied and connected to other cognitive and social factors beyond a specialized language faculty.
This document discusses translation in language learning from key stage 2 through 5. It addresses translating into both the first language (L1) and second language (L2), exploring issues of literacy, vocabulary and grammar development, and cultural understanding. A range of tools and approaches for translation are presented, including the use of online resources, literature, news articles, social media posts and songs. Benefits and challenges of different translation activities are considered.
Surname 5Chang QiuLinguistic1112018Machine and Human.docxmabelf3
Surname 5
Chang Qiu
Linguistic
11/1/2018
Machine and Human Translate
Most of the contents in the article involve a lot of criticism of the activities and services of the google translate compared to the normal conversation done in a face to face communication. Douglas Hofstadter provides a definitive introduction into the modern uses of technology and its consequences and applications. It is a challenge for thinking technology will solve even the simplest of the forms of human existence, such as language and communication. Hofstadter considers Google translations does not have the capabilities of meeting the balance between human communication and the use of language in different contexts.
In the contemporary community, the field of language translation engines is experiencing a gradual improvement with the recent introduction of the deep neural nets. It has been suggested by some observers and professionals that the era of the “Great AI Awakening” is upon us. If such a development was to occur, it would cause a very devastating upheaval in the Douglas Hofstadter life and beliefs. Although he might be fascinated and interested in the concept of machines translators and the ability of machines to translate, he is still not interested in seeing the human translators being replaced by the inanimate machines.
The idea and belief that this might come, frightens him. To his belief, the concept of human translators should be given more credit and awareness mainly because he considers it to be a form of art which is derived from individuals with many years of language experience throughout their lifetime. If it were to occur that the human translators are to be replaced by the machine translators and become considered as past relics, Douglas Hofstadter would be very saddened and be left in a state of terrible confusion.
There are different issues highlighted by Hofstadter and these issues are at the inability to effectively translate human communication by a machine’s translation configurations. For example, one phenomenon provided in the translation of the statement "Dans Leur Maison, tout vient en paires. Il y a sa Voiture et sa voiture, ses serviettes et ses serviettes, sa bibliothèque et Les siennes” by the google translate. The translation fails due to the different acts and why it affects the consistency and credibility brought by the Google translation.
The translation of any language with the use of a machine translator also contains its own sets of misgivings and challenges. For example, in the case of the French translation, it has been noted that the conversion for French is one of the youngest enterprises as compared to the rest of the other translated language. However impressive the current progress of the endeavor has produced over the past couple of years, the language still continues to face several challenges and issues during their translation process. A recent examination was conducted, trying to identify the problems and c.
The presentation explains topics on study of language, applications on natural language processing, levels of language analysis, representation and understanding, linguistic background and elements of a simple noun phrase
Paco Guzmán is a Research Scientist Manager at Facebook AI where he leads a team working on People-Facing Translations. Before joining Facebook in 2016, Paco was a Research Scientist at Qatar Computing Research Institute from 2012-2016. He obtained his PhD in 2011 from ITESM in Mexico. The document then discusses some of the challenges of machine translation, including translating languages with fewer resources, domain evaluation, and language similarity. It also discusses approaches to address these challenges like multilingual training, backtranslation, building translation datasets for low-resource languages, and leveraging web-scale mining of parallel texts.
Ähnlich wie Google BERT - What SEOs and Marketers Need to Know (20)
Human quality raters have been the mainstay of search engine evaluation for decades but a sea-change is on its way due to the need for scale as machine learning and demand evolves.
Life of An SEO - Surfing The Waves of Googles Many Algorithmic UpdatesDawn Anderson MSc DigM
the life of an SEO is never boring. Search is always changing and subsequently Google's algorithms are updated to reflect changing search behaviours and to combat the actions of bad actors / spam in the search engine results pages. We look at past algorithms, the many types of algorithms and identify how you can ascertain whether you've been impacted by an algorithmic update and how to remedy / recover
Natural Semantic SEO - Surfacing Walnuts in Densely Represented, Every Increa...Dawn Anderson MSc DigM
Structured data accounts for only a small part of the web and the problem grows as the volume of the online content grows. Schema markup is a drop in the ocean to help with this. However, things are being addressed in the natural language research space in the form of dense retrieval and other developments such as Sentence BERT and FAISS. Utilising heuristics such as umbrellas and sidecar pages will help to send clues and assist with ensuring search engines rank the right pages from your sites for SEO
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.
