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”Voice	
  Search	
  
SEO	
  &	
  Assistive	
  
Systems...	
  
Challenges	
  &	
  
Opportunities”	
  
“The	
  current	
  situation	
  for	
  
voice	
  search	
  &	
  SEO	
  and	
  
overcoming	
  challenges“
Dawn	
  Anderson
@DawnieAndo from	
  
@MoveItMarketing
WHO
IS
DAWN
ANDERSON
@dawnieando
dawn.anderson@@move-it-
marketing.co.uk
move-it-marketing.co.uk
linkedin.com/in/msdawnanderson
11+  years  SEO  &  Digital  Marketing  Consultant
&  Pracademic:
Contributor:
Speaker  &  Trainer:
Fellow  of:
So,	
  just	
  what	
  is	
  conversational	
  
assistance	
  &	
  where’s	
  the	
  
opportunity?
Evolution	
  of	
  
Information
Retrieval
Classic	
  
Information	
  
Retrieval	
  (IR)
Interactive	
  
Information	
  
Retrieval	
  (IIR)
Mobile	
  
Information	
  
Retrieval	
  (MIR)
Machine	
  
Learning
Something’s	
  brewing…‘A	
  Call	
  to	
  
Arms’	
  in	
  ‘Assistive	
  AI’
…	
  and	
  some	
  names
Subordinate	
  
systems
Conducive	
  
systems
Decisive	
  
systems
A	
  Trio	
  of	
  assistive	
  systems
But…	
  we	
  will	
  look	
  at	
  only	
  2	
  types	
  today…	
  which	
  
are	
  a	
  hybrid	
  within	
  the	
  assistive	
  systems
Provide	
  
answers	
  
/	
  search
Conversation	
  	
  
Search
Help	
  
with	
  
activities	
  
/	
  tasks
Conversation	
  
Actions
Ok	
  Google,	
  Hello	
  Siri,	
  
Greetings	
  Cortana
Phones,	
  Desktop,	
  Laptops	
  &	
  Smart	
  Speakers	
  
&	
  Watches
Joe	
  Public	
  is	
  in	
  awe	
  
and	
  wonder	
  at	
  Smart	
  
Speakers
Interest	
  over	
  time	
  for	
  Google	
  Home	
  &	
  Amazon	
  Alexa
Stepping	
  out	
  of	
  the	
  SEO	
  bubble
Source:	
  SISTRIX.	
  2018. Stepping	
  out	
  of	
  the	
  SEO	
  Bubble	
  -­‐ SISTRIX.	
  [ONLINE]	
  Available	
  
at: https://www.sistrix.com/blog/stepping-­‐out-­‐of-­‐the-­‐seo-­‐bubble/.
26,700+	
  respondents
Why	
  do	
  people	
  use	
  voice	
  search?
Source:	
  Higher	
  visibility	
  study	
  on	
  2000	
  people	
  (opportunity	
  sampling)
When	
  do	
  people	
  use	
  voice	
  search?
Source:	
  Higher	
  visibility	
  study	
  on	
  2000	
  people	
  (opportunity	
  sampling)
What	
  do	
  people	
  do	
  when	
  using	
  voice	
  search?
We	
  are	
  still	
  on	
  
‘Day	
  One’	
  with	
  
this	
  stuff
We	
  are	
  like	
  prospecting	
  
gold	
  miners…	
  future	
  facing
All	
  signals	
  point	
  to	
  the	
  future
By	
  2020	
  30%	
  of	
  web	
  
browsing	
  sessions	
  will	
  be	
  
done	
  without	
  a	
  screen	
  
(Gartner,	
  2016)
30%
70%
Web	
  browser	
  sessions
Without	
  a	
  screen
With	
  a	
  screen
Search	
  engines	
  &	
  IR	
  researchers	
  compete	
  over	
  voice	
  recognition
How	
  can	
  we	
  maximise conversation	
  search	
  &	
  
