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TW-BERT: Good for
Users, Good for SEOs
Dave Cousin
Oban International
@davetheseo
https://www.slideshare.net/OBAN-IDForum
https://www.linkedin.com/in/davecousin/
obaninternational.com
What we’re going to cover
Why BERT needs improving?
What is Term Weighting?
What is BERT?
What is TW-BERT
Where this fits in Google’s
System?
How we’re going to start
optimising
obaninternational.com
TW-BERT: the basics
obaninternational.com
obaninternational.com
Humans understand context intuitively
Nike Running Shoes
Nike Running Shoes Running Shoes
obaninternational.com
obaninternational.com
obaninternational.com
obaninternational.com
What TW-BERT is basically
• A model
• Developed by Google Researchers
• Explained in a paper called ‘End to End
Query Term Weighting’*
• TW-BERT helps(/will help) Google think like
a human
• Deciding what results to show
• What order to show them
• Massively improves search intent
understanding
* https://dl.acm.org/doi/pdf/10.1145/3580305.3599815
It understand the
importance of words and
sequences of words
Whereas BERT currently deals with
wordpieces
Because…
The problem to be solved
obaninternational.com
obaninternational.com
The problem for users
• BERT was meant to understanding intent
better than ever
• It helps Google…
“grasp the subtle nuances of language that
computers don’t quite understand the way
humans do”
obaninternational.com
obaninternational.com
The problem for users
• Users have over the past
couple of years been maybe
more critical of Google’s results
than ever:
obaninternational.com
obaninternational.com
The problem for SEOs
• We’ve all had SERPs where we used to
be top and we see who took over and
think WTF
obaninternational.com
obaninternational.com
Real or imagined?
• There’s even a 2024 research paper saying Google’s results are worse:
https://downloads.webis.de/publications/papers/bevendorff_2024a.pdf
• Ok it’s not due to BERT
• But BERT hasn’t fixed it
• There IS more Spam and great matches aren’t
always getting visibility
The Solution
obaninternational.com
obaninternational.com
What is Term Weighting?
• Which sequences of words are
important?
• Non Weighted:
• Match MORE terms
Where can I get great Korean BBQ in Brighton
obaninternational.com
obaninternational.com
What is Term Weighting?
• Which sequences of words are
important?
Weighted:
• Match IMPORTANT terms
Where can I get great Korean BBQ in Brighton
obaninternational.com
obaninternational.com
Words, n-grams and wordpieces
Wordpieces (19):
I|want|##ed|a|new|##er|day|##ly|##s|##paper|from|the|##agent|to|
##day|get|##est|one|##morrow
I wanted a newer daily newspaper from the newsagent today, I want to get
the newest one tomorrow
Unigrams (16):
|I|wanted|a|newer|daily|newspaper|from|the|newsagent|today|want|
to|get|newest|one|tomorrow|
Bigrams (17):
|I wanted|wanted a|a newer|newer daily|daily newspaper|newspaper
from|from the|the newsagent|newsagent today|today, i|i want|want
to|to get|get the|the newest|newest one|one tomorrow|
obaninternational.com
obaninternational.com
Importance of n-grams
30,000
Wordpieces
35,000
Words
obaninternational.com
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Importance of n-grams
• Humans, understand other human’s
without consciously thinking about it
30,000
Wordpieces
10 million+
N-grams
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Importance of n-grams
• We identify phrases or terms we
recognise
30,000
Wordpieces
10 million+
N-grams
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obaninternational.com
Importance of n-grams
• Challenge to replicate in NLP
30,000
Wordpieces
10 million+
N-grams
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obaninternational.com
Importance of n-grams
• Needs massive high dimensional
neural networks and training
corpora
30,000
Wordpieces
10 million+
N-grams
obaninternational.com
obaninternational.com
What is BERT?
Bidirectional
Encoder
Representations from
Transformers
obaninternational.com
obaninternational.com
What is BERT?