Zipfs Law & Zipfian Distribution in SEO - Pubcon Virtual Fall 2020 - Dawn And...Dawn Anderson MSc DigM
Zipf's Law is prevalent throughout many forms of data and that includes the internet at large and within sectors of the internet, websites and web pages plus linguistics. How does this impact SEO if at all?
Disambiguating Equiprobability in SEO Dawn Anderson Friends of Search 2020Dawn Anderson MSc DigM
Connecting the probable dots in content and data can help significantly and improve your search strategy. Ambiguity in SEO comes in many form too, going beyond content and into entities and locations. This talk touches on some of the areas where ambiguity can impact and hinder your performance
Connecting The Worlds of Information Retrieval & SEO - Search solutions 2019 ...Dawn Anderson MSc DigM
This document discusses the connections between the fields of information retrieval (IR) and search engine optimization (SEO). It notes that while the two fields are interested in similar topics around search and user behavior, they rarely interact at conferences or within their communities. However, attending IR conferences has provided the author with many learnings that have helped improve their SEO work. Both fields also face ongoing challenges, such as dealing with non-structured data and modern web frameworks, and continued collaboration between IR and SEO could help address problems in web search.
It's very easy to get started too quickly in SEO for a new website and not plan properly using a framework to improve probability of success. Here we look at the SOSTAC framework for a new site and explore some traditional strategic models and marketing frameworks to employ in an SEO capacity for a dog friendly website in the UK territory. Expect SOSTAC, Porters 5 Forces, Ansoff Matrix, 5S's, 8P's and more
In an information economy where users are time poor and research hungry we need to take a mobile first approach to meet the needs of both users and search engines looking to align users informational needs with relevant search results. With limited space and a now mobile-first index how can we align our SEO strategy with this?
The changing search landscape calls for different approaches to user needs, including context, intent and device considerations. Here we take a look at ways to keep working to keep your ecommerce site well positioned for strong transactional and informational queries in the moments that matter to shoppers online.
Voice Search and Conversation Action Assistive Systems - Challenges & Opportu...Dawn Anderson MSc DigM
We are headed to the age of assistive task driven search where the user needs help to 'do' things as well as learn things. Smart speakers, mobile phones, assistive systems and conversational search and action devices are where the buck is headed for now. Where are we at in this wave? What are the challenges? What are the opportunities right now? Here we look at some of the ways we can start to prepare our tactics and strategy to be pioneering search marketers with conversation search and conversation action.
The Iceberg Approach - Power from what lies beneath in SEO for a mobile-first...Dawn Anderson MSc DigM
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Mobile-first goes beyond simply indexing in a search engine. It has several meanings, which traverse user-behaviour, web design, adoption in different territories, adoption amongst user segments, adoption in different verticals. We need to be aware of these fundamentals changes in search behaviour and adapt quickly.
Here we take a look at server log file analysis for SEO and explore not only the benefits but also the process of finding, gathering, shipping and analysing user agent logs
Voice Search Challenges For Search and Information Retrieval and SEODawn Anderson MSc DigM
Whilst search engines are making great strides to achieve gold standards in error free voice search recognition there are still a number of challenges. We look at some of them here and seek to understand how we may adapt to optimise for them. Thanks to Enrique Alfonseca, the Google Conversational Research Team, ESSIR Barcelona for the great learnings and education.
A few of the recent findings we discovered whilst working on an SEO beast which cover crawling, server log file analysis, site speed optimization and database optimizations. Technical SEO insights
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Things can add up over time when you migrate sites or have many legacy domains, subdomains and old code in a website. Signs of poor quality add up as incremental crawling never stops. This is akin to SEO technical debt which you need to repay to regain good site health and positive quality signals. You can't repay the debt all at once, but in iterative incremental steps over time.
The document discusses different types of duplicate content that can exist on websites, including perfect duplicates, near duplicates, partial duplicates, and content inclusion. It explains that search engines like Google have developed techniques to detect and handle different types of duplicate content differently. For example, perfect duplicates are filtered out before being indexed, while near duplicates or those with different URLs but similar text (DUST) may be indexed but not crawled as frequently to save resources. The document also discusses challenges around detecting different types of duplicate content and how search engines aim to return the most relevant result from a cluster of near-duplicate pages for a given query.