conversation	
  actions	
  opportunities?
Accuracy	
  of	
  
results	
  is	
  more	
  
important	
  
than	
  quantity
Let	
  us	
  first	
  look	
  
at	
  ‘conversation	
  
Search’
In	
  2017	
  we	
  asked	
  ”How”	
  more	
  than	
  anything	
  else
Since	
  voice	
  
search	
  is	
  mostly	
  
on	
  mobile	
  
devices	
  
(including	
  
phones)…	
  be	
  
VERY	
  mobile	
  
friendly
We	
  have	
  some	
  
guidance
Google’s	
  human	
  quality	
  
raters	
  guidelines
And	
  also	
  some	
  of	
  the	
  researchers	
  who	
  work	
  on	
  
the	
  Conversational	
  Search	
  team	
  at	
  Google	
  
Switzerland
Machine	
  Learning	
  
Word2Vec	
  &	
  Concept2Vec
What	
  did	
  
they	
  say?
Tip	
  1	
  – Meet	
  informational	
  needs…	
  in	
  the	
  right	
  
context
What	
  are	
  the	
  questions?
Who? What?
Where? When?
Why?
Transactional
Navigational
Informational
A	
  Taxonomy	
  
of	
  Web	
  Search	
  
(Broder,	
  2002)
Map	
  Different	
  
Question	
  
Answering	
  
Content	
  To	
  
Informational	
  
Needs
Informational
Navigational
Transactional
Guides,	
  FAQs,	
  Quick	
  
Answers,	
  How	
  to,	
  
Articles
Directions,	
  
Branch	
  locations,	
  
Meet	
  the	
  Team,	
  
Avout
Case	
  Studies,	
  
Product	
  Reviews,	
  
Testimonials,	
  
Product	
  videos,	
  
360	
  images,	
  specs
Think	
  often	
  ‘On	
  the	
  Go’	
  location	
  intent	
  focus
Tip	
  2	
  – Keep	
  answers	
  short	
  &	
  get	
  to	
  the	
  point	
  
early	
  – prosody	
  modifications	
  &	
  sentence	
  stress
Keep	
  it	
  brief	
  &	
  concise
Tip	
  3	
  – Watch	
  	
  out	
  for	
  grammar,	
  spelling	
  &	
  
pronunciation
Soundex,	
  Metaphone,	
  Double	
  Metaphone
(or	
  similar)	
  Algorithms
Tip	
  4	
  – Watch	
  out	
  for	
  pesky	
  pronouns
Linguisitics are	
  
Complicated	
  –
Watch	
  out	
  for	
  
anaphora	
  &	
  
cataphora
resolution
It’s	
  raining	
  pronouns	
  – many	
  types	
  of	
  pronouns
So	
  many	
  ways	
  to	
  misunderstand	
  natural	
  language
Intensive Reciprocal Reflexive Personal Relative Indefinite Demonstrativ
e
Possessive Interrogative
Myself Each	
  other Herself I Who Anything This Mine Who
Himself One	
  
another
Himself You Whom Everybody That His Whom
Herself Myself He Whose few Those Theirs Which
Itself Ourselves She Which many These Hers What
Ourselves Yourself We That none Ours Whatever
Yourself They What some Yours Whichever
Me Whatever Whomever
Him Whoever
Her Whomever
Us whichever
Computer	
  
programs	
  lose	
  
track	
  of	
  who	
  
is	
  who	
  easily
I’m	
  confused…	
  Here…	
  Have	
  
some	
  flowers	
  instead	
  ;P	
  ;P
They
Us
She
He Them
I
You
Him Her
Me
E.g.	
  
