What are Word Vectors & How Structured Markup Supercharges them

What are word vectors?

Types of word Embedding (Word Vectors)

  • Frequency-based Embedding
  • Prediction based Embedding

A brief introduction to WORD2Vec

  • [king] — [man] + [woman] ~= [queen] (another way of thinking about this is that [king] — [queen] is encoding just the gendered part of [monarch])
  • [walking] — [swimming] + [swam] ~= [walked] (or [swam] — [swimming] is encoding just the “past-tense-ness” of the verb)
  • [madrid] — [spain] + [france] ~= [paris] (or [madrid] — [spain] ~= [paris] — [france] which is presumably roughly “capital”)
  • – CBOW (Continuous bag of words) or CBOW model
  • – Skip-gram model.

How word vectors and word2vecs works

[Case Study] Driving growth in new markets with on-page SEO

Word2Vec- How does it work?

Pros and Cons of Word2Vec

  • There is no difficulty understanding Word2vec concepts. Word2Vec is not so complex that you aren’t aware of what is happening behind the scenes.
  • Word2Vec’s architecture is very powerful and easy to use. Compared to other techniques, it is fast to train.
  • Training is almost entirely automated here, so human-tagged data is no longer required.
  • This technique works for both small and large datasets. As a result, it is an easy-to-scale model.
  • If you know the concepts, you can easily replicate the entire concept and algorithm.
  • It captures semantic similarity exceptionally well.
  • Accurate and computationally efficient
  • Since this approach is unsupervised, it is very time-saving in terms of effort.

Challenges of Word2Vec

  • When developing a word2vec model for your dataset, debugging can be a major challenge, as the word2vec model is easy to develop but hard to debug.
  • It does not deal with ambiguities. So, in the case of words with multiple meanings, Embedding will reflect the average of these meanings in vector space.
  • Unable to handle unknown or OOV words: The biggest problem with word2vec is the inability to handle unknown or out-of-vocabulary (OOV) words.

Word Vectors: A Game-changer in Search Engine Optimization?

Hummingbird

RankBrain

BERT

Rank your site with Word Vectors

  • The first step is to Identify the Word Vectors of the top 10 SERP rankings for your niche.
  • Know what keywords your competitors are using and what you might be overlooking.

WordGraph, World’s First Word Vector Tool

How to Supercharge Word Vectors: Using Structured Data Markup

How to add structured data

Why is Structured Data Important for SEO?

  • Structured data support semantic search
  • It also supports your E‑A-T (expertise, authoritativeness, and trust)
  • Having structured data can also increase conversion rates, since more people will see your listings, which increases the likelihood that they will buy from you.
  • Using structured data the search engines are better able to understand your brand, your website, and your content.
  • It will be easier for search engines to distinguish between contact pages, product descriptions, recipe pages, events pages, and customer reviews.
  • With the help of structured data Google builds a better, more accurate knowledge graph and knowledge panel about your brand.
  • These improvements can result in more organic impressions and organic clicks.
  • Articles
  • Blog Posts
  • News Articles
  • Events
  • Products
  • Videos
  • Services
  • Reviews
  • Aggregate Ratings
  • Restaurants
  • Local businesses

Structured Data with Entity Embeddings

Final Thoughts

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A #DigitalStrategist, Marketing Consultant, #SOCIALMEDIA INFLUENCER, and #GrowthHacker

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Ashok Sharma

Ashok Sharma

A #DigitalStrategist, Marketing Consultant, #SOCIALMEDIA INFLUENCER, and #GrowthHacker

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