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Technical SEO - Generational cruft in SEO - there is never a new site when th...Dawn Anderson MSc DigM
This document discusses how search engines like Google maintain historical records of URLs they have crawled, including metrics like crawl frequency and importance. This historical data is used to predict how often URLs should be recrawled and prioritize them in the crawling queue. Even URLs that return 404 or 410 responses may still be recrawled periodically since search engines never fully remove pages from their indexes. Managing URL history and prioritizing crawls becomes challenging at large scale due to the massive number of URLs maintained in search engine databases over time.
Capstone Project: Luxury Handloom Saree Brand
As part of my college project, I applied my learning in brand strategy to create a comprehensive project for a luxury handloom saree brand. Key aspects of this project included:
- *Competitor Analysis:* Conducted in-depth competitor analysis to identify market position and differentiation opportunities.
- *Target Audience:* Defined and segmented the target audience to tailor brand messages effectively.
- *Brand Strategy:* Developed a detailed brand strategy to enhance market presence and appeal.
- *Brand Perception:* Analyzed and shaped the brand perception to align with luxury and heritage values.
- *Brand Ladder:* Created a brand ladder to outline the brand's core values, benefits, and attributes.
- *Brand Architecture:* Established a cohesive brand architecture to ensure consistency across all brand touchpoints.
This project helped me gain practical experience in brand strategy, from research and analysis to strategic planning and implementation.
AI Best Practices for Marketing HUG June 2024Amanda Farrell
During this presentation, the Nextiny marketing team reviews best practices when adopting generative AI into content creation. Join our HUG community to register for more events https://events.hubspot.com/sarasota/
Unlock the secrets to creating a standout trade show booth with our comprehensive guide from Blue Atlas Marketing! This presentation is packed with essential tips and innovative strategies to ensure your booth attracts attention, engages visitors, and drives business success. Whether you're a seasoned exhibitor or a first-timer, these expert insights will help you maximize your impact and make a memorable impression in a crowded exhibition hall. Learn how to:
Design an eye-catching and inviting booth
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Transform your trade show presence with these proven tactics and ensure your booth stands out from the competition. Download the PDF now and start planning your next successful exhibit!
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Digital Marketing is a latest method of Marketing techniques widely used across the Globe. Digital Marketing is an online marketing technique and methods used for all products and services through Search Engine and Social media advertisements. Previously the marketing techniques were used without using the internet via direct and indirect marketing strategies such as advertising through Telemarketing,Newspapers,Televisions,Posters etc.
List of Services offered in Digital Marketing |Techvolt Software :
Techvolt Software offers best Digital Marketing services for promoting your products and services through online platform on the below methods of Digital marketing
1. Search Engine Optimization (SEO)
2. Search Engine Marketing (SEM)
3. Social Media Optimization (SMO)
4. Social Media Marketing (SMM)
5. Campaigns
Importance | Need of Digital Marketing (Online Promotions) :
1. Quick Promotions through Online
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3. Latest Technology development vs Business promotions
4. Creation of Social Branding
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Benefits Digital Marketing Services at Techvolt software :
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With Regards
Gokila digital marketer
Coimbatore
Did you know that while 50% of content on the internet is in English, English only makes up 26% of the world’s spoken language? And yet 87% of customers won’t buy from an English only website.
Uncover the immense potential of communicating with customers in their own language and learn how translation holds the key to unlocking global growth. Join Smartling CEO, Bryan Murphy, as he reveals how translation software can streamline the translation process and seamlessly integrate into your martech stack for optimal efficiency. And that's not all – he’ll also share some inspiring success stories and practical tips that will turbocharge your multilingual marketing efforts!
Key takeaways:
1. The growth potential of reaching customers in their native language
2. Tips to streamline translation with software and integrations to your tech stack
3. Success stories from companies that have increased lead generation, doubled revenue, and more with translation
Efficient Website Management for Digital Marketing ProsLauren Polinsky
Learn how to optimize website projects, leverage SEO tactics effectively, and implement product-led marketing approaches for enhanced digital presence and ROI.
This session is your key to unlocking the secrets of successful digital marketing campaigns and maximizing your business's online potential.
Actionable tactics you can apply after this session:
- Streamlined Website Management: Discover techniques to streamline website development, manage day-to-day operations efficiently, and ensure smooth project execution.
- Effective SEO Practices: Gain valuable insights into optimizing your website for search engines, improving visibility, and driving organic traffic to your digital assets.
- Leverage Product-Led Marketing: Explore strategies for incorporating product-led marketing principles into your digital marketing efforts, enhancing user engagement and driving conversions.
Don't miss out on this opportunity to elevate your digital marketing game and achieve tangible results!