Minimize	
  
these	
  
personal	
  
pronouns…
That Those
These This
Time	
  &	
  Space	
  confuses	
  
things	
  further
Instead…	
  refer	
  to	
  entities	
  by	
  name	
  (where	
  
possible)
Dis…	
  Ambiguate
Unstructured	
  
data	
  (text)
Semi-­‐
structured	
  
data
Relational	
  
databases
Structured	
  
data
XML	
  sitemaps Ordered	
  lists
Unordered	
  
lists
Tabular	
  data
Data	
  Feeds
Turn	
  ‘fluffy’	
  web	
  pages	
  into	
  machine-­‐
understandable	
  formats	
  – add	
  signals
Structured	
  
data	
  explosion
2017
2014
Over	
  half	
  of	
  voice	
  
search	
  results	
  
hold	
  featured	
  
snippets	
  (Dr	
  Pete	
  
Myers,	
  Moz,	
  
2017)
Work	
  on	
  building	
  out	
  the	
  Knowledge	
  Graph
Tip	
  5	
  – Cover	
  all	
  bases	
  due	
  to	
  paraphrasing	
  
absence
• Well	
  structured	
  long	
  form	
  
informational	
  content	
  (where	
  
appropriate)
• Semantic	
  headings
• Write	
  for	
  a	
  featured	
  snippet	
  
win	
  (few	
  exceptions)
• Cover	
  the	
  bases	
  because	
  of	
  
extraction	
  &	
  compression	
  (no	
  
paraphrasing)
Tip	
  6	
  -­‐ Avoid	
  Tables
You	
  may	
  need	
  
a	
  dual	
  or	
  
triple	
  content	
  
strategy
2%
Now	
  let’s	
  look	
  at	
  ‘conversation	
  actions’
Many	
  subtasks	
  towards	
  a	
  major	
  task
Google	
  Assistant	
  – Actions,	
  
Entities,	
  Dialog	
  flows	
  &	
  Intents
Many	
  built-­‐in	
  intents	
  &	
  many	
  ‘coming	
  soon’
Extend	
  Actions	
  on	
  Google	
  using	
  Machine	
  
Learning
Extend	
  Actions	
  
on	
  Google	
  
using	
  Machine	
  
Learning
Understand	
  your	
  customers	
  to	
  assist	
  with	
  AI
Customer	
  
Service	
  Data
Customer	
  
Panels
Email	
  
questions
FAQs
Build	
  
Assistant	
  App
Understand	
  your	
  customers	
  to	
  assist	
  with	
  AI
Perceived	
  
Information	
  need
Micro-­‐task
Micro-­‐task Micro-­‐task Micro-­‐task Micro-­‐task Task
Micro-­‐task Micro-­‐task Micro-­‐task Micro-­‐task Task
Micro-­‐task Micro-­‐task Task
Micro-­‐task
Micro-­‐task
Micro-­‐task Task
Micro-­‐task Micro-­‐task Task
Micro-­‐task Task
We	
  can	
  identify	
  the	
  
user’s	
  probable	
  top	
  
tasks	
  &	
  subtasks
Identify	
  their	
  needs	
  &	
  
what	
  info	
  they	
  need	
  
along	
  the	
  way
Paraphrase	
  handling	
  on	
  ‘Actions’	
  appears	
  to	
  
be	
  programmable
Hotel	
  Booking	
  
Dialogflow
Book	
  hotel	
  
intent
When	
  do	
  you	
  
want	
  to	
  stay?
dates
dates
How	
  many	
  
nights?
3	
  nights 2	
  nights
Overnight A	
  week
Single	
  or	
  
double	
  room?
Single	
  room Double	
  room
Programme your	
  
own	
  expected	
  
questions	
  and	
  
answers
We	
  are	
  at	
  ’Day	
  One’	
  but	
  the	
  
future	
  is	
  ’Assistive’
Thank	
  you
Keep	
  in	
  touch
@DawnieAndo
@MoveItMarketing
References,	
  Sources	
  &	
  
Further	
  Reading
References
• Broder,	
  A.,	
  2002,	
  September.	
  A	
  taxonomy	
  of	
  web	
  search.	
  In ACM	
  Sigir forum (Vol.	
  36,	
  No.	
  
