Voice Search Challenges For Search and Information Retrieval and SEO
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The document discusses challenges and advancements in voice and conversational search, emphasizing the importance of accurate spelling, grammar, and structured data for effective results. It suggests that current search engines have improved in voice recognition but still struggle with ambiguity and context in multi-turn conversations. The document also highlights the need for better ranking systems that focus on delivering concise and accurate answers to user queries.
![#SMX #32A @dawnieando
• [Skip]
• [play mumford and sons reminder] -‐ Action Response: Set a
Reminder Time: Please specify a time Fails to Meet The user
wanted to play a specific song, and the device instead set a
reminder. No users would be satisfied with this response.
Bad Result - Confusion between ‘actions’ & ‘queries’](https://image.slidesharecdn.com/smx-west-2018-dawn-anderson-challenges-with-voice-search-seo-180315203827/85/Voice-Search-Challenges-For-Search-and-Information-Retrieval-and-SEO-11-320.jpg)
![#SMX #32A @dawnieando
• Structured data (tables and data stored in databases)
• Semi-‐structured data (XML, JSON, meta headings [h1-‐h6])
• Semantically-‐enriched data (marked up schema, entities)
• Unstuctured data (normal web text copy)
• The web is messy and noisy
• Unstructured data is difficult to make sense of (no topical
strength)
The different types of data & the problem with
unstructured data](https://image.slidesharecdn.com/smx-west-2018-dawn-anderson-challenges-with-voice-search-seo-180315203827/85/Voice-Search-Challenges-For-Search-and-Information-Retrieval-and-SEO-27-320.jpg)
![#SMX #32A @dawnieando
• WordSimilarity353 Test Collection -‐http://www.cs.technion.ac.il/~gabr/resources/data/wordsim353/
• Miller, G.A. and Charles, W.G., 1991. Contextual correlates of semantic similarity. Language and
cognitive processes, 6(1), pp.1-‐28.
• Linkedin Harry Shum. 2018. From Search to Research. [ONLINE] Available
at: https://www.linkedin.com/pulse/from-‐search-‐research-‐harry-‐shum/. [Accessed 22 February 2018].
• Coreference Resolution -‐ The Stanford Natural Language Processing Group. 2018. The Stanford Natural
Language Processing Group. [ONLINE] Available at: https://nlp.stanford.edu/projects/coref.shtml.
[Accessed 19 February 2018].
Sources & References](https://image.slidesharecdn.com/smx-west-2018-dawn-anderson-challenges-with-voice-search-seo-180315203827/85/Voice-Search-Challenges-For-Search-and-Information-Retrieval-and-SEO-52-320.jpg)
Voice Search Challenges For Search and Information Retrieval and SEO
- 1. #SMX #32A @dawnieando …And how you can overcome some of them SOME CURRENT CHALLENGES WITH VOICE & CONVERSATIONAL SEARCH
- 2. #SMX #32A @dawnieando Who is Dawn Anderson? • From rainy Manchester, UK • A bit of a ‘pracademic’ (hybrid of academic and practitioner) • International SEO consultant • Move It Marketing • I lecture on search and digital marketing strategy • But I mostly ‘do’ SEO • 11 years in SEO now • Googlebot hunter ;P ;P • Consulting with brands, in-‐house teams and start-‐ ups • My pomeranian Bert is often featured in tweets and posts ;P ;P
- 3. #SMX #32A @dawnieando Interest over time on Alexa and Google Home
- 4. #SMX #32A @dawnieando Seasonal social media demonstrates mass engagement
- 5. #SMX #32A @dawnieando Eyes-‐free device sales are sky-‐rocketing
- 6. #SMX #32A @dawnieando Search Engines are Getting Better At Voice Recognition & Question Answering
- 7. #SMX #32A @dawnieando TITLE SLIDE ALTERNATIVE LAYOUT w/ *EXAMPLE* IMAGE (SWAP IN YOUR OWN AS NEEDED) In 2017 was the year of “questions”
- 8. #SMX #32A @dawnieando Google Raters guidelines for voice search published
- 9. #SMX #32A @dawnieando What does a good result look like? SPOILER • Meets informational needs • In short answers (as applicable) • Or the answer is at the beginning of the paragraph or result • Grammatically correct (syntactically well-formed) • No spelling mistakes • With accurate pronunciation
- 10. #SMX #32A @dawnieando What does a bad result look like?