Mastering Local SEO for Service Businesses in the AI Era"" is tailored specifically for local service providers like plumbers, dentists, and others seeking to dominate their local search landscape. This session delves into leveraging AI advancements to enhance your online visibility and search rankings through the Content Factory model, designed for creating high-impact, SEO-driven content. Discover the Dollar-a-Day advertising strategy, a cost-effective approach to boost your local SEO efforts and attract more customers with minimal investment. Gain practical insights on optimizing your online presence to meet the specific needs of local service seekers, ensuring your business not only appears but stands out in local searches. This concise, action-oriented workshop is your roadmap to navigating the complexities of digital marketing in the AI age, driving more leads, conversions, and ultimately, success for your local service business.
Key Takeaways:
Embrace AI for Local SEO: Learn to harness the power of AI technologies to optimize your website and content for local search. Understand the pivotal role AI plays in analyzing search trends and consumer behavior, enabling you to tailor your SEO strategies to meet the specific demands of your target local audience. Leverage the Content Factory Model: Discover the step-by-step process of creating SEO-optimized content at scale. This approach ensures a steady stream of high-quality content that engages local customers and boosts your search rankings. Get an action guide on implementing this model, complete with templates and scheduling strategies to maintain a consistent online presence. Maximize ROI with Dollar-a-Day Advertising: Dive into the cost-effective Dollar-a-Day advertising strategy that amplifies your visibility in local searches without breaking the bank. Learn how to strategically allocate your budget across platforms to target potential local customers effectively. The session includes an action guide on setting up, monitoring, and optimizing your ad campaigns to ensure maximum impact with minimal investment.
Dive deep into the cutting-edge strategies we're employing to revolutionize our web presence in the age of AI-driven search. As Gen Z reshapes the digital realm, discover how we can bridge the generational divide. Unlock the synergistic power of PPC, social media, and SEO, driving unparalleled revenues for our projects.
From Subreddits To Search: Maximizing Your Brand's Impact On RedditSearch Engine Journal
The search landscape is undergoing a seismic shift, and Reddit is at the epicenter. Google's Helpful Content Update and its $60 million deal with Reddit, coupled with OpenAI's partnership, have catapulted Reddit's real-time content to unprecedented heights.
Check out this insightful webinar exploring the newfound importance of Reddit in the digital marketing landscape. Learn how these changes make Reddit an essential platform for getting your brand and content in front of evolving search audiences.
You’ll hear:
- The evolution of Reddit as a major influencer on SERPS over the years.
- The impact of recent changes and partnerships on Reddit’s place in search.
- A comprehensive look at Reddit, how it works, and how to approach it.
- Unique engagement opportunities presented by Reddit.
With Brent Csutoras, a Reddit expert with over 18 years of experience on the platform, we’ll delve into the intricacies of Reddit's communities, known as Subreddits, and how to leverage their power without compromising authenticity or violating community guidelines in the age of AI-driven search experiences.
Don't miss this opportunity to stay ahead of the curve and leverage Reddit for your brand's success.
What Software is Used in Marketing in 2024.Ishaaq6
This paper explores the diverse landscape of marketing software, examining its pivotal role in modern marketing strategies. It provides a comprehensive overview of various types of marketing software tools and platforms essential for enhancing efficiency, optimizing campaigns, and achieving business objectives. Key categories discussed include email marketing software, social media management tools, content management systems (CMS), customer relationship management (CRM) software, search engine optimization (SEO) tools, and marketing automation platforms.
The paper delves into the functionalities, benefits, and examples of each type of software, highlighting their unique contributions to effective marketing practices. It explores the importance of integration and automation in maximizing the impact of these tools, addressing challenges and strategies for seamless implementation across different marketing channels.
Furthermore, the paper examines emerging trends in marketing software, such as AI and machine learning applications, personalization strategies, predictive analytics, and the ethical considerations surrounding data privacy and consumer rights. Case studies illustrate real-world applications and success stories of businesses leveraging marketing software to achieve significant outcomes in their marketing campaigns.
In conclusion, this paper provides valuable insights into the evolving landscape of marketing technology, emphasizing the transformative potential of software solutions in driving innovation, efficiency, and competitive advantage in today's dynamic marketplace.
This description outlines the scope, structure, and focus of the paper, giving readers a clear understanding of what to expect and why the topic of marketing software is important and relevant in contemporary marketing practices.