2,	
  pp.	
  3-­‐10).	
  ACM.
• Chuklin,	
  A.,	
  Severyn,	
  A.,	
  Trippas,	
  J.,	
  Alfonseca,	
  E.,	
  Silen,	
  H.	
  and	
  Spina,	
  D.,	
  2018.	
  Prosody	
  
Modifications	
  for	
  Question-­‐Answering	
  in	
  Voice-­‐Only	
  Settings. arXiv preprint	
  
arXiv:1806.03957.
• HigherVisibility.	
  2018. How	
  Popular	
  is	
  Voice	
  Search?	
  |	
  HigherVisibility.	
  [ONLINE]	
  Available	
  
at: https://www.highervisibility.com/blog/how-­‐popular-­‐is-­‐voice-­‐search/
• Filippova,	
  K.,	
  Alfonseca,	
  E.,	
  Colmenares,	
  C.A.,	
  Kaiser,	
  L.	
  and	
  Vinyals,	
  O.,	
  2015.	
  Sentence	
  
compression	
  by	
  deletion	
  with	
  lstms.	
  In Proceedings	
  of	
  the	
  2015	
  Conference	
  on	
  Empirical	
  
Methods	
  in	
  Natural	
  Language	
  Processing (pp.	
  360-­‐368).
• Filippova,	
  K.	
  and	
  Alfonseca,	
  E.,	
  2015.	
  Fast	
  k-­‐best	
  sentence	
  compression. arXiv preprint	
  
arXiv:1510.08418.
• Google	
  Developers.	
  2018. Content-­‐based	
  Actions	
   | Actions	
  on	
  Google	
   | Google	
  
Developers.	
  [ONLINE]	
  Available	
  at: https://developers.google.com/actions/content-­‐
actions/.	
  [Accessed	
  18	
  June	
  2018]
References
• Mitkov,	
  R.,	
  2014. Anaphora	
  resolution.	
  Routledge.
• NLP	
  Department	
  -­‐ Stanford	
  University	
  -­‐ Imran	
  Q	
  Sayed.	
  2018. Issues	
  in	
  Anaphora	
  
Resolution.	
  [ONLINE]	
  Available	
  
at: https://nlp.stanford.edu/courses/cs224n/2003/fp/iqsayed/project_report.pdf.	
  
[Accessed	
  28	
  June	
  2018].
• Radlinski,	
  F.	
  and	
  Craswell,	
  N.,	
  2017,	
  March.	
  A	
  theoretical	
  framework	
  for	
  conversational	
  
search.	
  In Proceedings	
  of	
  the	
  2017	
  Conference	
  on	
  Conference	
  Human	
  Information	
  
Interaction	
  and	
  Retrieval (pp.	
  117-­‐126).	
  ACM.
• Schalkwyk,	
  J.,	
  Beeferman,	
  D.,	
  Beaufays,	
  F.,	
  Byrne,	
  B.,	
  Chelba,	
  C.,	
  Cohen,	
  M.,	
  Kamvar,	
  M.	
  
and	
  Strope,	
  B.,	
  2010.	
  “Your	
  word	
  is	
  my	
  command”:	
  Google	
  search	
  by	
  voice:	
  a	
  case	
  study.	
  
In Advances	
  in	
  speech	
  recognition (pp.	
  61-­‐90).	
  Springer,	
  Boston,	
  MA.
• SISTRIX.	
  2018. Stepping	
  out	
  of	
  the	
  SEO	
  Bubble	
  -­‐ SISTRIX.	
  [ONLINE]	
  Available	
  
at: https://www.sistrix.com/blog/stepping-­‐out-­‐of-­‐the-­‐seo-­‐bubble/.	
  [Accessed	
  16	
  June	
  
2018].
References
• The	
  Stanford	
  Question	
  Answering	
  Dataset.	
  2018. The	
  Stanford	
  
Question	
  Answering	
  Dataset.	
  [ONLINE]	
  Available	
  
at: https://rajpurkar.github.io/SQuAD-­‐explorer/.	
  
• Trippas,	
  J.R.,	
  Spina,	
  D.,	
  Cavedon,	
  L.,	
  Joho,	
  H.	
  and	
  Sanderson,	
  M.,	
  2018.	
  