- 11. #SMX #32A @dawnieando • [Skip] • [play mumford and sons reminder] -‐ Action Response: Set a Reminder Time: Please specify a time Fails to Meet The user wanted to play a specific song, and the device instead set a reminder. No users would be satisfied with this response. Bad Result - Confusion between ‘actions’ & ‘queries’
- 12. #SMX #32A @dawnieando Who knows how many times Google Home cannot help? • Only Google knows • But they aren’t sharing • Search engine embarrassment?
- 13. #SMX #32A @dawnieando RECOGNITION IS NOT NATURAL LANGUAGE UNDERSTANDING
- 14. #SMX #32A @dawnieando ESSIR2017 European Summer School on Information Retrieval Information Retrieval Lectures
- 15. #SMX #32A @dawnieando Enrique Alfonseca – Google Research Team TITLE SLIDE ALTERNATIVE LAYOUT w/ *EXAMPLE* IMAGE (SWAP IN YOUR OWN AS NEEDED)
- 16. #SMX #32A @dawnieando Better ranking needed because the user tends to focus on a single answer
- 17. #SMX #32A @dawnieando § One shot at the answer § Berrypicking ‘evolving search’ may not apply so easily § Does not benefit from query refinement and user feedback as desktop SERPs do – May be why there are still many unanswered queries Better Ranking Is Needed As The User Focuses On A Single Result
- 18. #SMX #32A @dawnieando Accurate spelling and grammar matter a lot in voice search TITLE SLIDE ALTERNATIVE LAYOUT w/ *EXAMPLE* IMAGE (SWAP IN YOUR OWN AS NEEDED) Query diversity ‘clusters’ in keyboard ‘evolving’ user search
- 19. #SMX #32A @dawnieando Accurate spelling and grammar matter a lot in voice search TITLE SLIDE ALTERNATIVE LAYOUT w/ *EXAMPLE* IMAGE (SWAP IN YOUR OWN AS NEEDED) Query refinement (via user feedback) is not possible with voice search
- 20. #SMX #32A @dawnieando #SMXInsights § No query expansion or relaxation – Precision more important than recall – Because there can be only one (or 2)
- 21. #SMX #32A @dawnieando Accurate spelling and grammar matter a lot in voice search TITLE SLIDE ALTERNATIVE LAYOUT w/ *EXAMPLE* IMAGE (SWAP IN YOUR OWN AS NEEDED) Precision > Recall in voice search Accuracy > Diversity
- 22. #SMX #32A @dawnieando A rambled answer at the end is the worst possible result
- 23. #SMX #32A @dawnieando “There is no re-‐ordering in voice search – no paraphrasing – just extraction and compression.” (Alfonseca, 2017, ESSIR2017)
- 24. #SMX #32A @dawnieando Example of classic IR teaching query interpretation system TITLE SLIDE ALTERNATIVE LAYOUT w/ *EXAMPLE* IMAGE (SWAP IN YOUR OWN AS NEEDED)
- 25. #SMX #32A @dawnieando #SMXInsights § No paraphrasing with conversational search – Paraphrasing likely needs full understanding of query & intent to reformulate
- 26. #SMX #32A @dawnieando • The knowledge base is checked first • Then the web is checked to ‘fill in gaps’ • Taking from the messy unstructured data of web pages Knowledge base first, web text second
- 27. #SMX #32A @dawnieando • Structured data (tables and data stored in databases) • Semi-‐structured data (XML, JSON, meta headings [h1-‐h6]) • Semantically-‐enriched data (marked up schema, entities) • Unstuctured data (normal web text copy) • The web is messy and noisy • Unstructured data is difficult to make sense of (no topical strength) The different types of data & the problem with unstructured data
- 28. #SMX #32A @dawnieando Structured data has never been more important for disambiguation
- 29. #SMX #32A @dawnieando • Adds meaning • Disambiguates • Adds structure • Helps with context • The web is noisy • Unstructured data is voluminous Structured Data is very, very useful here