From Hope to Despair The Top 10 Reasons Businesses Ditch SEO Tactics.pptxBoston SEO Services
From Hope to Despair: The Top 10 Reasons Businesses Ditch SEO Tactics
Are you tired of seeing your business's online visibility plummet from hope to despair? When it comes to SEO tactics, many businesses find themselves grappling with challenges that lead them to abandon their strategies altogether. In a digital landscape that's constantly evolving, staying on top of SEO best practices is crucial to maintaining a competitive edge.
In this blog, we delve deep into the top 10 reasons why businesses ditch SEO tactics, uncovering the pain points that may resonate with you:
1. Algorithm Changes: The ever-changing algorithms can leave businesses feeling like they're chasing a moving target. Search engines like Google frequently update their algorithms to improve user experience and provide more relevant search results. However, these updates can significantly impact your website's visibility and ranking if you're not prepared.
2. Lack of Results: Investing time and resources without seeing tangible results can be disheartening. The absence of immediate results often leads businesses to lose faith in their SEO strategies. It's important to remember that SEO is a long-term game that requires patience and consistent effort.
3. Technical Challenges: From site speed issues to complex metadata implementation, technical hurdles can be daunting. Overcoming these challenges is crucial for SEO success, as technical issues can hinder your website's performance and user experience.
4. Keyword Competition: Fierce competition for top keywords can make it hard to rank effectively. Businesses often struggle to find the right balance between targeting high-traffic keywords and finding less competitive, niche keywords that can still drive significant traffic.
5. Lack of Understanding of SEO Basics: Many businesses dive into the complex world of SEO without fully grasping the fundamental principles. This lack of understanding can lead to several issues:
Keyword Awareness: Failing to recognize the importance of keyword research and targeting the right keywords in content.
On-Page Optimization: Ignorance regarding crucial on-page elements such as meta tags, headers, and content structure.
Technical SEO Best Practices: Overlooking essential aspects like site speed, mobile responsiveness, and crawlability.
Backlinks: Not understanding the value of high-quality backlinks from reputable sources.
Analytics: Failing to track and analyze data prevents businesses from optimizing their SEO efforts effectively.
6. Unrealistic Expectations and Timeframe: Entrepreneurs often fall prey to the allure of quick fixes and overnight success. Unrealistic expectations can overshadow the reality of the time and effort needed to see tangible results in the highly competitive digital landscape. SEO is a long-term strategy, and setting realistic goals is crucial for success.
#SEO #DigitalMarketing #BusinessGrowth #OnlineVisibility #SEOChallenges #BostonSEO
We’ve entered a new era in digital. Search and AI are colliding, in more ways than one. And they all have major implications for marketers.
• SEOs now use AI to optimize content.
• Google now uses AI to generate answers.
• Users are skipping search completely. They can now use AI to get answers. So AI has changed everything …or maybe not. Our audience hasn’t changed. Their information needs haven’t changed. Their perception of quality hasn’t changed. In reality, the most important things haven’t changed at all. In this session, you’ll learn the impact of AI. And you’ll learn ways that AI can make us better at the classic challenges: getting discovered, connecting through content and staying top of mind with the people who matter most. We’ll use timely tools to rebuild timeless foundations. We’ll do better basics, but with the most advanced techniques. Andy will share a set of frameworks, prompts and techniques for better digital basics, using the latest tools of today. And in the end, Andy will consider - in a brief glimpse - what might be the biggest change of all, and how to expand your footprint in the new digital landscape.
Key Takeaways:
How to use AI to optimize your content
How to find topics that algorithms love
How to get AI to mention your content and your brand
Embark on style journeys Indian clothing store denver guide.pptxOmnama Fashions
Finding the perfect "Indian Clothing Store Denver" is essential for those seeking vibrant, authentic, and culturally rich attire in the heart of Colorado. Denver, a city known for its diverse culture and eclectic fashion scene, offers a variety of options for those in search of traditional and contemporary Indian clothing. Whether you're preparing for a wedding, festival, or cultural event, or simply wish to incorporate the elegance and beauty of Indian fashion into your wardrobe, discovering the right store can make all the difference.
3. @dawnieando
• A Google algorithmic
update
• Google announce BERT to the
organic search world in a VERY
geeky way
• Mentions of the 15% of new
queries every day
• Touches on ‘The Vocabulary
Problem’ (many ways of querying
the same thing)
October 2019 - Welcome To Search, BERT
4. @dawnieando
• Probably the biggest
improvement in search EVER
• The biggest change in search
in five years, since RankBrain
Fundamentally… Google BERT is
5. @dawnieando
!Layman’s Terms: it can be
used to help Google better
understand the context of
words in search queries &
content
So, just what is Google BERT update?