Informing	
  the	
  Design	
  of	
  Spoken	
  Conversational	
  Search.

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  • 1. ”Voice  Search   SEO  &  Assistive   Systems...   Challenges  &   Opportunities”   “The  current  situation  for   voice  search  &  SEO  and   overcoming  challenges“ Dawn  Anderson @DawnieAndo from   @MoveItMarketing
  • 2. WHO IS DAWN ANDERSON @dawnieando dawn.anderson@@move-it- marketing.co.uk move-it-marketing.co.uk linkedin.com/in/msdawnanderson 11+  years  SEO  &  Digital  Marketing  Consultant &  Pracademic: Contributor: Speaker  &  Trainer: Fellow  of:
  • 3. So,  just  what  is  conversational   assistance  &  where’s  the   opportunity?
  • 4. Evolution  of   Information Retrieval Classic   Information   Retrieval  (IR) Interactive   Information   Retrieval  (IIR) Mobile   Information   Retrieval  (MIR) Machine   Learning
  • 5. Something’s  brewing…‘A  Call  to   Arms’  in  ‘Assistive  AI’
  • 6. …  and  some  names Subordinate   systems Conducive   systems Decisive   systems
  • 7. A  Trio  of  assistive  systems
  • 8. But…  we  will  look  at  only  2  types  today…  which   are  a  hybrid  within  the  assistive  systems Provide   answers   /  search Conversation     Search Help   with   activities   /  tasks Conversation   Actions
  • 9. Ok  Google,  Hello  Siri,   Greetings  Cortana
  • 10. Phones,  Desktop,  Laptops  &  Smart  Speakers   &  Watches
  • 11. Joe  Public  is  in  awe   and  wonder  at  Smart   Speakers
  • 12. Interest  over  time  for  Google  Home  &  Amazon  Alexa
  • 13.
  • 14. Stepping  out  of  the  SEO  bubble Source:  SISTRIX.  2018. Stepping  out  of  the  SEO  Bubble  -­‐ SISTRIX.  [ONLINE]  Available   at: https://www.sistrix.com/blog/stepping-­‐out-­‐of-­‐the-­‐seo-­‐bubble/. 26,700+  respondents
  • 15. Why  do  people  use  voice  search? Source:  Higher  visibility  study  on  2000  people  (opportunity  sampling)
  • 16. When  do  people  use  voice  search? Source:  Higher  visibility  study  on  2000  people  (opportunity  sampling)
  • 17. What  do  people  do  when  using  voice  search?
  • 18. We  are  still  on   ‘Day  One’  with   this  stuff
  • 19. We  are  like  prospecting   gold  miners…  future  facing
  • 20. All  signals  point  to  the  future
  • 21. By  2020  30%  of  web   browsing  sessions  will  be   done  without  a  screen   (Gartner,  2016) 30% 70% Web  browser  sessions Without  a  screen With  a  screen
  • 22. Search  engines  &  IR  researchers  compete  over  voice  recognition
  • 23. How  can  we  maximise conversation  search  &   conversation  actions  opportunities?
  • 24. Accuracy  of   results  is  more   important   than  quantity
  • 25. Let  us  first  look   at  ‘conversation   Search’
  • 26. In  2017  we  asked  ”How”  more  than  anything  else
  • 27.
  • 28. Since  voice   search  is  mostly   on  mobile   devices   (including   phones)…  be   VERY  mobile   friendly
  • 29. We  have  some   guidance
  • 30. Google’s  human  quality   raters  guidelines
  • 31. And  also  some  of  the  researchers  who  work  on   the  Conversational  Search  team  at  Google   Switzerland
  • 32.
  • 33. Machine  Learning   Word2Vec  &  Concept2Vec
  • 35. Tip  1  – Meet  informational  needs…  in  the  right   context
  • 36. What  are  the  questions? Who? What? Where? When? Why?