- 30. #SMX #32A @dawnieando #SMXInsights § Simply adding topical H1 – H6 headings turns unstructured web data into semi-structured data
- 31. #SMX #32A @dawnieando Share these #SMXInsights on your social channels! #SMXInsights § Tables are problematic for voice search – Support tabular data with well formed paragraphs and sentences
- 32. #SMX #32A @dawnieando • What may be good for featured snippets (tabular data) may not be good for voice search • You may need additional strategy for voice search & tabular data in featured snippets • Pete Myers from Moz found only 30% voice search results on Google Home came from tables in featured snippets (Image credit: Pete Myers, Moz) Tables are currently problematic
- 33. #SMX #32A @dawnieando CONFIRMED BY: • Google’s Enrique Alfonseca (2017) • Microsoft’s Harry Shum (2018) • Conversational contextual search is difficult Multi-turn conversations are still challenging
- 34. #SMX #32A @dawnieando • (“anaphoric” is referring upward to previously mentioned words) • Resolution means trying to understand what it was which is referred to in those previously mentioned words Anaphoric Resolution
- 35. #SMX #32A @dawnieando • (“cataphoric” is referring downward to subsequent words) • Resolution means trying to understand what it is which is referred to in those subsequent words Cataphoric Resolution
- 36. #SMX #32A @dawnieando Likely relates to anaphoric (likely) & cataphoric (far less likely) resolution Pronouns seem still Problematic
- 37. #SMX #32A @dawnieando Our ’Previous’ Work
- 38. #SMX #32A @dawnieando AKA – Word category disambiguation • Function words – POS (Syntax) • Content words – POS (relevant) • Verbs – POS • Nouns -‐ POS • Pronouns -‐ POS • Plural-‐pronouns -‐ POS Pygmalion are carrying out Part of Speech (POS) & Named Entity Tagging (NE tags) manually
- 39. #SMX #32A @dawnieando WORD DISAMBIGUATION
- 40. #SMX #32A @dawnieando Ambiguous queries need context – ‘House’
- 41. #SMX #32A @dawnieando Linguistics are complex Homophora Endophora Exophora Hyponyms Hypernyms Homonyms
- 42. #SMX #32A @dawnieando COREFERENCE RESOLUTION IS A CHALLENGING PROBLEM FOR DISAMBIGUATION
- 43. #SMX #32A @dawnieando THE IMPORTANCE OF CO-OCCURRENCE
- 44. #SMX #32A @dawnieando ”You shall know a word by the company it keeps” (Firth)
- 45. #SMX #32A @dawnieando Other ’Previous’ Work – Similarity & Relatedness
- 46. #SMX #32A @dawnieando WordSimilarity353 Test Collection
- 47. #SMX #32A @dawnieando money cash 9.08 money currency 9.04 football soccer 9.03 magician wizard 9.02 gem jewel 8.96 car automobile 8.94 boy lad 8.83 furnace stove 8.79 Maradona football 8.62 king queen 8.58 money bank 8.5 Jerusalem Israel 8.46 vodka gin 8.46 planet star 8.45 money dollar 8.42 vodka brandy 8.13 bank money 8.12 physics proton 8.12 planet galaxy 8.11 stock market 8.08 psychology psychiatry 8.08 planet moon 8.08 planet constellation 8.06 planet sun 8.02 tiger feline 8 planet astronomer 7.94 movie theater 7.92 planet space 7.92 baby mother 7.85 wood forest 7.73 money deposit 7.73 psychology mind 7.69 Jerusalem Palestinian 7.65 Arafat terror 7.65 computer keyboard 7.62 computer internet 7.58 money property 7.57 tennis racket 7.56 psychology cognition 7.48 book paper 7.46 book library 7.46 media radio 7.42 psychology depression 7.42 jaguar cat 7.42 movie star 7.38 bird crane 7.38 tiger cat 7.35 physics chemistry 7.35 money possession 7.29 jaguar car 7.27 cup drink 7.25 psychology health 7.23 bird cock 7.1 company stock 7.08 tiger carnivore 7.08 WordSimilarity353 Test Collection