6. @dawnieando
• Used globally in all
languages on featured
snippets
• BERT to impact rankings for 1
in 10 queries
• Initially for English language
queries in US
The bottom line search announcement
7. @dawnieando
Dec 2019 – BERT expands internationally
• Over 70 languages
• Still only impacts 10% of
queries despite the
considerable expansion
• Still all featured snippets
globally
8. @dawnieando
• BERT deals with
ambiguity & ‘nuance’ in
queries & content
• Unlikely to impact short
queries
• More likely to impact
conversational queries
• Unlikely to impact
branded queries
Why just 10% of Google Queries Impacted?
9. @dawnieando
• The SEO community is
abuzz
• BERT is a big deal
• Likened to ‘Rank Brain’ in
some of the ‘interesting’
interpretations
• Some confusions around
‘What BERT is and what it
means for search’
SEO’s React
10. @dawnieando
!A neural network-based
technique for natural language
processing pre-training
!An anagram of Bi-Directional
Encoder Representations from
Transformers
BERT in Geek Speak
12. @dawnieando
• Search algorithm update
• Open source pre-trained model / framework for
natural language understanding
• Academic research paper
• Evolving tool for computational linguistics efficiency
• Beginning of MANY BERT’ish language models
Important: BERT is Many Things
13. @dawnieando
So What’s The Backstory?
Where%did%BERT%come%from?
Where%did%the%need%for%BERT%arise?
The$Impact$of$BERT$for$SEO$&$beyond?
What%next?
14. @dawnieando
• Academic Paper
• Research Project by Devlin et al
• Published a year before the
update in October 2018
• Bert: Pre-training of deep
bidirectional transformers for
language understanding
BERT started as a research paper in 2018
15. @dawnieando
• Open sourced so anyone can
build a BERT
• BERT created a sea-change
leap-forward in natural language
understanding in information
retrieval very quickly
• Provided a pre-trained language
model which required only fine-
tuning
BERT Open Sourced in 2018
16. @dawnieando
The whole of the English
Wikipedia & The Books
Corpus combined.
Over 2,500 million words
BERT Has Been Pre-Trained On Many Words
17. @dawnieando
Vanilla BERT provides a pre-
trained starting point layer for
neural networks in machine
learning & natural language
diverse tasks
The machine learning community got very
excited about BERT
18. @dawnieando
• BERT is fine-tuned on a variety of
downstream NLP tasks, including
question and answer datasets
BERT Can Be Fine-Tuned in A Short Space of Time
19. @dawnieando
• Vanilla BERT can be used ‘out of the box’
or fine-tuned
• Provides a great starting point & saves
huge amounts of time & money
• Those wishing to, ‘can build upon’, and
improve BERT
BERT Saves Researchers Time AND Money
20. @dawnieando
• Microsoft – MT-DNN
• Facebook – RoBERTa
• XLNet
• ERNIE – Baidu
• Lots of other
contenders
Since 2018 Major tech companies extend BERT
25. @dawnieando
Language models like
BERT help machines
understand the nuance
in word’s context and
surrounding text
cohesion
What Purpose Does BERT Serve & How?
26. @dawnieando
• Dates back over 60 years old to the Turing Test paper
• Aims at understanding the way words fit together with
structure and meaning.
• NLU is Connected to the field of linguistics (computational
linguistics)
• Over time, increasingly computational linguistics
overflows to a growing online web of content
What is Natural Language Understanding?
30. @dawnieando
“The meaning of a word is its use in a
language” (Ludwig, Wittgenstein,
Philosopher, 1953)
Image attribution: Mortiz, Nahr
(Public domain)
Single Words Have No Meaning
31. @dawnieando
The word ‘like’ in this sentence, is both a:
!(VBP) : (‘verb’ (non 3rd-person, singular,
present) )
!(IN) : (Preposition or subordinating
conjunction)
An Example of Word’s Meaning Changing
• I -> PRP
• Like -> VBP
• That -> IN
• He -> PRP
• Is -> VBZ
• Like -> IN
• That -> DT
33. @dawnieando
E.g. Verbs, nouns, adjectives
• Penn-treebank tagger -> 36
different parts of speech
• CLAWS7 (C7) -> 146 different
parts of speech
• Brown Corpus Tagger -> 81
different parts of speech
Words Are ‘Part of Speech’ When Combined
34. @dawnieando
• He kicked the bucket
• I have yet to tick that off
my bucket list
• The bucket was filled with
water
The Meaning of The Word ‘Bucket’ Changes
35. @dawnieando
Words Need ’Text Cohesion’
The$‘Glue’$which$adds$meaning
May$historically$be$‘stop$words’
Surrounding)words)can)change)‘intent’
They%add%‘context’
36. @dawnieando
”Ambiguity is the greatest bottleneck to computational
knowledge acquisition, the killer problem of all natural
language processing.”