  • 38. Map  Different   Question   Answering   Content  To   Informational   Needs Informational Navigational Transactional Guides,  FAQs,  Quick   Answers,  How  to,   Articles Directions,   Branch  locations,   Meet  the  Team,   Avout Case  Studies,   Product  Reviews,   Testimonials,   Product  videos,   360  images,  specs
  • 39. Think  often  ‘On  the  Go’  location  intent  focus
  • 40. Tip  2  – Keep  answers  short  &  get  to  the  point   early  – prosody  modifications  &  sentence  stress
  • 41. Keep  it  brief  &  concise
  • 42. Tip  3  – Watch    out  for  grammar,  spelling  &   pronunciation
  • 43. Soundex,  Metaphone,  Double  Metaphone (or  similar)  Algorithms
  • 44. Tip  4  – Watch  out  for  pesky  pronouns
  • 45. Linguisitics are   Complicated  – Watch  out  for   anaphora  &   cataphora resolution
  • 46. It’s  raining  pronouns  – many  types  of  pronouns
  • 47. So  many  ways  to  misunderstand  natural  language Intensive Reciprocal Reflexive Personal Relative Indefinite Demonstrativ e Possessive Interrogative Myself Each  other Herself I Who Anything This Mine Who Himself One   another Himself You Whom Everybody That His Whom Herself Myself He Whose few Those Theirs Which Itself Ourselves She Which many These Hers What Ourselves Yourself We That none Ours Whatever Yourself They What some Yours Whichever Me Whatever Whomever Him Whoever Her Whomever Us whichever
  • 48. Computer   programs  lose   track  of  who   is  who  easily I’m  confused…  Here…  Have   some  flowers  instead  ;P  ;P
  • 49. They Us She He Them I You Him Her Me E.g.   Minimize   these   personal   pronouns…
  • 50. That Those These This Time  &  Space  confuses   things  further
  • 51. Instead…  refer  to  entities  by  name  (where   possible)
  • 53. Unstructured   data  (text) Semi-­‐ structured   data Relational   databases Structured   data XML  sitemaps Ordered  lists Unordered   lists Tabular  data Data  Feeds Turn  ‘fluffy’  web  pages  into  machine-­‐ understandable  formats  – add  signals
  • 55. Over  half  of  voice   search  results   hold  featured   snippets  (Dr  Pete   Myers,  Moz,   2017) Work  on  building  out  the  Knowledge  Graph
  • 56. Tip  5  – Cover  all  bases  due  to  paraphrasing   absence
  • 57. • Well  structured  long  form   informational  content  (where   appropriate) • Semantic  headings • Write  for  a  featured  snippet   win  (few  exceptions) • Cover  the  bases  because  of   extraction  &  compression  (no   paraphrasing)
  • 58. Tip  6  -­‐ Avoid  Tables
  • 59. You  may  need   a  dual  or   triple  content   strategy 2%
  • 60. Now  let’s  look  at  ‘conversation  actions’
  • 61.
  • 62. Many  subtasks  towards  a  major  task
  • 63. Google  Assistant  – Actions,   Entities,  Dialog  flows  &  Intents
  • 64. Many  built-­‐in  intents  &  many  ‘coming  soon’
  • 65. Extend  Actions  on  Google  using  Machine   Learning
  • 66. Extend  Actions   on  Google   using  Machine   Learning
  • 67. Understand  your  customers  to  assist  with  AI Customer   Service  Data Customer   Panels Email   questions FAQs Build   Assistant  App