• Deep Learning based Lexical Retriever
for NLP
Bidirectional Encoder Representations from Transformers
obaninternational.com
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What is BERT?
• Based on a (relatively) huge corpus or
training material
Bidirectional Encoder Representations from Transformers
obaninternational.com
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What is BERT?
• This allows the creation of a neural
network (right)
Bidirectional Encoder Representations from Transformers
obaninternational.com
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What is BERT?
• Each wordpiece is mapped to others
with direction & magnitude
Bidirectional Encoder Representations from Transformers
obaninternational.com
obaninternational.com
What BERT does to search queries?
• Understands relationship between all
wordpieces
• Expands queries: not just synonyms also
semantic & contextual terms e.g.
+‘restaurant’ to ‘Korean bbq’
• Weights all wordpieces
• Dynamic and considers:
• Context
• Meaning
• Semantics
• Sentiment
bose noise cancel ##ing head
with micro and blue tooth
##phone
obaninternational.com
obaninternational.com
What BERT does to search queries?
bose noise cancel ##ing head
with micro and blue tooth
##phone
obaninternational.com
obaninternational.com
What BERT does to search queries?
bose
noise
cancel
##ing
head
with
micro
and
blue
tooth
##phone
obaninternational.com
28
The basics of how search engines work
Crawl (+Render) Index
Ranking
Algorithm
Search / SERP
Send
Fetch
Results
Query
obaninternational.com
29
IR and where BERT fits in
Index Match Score
Ranking
Algorithm
BERT Search
Candidate
Docs
Fetch
Results
Query
Weighted,
expanded
3 : 2
Scored
Pairs
obaninternational.com
obaninternational.com
TW-BERT’s bridging solution
BERT’s Wordpiece Island
TW-BERT Statistical
Bridge
Opened 2023
Fast
Efficient
Can Misunderstand
Deeper Understanding
of Language
Slow
Expensive
Isle of Deep Learning
obaninternational.com
obaninternational.com
What TW-BERT does to search queries?
bose noise cancel ##ing head
with micro and blue tooth
##phone
obaninternational.com
obaninternational.com
What TW-BERT does to search queries?
bose noise cancel ##ing head
with micro and blue tooth
##phone
obaninternational.com
obaninternational.com
What TW-BERT does to search queries?
bose
noise
cancel
##ing
head
with
micro
and
blue
tooth
##phone
obaninternational.com
35
And where TW-BERT fits
Index Match Score Ranking
Algorithm
TW-
BERT
BERT Search
Candidate
Docs
Fetch
Results
Query
Weighted,
expanded
3 : 2
Scored
Pairs
Feedback
Loop
obaninternational.com
obaninternational.com
The paper: End to End Query Term Weighting
• Findings Vs BERT & Deep learning:
obaninternational.com
obaninternational.com
The paper: End to End Query Term Weighting
• Findings Vs BERT & Deep learning:
Better overall precision
Particularly good for top 10, top 100
obaninternational.com
obaninternational.com
The paper: End to End Query Term Weighting
• Findings Vs BERT & Deep learning:
Performs very well for 1-10 word
terms
obaninternational.com
obaninternational.com
Is Google using TW-BERT?
• Probably….. a bit
• Why wouldn’t they?
• It helps fix a major flaw
• Some people put July/August
fluctuations down to TW-BERT:
Source: Algoroo
obaninternational.com
obaninternational.com
If Google are, then is this it?
• Can tweak this and significance
• More feedback loop data = more certainty
and accuracy
• Easy to copy for other search engines
obaninternational.com
obaninternational.com
Are N-Grams the new keywords?
• No KWs aren’t dead
• Just a bit limp – another step in their
diminishing importance
obaninternational.com
obaninternational.com
Significance of n-grams in SEO
• We have to optimise for intent first
• KW stuffing will do nothing if the content
doesn’t deliver
obaninternational.com
obaninternational.com
Significance of improved query expansion
• More competition from relevant content
without the same KWs
• Users will use more long tail & natural
language
obaninternational.com
obaninternational.com
Are you ready for TW-BERT?