(Stephen Clark, formerly of Cambridge University & now a full-
time research scientist with Google Deep Mind)
Ambiguity Is Problematic
37. @dawnieando
• Words with a similar meaning to something else
• Example: humorous, comical, hilarious, hysterical are ALL
synonyms of funny
Synonymous (Synonyms)
38. @dawnieando
Ambiguity & Polysemy
• Ambiguity is at a sentence level
• Polysemous words are arguably the
most problematic due to ‘nuanced’
nature
39. @dawnieando
• Words usually with the
same root and multiple
meanings
• Example: “Run” has 396
Oxford English Dictionary
definitions
Polysemous (Polysemy)
41. @dawnieando
• Words spelt the same but with very different ‘root’ of word
meanings
• Example: pen (writing implement), pen (pig pen)
• Example: rose (stood up / ascended), rose (flower)
• Example: bark (dog sound), bark (tree bark)
Homonyms
42. @dawnieando
Spelt differently with
VERY different
meanings but
sound exactly the
same
• Draft, draught
• Dual, duel
• Made, maid
• For, fore, four
• To, too, two
• There, their
• Where, wear, were
Homophones – Difficult To Disambiguate Verbally
46. @dawnieando
EXAMPLES
• Zipfian Distribution
• Firthian Linguistics
• Treebanks
• Language can be tied back to
mathematical spaces & algorithms
Language Has Natural Patterns & Phenomena
47. @dawnieando
Example: Zipfian Distribution (Power Law)
• The frequency of any
word in a collection is
inversely proportional to
its rank in the frequency
table
• Applies to any word
frequency ANYWHERE
• Image is 30 Wikipedias
48. @dawnieando
To illustrate Zipfian Distribution (Most used Words):
Rank Word Frequency)of)Use)in)a)Corpus
1 the
2 be 1/2
3 to 1/3
4 of 1/4
5 and 1/5
6 a 1/6
7 in 1/7
8 that 1/8
9 have 1/9
10 I 1/10
49. @dawnieando
“You shall know a word by the
company it keeps” (Firth, 1957)
Firthian Linguistics
One Such Phenomenon is Co-occurrence
50. @dawnieando
Words with similar meaning tend
to live near each other in a body
of text
Word’s ‘nearness’ can be
measured in mathematical vector
spaces – a context vector is
‘word’s company’
Distributional Relatedness & Firthian Linguistics
51. @dawnieando
Co-occurrence, Similarity & Relatedness
• Language models
are trained on
large bodies of
text to learn
‘distributional
similarity’ (co-
occurrence)
52. @dawnieando
Context Vectors & Word Embeddings
• And build vector
space models for word
embeddings
• Models learn the
weights of similarity &
relatedness distances
54. @dawnieando
• He kicked the bucket
• I have yet to tick that off
my bucket list
• The bucket was filled with
water
Remember ‘bucket’ Without Text Cohesion?
55. @dawnieando
Word’s Context Still Needed Gaps Filling
• Past models used
context-free
embeddings
• A moving
‘context window’
was used to gain
word’s context
56. @dawnieando
But Even Then True Context Needs Both Sides of a
Word
• Past models were
‘uni-directional’
• The context
window moved
from left to right
or right to left
61. @dawnieando
!Transformer is a big
deal
!Derived from a 2017
paper called ‘Attention
is all you Need’ (Vaswani, A.,
Shazeer, N., Parmar, N., Uszkoreit, J., Jones,
L., Gomez, A.N., Kaiser, Ł. and Polosukhin,
I., 2017)
What About The Transformer Part?
64. @dawnieando
River Bank or Financial Bank?