  • 68. Understand  your  customers  to  assist  with  AI Perceived   Information  need Micro-­‐task Micro-­‐task Micro-­‐task Micro-­‐task Micro-­‐task Task Micro-­‐task Micro-­‐task Micro-­‐task Micro-­‐task Task Micro-­‐task Micro-­‐task Task Micro-­‐task Micro-­‐task Micro-­‐task Task Micro-­‐task Micro-­‐task Task Micro-­‐task Task We  can  identify  the   user’s  probable  top   tasks  &  subtasks Identify  their  needs  &   what  info  they  need   along  the  way
  • 69. Paraphrase  handling  on  ‘Actions’  appears  to   be  programmable
  • 71. Book  hotel   intent When  do  you   want  to  stay? dates dates How  many   nights? 3  nights 2  nights Overnight A  week Single  or   double  room? Single  room Double  room Programme your   own  expected   questions  and   answers
  • 72.
  • 73. We  are  at  ’Day  One’  but  the   future  is  ’Assistive’
  • 74. Thank  you Keep  in  touch @DawnieAndo @MoveItMarketing
  • 75. References,  Sources  &   Further  Reading
  • 76. References • Broder,  A.,  2002,  September.  A  taxonomy  of  web  search.  In ACM  Sigir forum (Vol.  36,  No.   2,  pp.  3-­‐10).  ACM. • Chuklin,  A.,  Severyn,  A.,  Trippas,  J.,  Alfonseca,  E.,  Silen,  H.  and  Spina,  D.,  2018.  Prosody   Modifications  for  Question-­‐Answering  in  Voice-­‐Only  Settings. arXiv preprint   arXiv:1806.03957. • HigherVisibility.  2018. How  Popular  is  Voice  Search?  |  HigherVisibility.  [ONLINE]  Available   at: https://www.highervisibility.com/blog/how-­‐popular-­‐is-­‐voice-­‐search/ • Filippova,  K.,  Alfonseca,  E.,  Colmenares,  C.A.,  Kaiser,  L.  and  Vinyals,  O.,  2015.  Sentence   compression  by  deletion  with  lstms.  In Proceedings  of  the  2015  Conference  on  Empirical   Methods  in  Natural  Language  Processing (pp.  360-­‐368). • Filippova,  K.  and  Alfonseca,  E.,  2015.  Fast  k-­‐best  sentence  compression. arXiv preprint   arXiv:1510.08418. • Google  Developers.  2018. Content-­‐based  Actions   | Actions  on  Google   | Google   Developers.  [ONLINE]  Available  at: https://developers.google.com/actions/content-­‐ actions/.  [Accessed  18  June  2018]
  • 77. References • Mitkov,  R.,  2014. Anaphora  resolution.  Routledge. • NLP  Department  -­‐ Stanford  University  -­‐ Imran  Q  Sayed.  2018. Issues  in  Anaphora   Resolution.  [ONLINE]  Available   at: https://nlp.stanford.edu/courses/cs224n/2003/fp/iqsayed/project_report.pdf.   [Accessed  28  June  2018]. • Radlinski,  F.  and  Craswell,  N.,  2017,  March.  A  theoretical  framework  for  conversational   search.  In Proceedings  of  the  2017  Conference  on  Conference  Human  Information   Interaction  and  Retrieval (pp.  117-­‐126).  ACM. • Schalkwyk,  J.,  Beeferman,  D.,  Beaufays,  F.,  Byrne,  B.,  Chelba,  C.,  Cohen,  M.,  Kamvar,  M.   and  Strope,  B.,  2010.  “Your  word  is  my  command”:  Google  search  by  voice:  a  case  study.   In Advances  in  speech  recognition (pp.  61-­‐90).  Springer,  Boston,  MA. • SISTRIX.  2018. Stepping  out  of  the  SEO  Bubble  -­‐ SISTRIX.  [ONLINE]  Available   at: https://www.sistrix.com/blog/stepping-­‐out-­‐of-­‐the-­‐seo-­‐bubble/.  [Accessed  16  June   2018].
  • 78. References • The  Stanford  Question  Answering  Dataset.  2018. The  Stanford   Question  Answering  Dataset.  [ONLINE]  Available   at: https://rajpurkar.github.io/SQuAD-­‐explorer/.   • Trippas,  J.R.,  Spina,  D.,  Cavedon,  L.,  Joho,  H.  and  Sanderson,  M.,  2018.   Informing  the  Design  of  Spoken  Conversational  Search.