- 48. #SMX #32A @dawnieando #SMXInsights § Secondary or 3-way strategy may be needed – Add a TL:DR – Or an executive summary – Or Q & A based table of contents – Or a ‘Short Answer’ then ‘Longer Answer’
- 49. #SMX #32A @dawnieando #SMXInsights § Mine forums, customer service, chat & emails – Build word clouds to provide answers to topics which matter to your audience
- 50. #SMX #32A @dawnieando Accurate spelling and grammar matter a lot in voice search TITLE SLIDE ALTERNATIVE LAYOUT w/ *EXAMPLE* IMAGE (SWAP IN YOUR OWN AS NEEDED) Soundex, Metaphone or similar ’misspelling’ algorithms may not apply to voice search
- 51. #SMX #32A @dawnieando LEARN MORE: UPCOMING @SMX EVENTS THANK YOU! SEE YOU AT THE NEXT #SMX
- 52. #SMX #32A @dawnieando • WordSimilarity353 Test Collection -‐http://www.cs.technion.ac.il/~gabr/resources/data/wordsim353/ • Miller, G.A. and Charles, W.G., 1991. Contextual correlates of semantic similarity. Language and cognitive processes, 6(1), pp.1-‐28. • Linkedin Harry Shum. 2018. From Search to Research. [ONLINE] Available at: https://www.linkedin.com/pulse/from-‐search-‐research-‐harry-‐shum/. [Accessed 22 February 2018]. • Coreference Resolution -‐ The Stanford Natural Language Processing Group. 2018. The Stanford Natural Language Processing Group. [ONLINE] Available at: https://nlp.stanford.edu/projects/coref.shtml. [Accessed 19 February 2018]. Sources & References
- 54. #SMX #32A @dawnieando EXAMPLES • Look at Wikipedia Redirects • Alternative names redirect to the most appropriate article title (for example, Edison Arantes do Nascimento redirects to Pelé) (Wikipedia) • SPARQL and DBPedia identifies many variations • (Beethoven example) • https://dbpedia.org/sparql • https://en.wikipedia.org/wiki/Wikipedia:Redirect Terms can have many ‘surface forms’
- 55. #SMX #32A @dawnieando ” It is concluded…the more often two words can be substituted into the same contexts the more similar in meaning they are judged to be.” (Miller & Charles,1991)
- 56. #SMX #32A @dawnieando Accurate spelling and grammar matter a lot in voice search TITLE SLIDE ALTERNATIVE LAYOUT w/ *EXAMPLE* IMAGE (SWAP IN YOUR OWN AS NEEDED) Difficult to deal with ‘query ambiguity’ Result ‘diversity’ assists with query ambiguity in desktop or non-‐voice results
- 57. #SMX #32A @dawnieando Accurate spelling and grammar matter a lot in voice search TITLE SLIDE ALTERNATIVE LAYOUT w/ *EXAMPLE* IMAGE (SWAP IN YOUR OWN AS NEEDED) Page Length ‘Normalization’ may not apply as with traditional results?? (Me musing)
- 58. #SMX #32A @dawnieando Long numbers should be rounded § 60,999,888.999999999 – It reads terribly – Needs to be rounded
- 59. #SMX #32A @dawnieando • First checks whether the next ‘turn’ of question relates to the previous question • Using LSTMs (Long Short Term Memory) • Bi-‐directional context embedding • Query and its context are both used as input Conversational Context & Microsoft
- 60. #SMX #32A @dawnieando Katja Filippova – Google Research Team TITLE SLIDE ALTERNATIVE LAYOUT w/ *EXAMPLE* IMAGE (SWAP IN YOUR OWN AS NEEDED)
- 61. #SMX #32A @dawnieando Query expansion and query relaxation
- 62. #SMX #32A @dawnieando https://www.ntid.rit.edu/sea/processes/referencewords/practice/ph oric Example of cataphoric and anaphoric resolution