By identifying ‘cheque’ or
‘deposit’ in the company
of ‘bank’ BERT can
disambiguate from a ‘river’
bank
65. @dawnieando
So Where is BERT’s Value in Google Search
• Named entity determination
• Textual entailment (next sentence prediction)
• Coreference resolution
• Question answering
• Word sense disambiguation
• Automatic summarization
• Polysemy resolution
68. @dawnieando
!A single word can change the whole intent of a query
!Conversational queries particularly so
!The ‘stop words’ are actually part of text-cohesion
!Historically ‘stop-words’ were often ignored
!The next sentence matters
BERT and Intent Understanding
69. @dawnieando
Example:
“I remember what my
Grandad said just
before he kicked the
bucket.”
Next Sentence Prediction (Textual Entailment)
Often the next sentence REALLY matters
71. @dawnieando
• There have been lots of improvement by others upon
BERT
• Google have likely improved dramatically on BERT too
• There were some issues with next-sentence prediction
• Facebook built RoBERTa
BERT Probably Doesn’t Resemble The Original BERT
Paper
72. @dawnieando
• Named entity determination
• Coreference resolution
• Question answering
• Word sense disambiguation
• Automatic summarization
• Polysemy resolution
Featured Snippets
Knowledge Graph & Web Page Extraction
Together
73. @dawnieando
!BERT is multilingual from mono-lingual
!Other language specific BERTs are being built
!Transformer was trained on international translations
!Language has transferrable phenomena
BERT and International SEO
Expect Big Things
74. @dawnieando
• Deepset – German BERT
• CamemBERT – French BERT
• AlBERTo – Italian BERT
• RobBERT - Dutch RoBERTa model
BERT & International SEO
75. @dawnieando
!The challenges of Pygmalion
!Conversational search can now ‘scale’
!BERT takes away some of the human
labelling effort necessary
!Next sentence prediction could impact
assistants and clarifying questions
BERT and Conversational Search
Expect Big Things
76. @dawnieando
Semantic Heterogeneity Issues in Entity Oriented
Search (Semantic Search)
!Helps with anaphora & cataphora
resolution (resolving pronouns of entities)
!Helps with coreference resolution
!Helps with named entity determination
!Next sentence prediction could impact
assistants and clarifying questions
78. @dawnieando
• It’s supposed to be natural
• In the same way you can’t optimize for Rank
Brain you can’t optimize for BERT
• BERT is a tool / learning process in search for
disambiguation & contextual understanding of
words
• BERT is a ‘black-box’ algorithm
Why can’t you optimize for BERT?
79. @dawnieando
• Black-box algorithm
• Hugging Face coined the phrase
BERTology
• Now a field of study exploring why
BERT makes choices
• Some concerns over bias &
responsible AI
Black Box Algorithms & BERTology
80. @dawnieando
!Cluster together content and interlink well on topic & nuance
!Avoid ‘too-similar’ completing categories - merge
!Consider not just the content in the page but the content in
the linked pages & sections
!Consider the content of the ‘whole domain’ as everything
contributes in co-occurrence
!Be extra vigilant when ‘pruning
Utilising Co-Occurrence Strategically
Employ Relatedness
82. @dawnieando
Anyone can build a BERT to train their own
language processing system for a variety of
natural language understanding downstream
tasks.
Fine-tuning can be carried out in a short time
BERT represents a union of data science and SEO
Anyone Can Use BERT – BERT is a Tool
83. @dawnieando
• Automatic categorization & subcategorization of
content
• Automatic generation of meta-descriptions
• Automatic summarization of extracts & teasers
• Categorising user-generated content / posts
probably better than humans
How Could BERT Be Harnessed For Efficiency
in SEO? A Few Examples
84. @dawnieando
• J R Oakes - @jroakes
• Hamlet Batista - @hamletbatista
• Andrea Volpini - @cyberandy
• Gefen Hermesh - @ghermesh
SEOs Are Getting Busy With BERTishness
86. @dawnieando
• Original BERT was computationally expensive to
run
• ALBERT stands for A Lite BERT
• Increased efficiency
• ALBERT is BERT’s natural successor
• ALBERT much leaner whilst providing similar
results
• A joint research work between Google & Toyota
ALBERT – BERT’s Successor
87. @dawnieando
Reformer (Google) – Transformer’s Successor
Understands word’s context
from the perspective of a
‘whole novel’.
https://venturebeat.com/2020/01/16/goog
les-ai-language-model-reformer-can-
process-the-entirety-of-novels/
88. @dawnieando
Growth has been huge in the natural language
processing community – Current Superglue
Leaderboard
BERT Was Just The Start
• Google T5 is winning
• Even more
advanced
technology
• Transfer-learning
• Expect big things