• Less competition from irrelevant
results
• But what if you have the irrelevant
result?
• Put yourself in the shoes of
searchers?
obaninternational.com
obaninternational.com
How to start optimising now: Match intent
• Understand intent
• What is ranking?
• Who has gained?
• What are they doing?
• Look at SGE too for intent
obaninternational.com
obaninternational.com
How to start optimising now: n-gram
optimisation
• Identify important n-grams in:
• Related hero search terms
• Questions or 5+ word terms
• Include naturally in context
1. How to make a beef goulash?
2. How to cook a beef goulash?
3. Cook a beef goulash
4. Beef goulash recipe
5. Step by step beef goulash
6. Beef goulash how to
obaninternational.com
obaninternational.com
How to stay optimised
• Check every 3-6 months for key terms:
• How’s language changing?
• Is knowledge changing?
• Consider intent in different countries
• What is it?
• Is it a scam?
• How To Invest?
• Focus on BitCoin
• News
• Prices • Latest scams
2012
2017
2024
‘cryptocurrency’ intent over time
obaninternational.com
obaninternational.com
So Good for Users, Google and SEOs?
• Improved intent matching helps Users find what they want faster
• And see less answers full of brand mentions
Users
• More likely to keep using Google as a search engine,
• Brand can’t cheat their way to the top, some will buy ads instead
Google
• SEOs get more relevant, qualified traffic
• Don’t have to go after irrelevant terms
SEOs
@davetheseo slideshare.net/OBAN-IDForum
linkedin.com/in/davecousin

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TW-BERT: Good for Users, Good for SEOs | Dave Cousin, Brighton SEO Deck

Editor's Notes

  1. So you don’t panic that it’s going to get super technical too quickly
  2. If we worked in a shop we would understand the importance of all of these words and sequences of words, we would get x not y But also know that if someone asks us for Niuke running trainers or sneakers that’s the same thing But if we didn’t have x we’d know what the next best thing might be BERT was introduced to improve ‘Query Expansion’ and improve Google’s ability to find semantically relevant results which might not match search terms exactly TW-Bert will further improve the link between the search term and the algorithm (without needing far more processing resource) Joins up deep learning for context and existing statistics based query expansion 1. Deep learning will better understand context and therefore importance of n-grams rather than only individual words 2. Certain important words or n-grams will be upweighted and need an exact match while others will be more open to replacement with semantically relevant terms
  3. Why am I talking to Auditorium 1 about it? Start with the very basics of what we are talking about and then layer some detail on
  4. Always been part of being an SEO complaining about who’s above you
  5. We’ve all had SERPs where we used to be top and we see who took over and think WTF Who here has seen this less in the last 6-9 months?
  6. Maybe because things did improve with BERT the challenges caused by AI content, spammy content and the like is more apparent
  7. Documents that match higher weighted terms come higher
  8. Documents that match higher weighted terms come higher
  9. Imagine all of these n-grams in a high dimensional space mapped to each other
  10. My attempt to explain with the help of AI to create an image to explain AI
  11. My attempt to explain with the help of AI to create an image to explain AI
  12. My attempt to explain with the help of AI to create an image to explain AI
  13. My attempt to explain with the help of AI to create an image to explain AI
  14. My attempt to explain with the help of AI to create an image to explain AI
  15. A
  16. A
  17. A
  18. Diagram, BERT(context and query expansion), Pipeline for candidate documents, Scoring Function (inc TF, IDF) for query-document pairs, relevancy ranking, adjustments for additional ranking factors, ranking loss (based on user signals)
  19. TW-BERT modifies BERT by combining and applying the analysis of wordpieces to the n-grams they are a part of
  20. A
  21. Note it isn’t headphone with microphone and micropone and Bluetooth, it’s headphone with microphone and headphone with bluetooth
  22. A dozen ways to ask a question what are the common/important n-grams
  23. Consider how intent varies in different countries but also demographics and develops at different speed