Recommended
Recommended
More Related Content
Similar to Better Care with NLP Matching
Similar to Better Care with NLP Matching (20)
More from FaithWestdorp
More from FaithWestdorp (18)
Recently uploaded
Recently uploaded (20)
Better Care with NLP Matching
- 1. A BETTER MATCH MEANS BETTER CARE® Kyruus, Inc. CONFIDENTIAL. DO NOT DISTRIBUTE The Search for NLP Standing up “QuickLP” for PoC
- 2. Kyruus, Inc. CONFIDENTIAL. DO NOT DISTRIBUTE Me I’m an engineer because I’m a curious person who likes products & problem- solving. - Interpersonal rhetoric - HCI - Healthcare IT - Solr - Data “intuition”
- 3. Kyruus, Inc. CONFIDENTIAL. DO NOT DISTRIBUTE A better match means better care
- 4. Kyruus, Inc. CONFIDENTIAL. DO NOT DISTRIBUTE Kyruus Search A better match means better care. The Kyruus Search & API team exists to connect humans to relevant care by connecting them to relevant data.
- 5. Kyruus, Inc. CONFIDENTIAL. DO NOT DISTRIBUTE Agenda The problem t The space t The options t
- 6. Kyruus, Inc. CONFIDENTIAL. DO NOT DISTRIBUTE Expectations Ideas, not solutions
- 7. Kyruus, Inc. CONFIDENTIAL. DO NOT DISTRIBUTE Expectations Where to look, not what to see
- 8. Kyruus, Inc. CONFIDENTIAL. DO NOT DISTRIBUTE The user journey is simple: Need t Input t Results t Problem Intent Query Documents $$
- 9. Kyruus, Inc. CONFIDENTIAL. DO NOT DISTRIBUTE Problem Information retrieval 101 User Information 😬(you)
- 10. Kyruus, Inc. CONFIDENTIAL. DO NOT DISTRIBUTE Problem Information retrieval 101 User Information 😬(you) Information Information Information Information Information Information
- 11. Kyruus, Inc. CONFIDENTIAL. DO NOT DISTRIBUTE Problem Information retrieval 101 User Information 😬(you) Information Information Information Information Information Information
- 12. Kyruus, Inc. CONFIDENTIAL. DO NOT DISTRIBUTE The Space | Statistical relevance https://towardsdatascience.com/tf-term-frequency-idf-inverse-document-frequency-from-scratch-in-python-6c2b61b78558
- 13. Kyruus, Inc. CONFIDENTIAL. DO NOT DISTRIBUTE The Space | NLP https://hackernoon.com/various-optimisation-techniques-and-their-impact-on-generation-of-word-embeddings-3480bd7ed54f
- 14. Kyruus, Inc. CONFIDENTIAL. DO NOT DISTRIBUTE Context-aware embeddings The Space | NLP
- 15. Kyruus, Inc. CONFIDENTIAL. DO NOT DISTRIBUTE The Space | NLP
- 16. Kyruus, Inc. CONFIDENTIAL. DO NOT DISTRIBUTE The Space -- NLP
- 17. Kyruus, Inc. CONFIDENTIAL. DO NOT DISTRIBUTE Reality check Your users don’t care t
- 18. Kyruus, Inc. CONFIDENTIAL. DO NOT DISTRIBUTE Reality check Your users don’t care t User Information 😬(you) Information Information Information Information Information Information
- 19. Kyruus, Inc. CONFIDENTIAL. DO NOT DISTRIBUTE Door No. 3, Johnny! Approach The work The hope The reality Spray & pray Tune, tweak, and “test” all sorts of configurations, settings, analyzers, etc. That you make enough permutations to catch most people and that the parts you can’t cover just don’t show up (head in the sand) - You’re leaving some users out in the cold - You’re spending valuable engineering resources trying to fix it in a way that will never last, simply building a house of cards that will fall as soon as something changes Host Sesame Street Spend lots of time to source a great ML/AI/NLP candidate, spend lots of money to secure the best candidate, and spend a lot of time trying to get your organization suddenly ready for the work they will do (e.g. analytics, logging, tracking, monitoring, et. al.) You’ll spend enough money to buy yourself a silver bullet, that this person will save the day - There are no silver bullets - Having the right person is only part of the equation, the organization must be at a point of maturation to support them and their work long-term - You just bet all your chips on red—and the house always wins Crawl, walk, run Find areas of opportunity and exploit them creatively with the tools on hand You solve discrete use cases, one at a time, while learning deeper opportunities and greater nuances in the user’s experience - You really do solve painful user experiences - You, your team, and your organization are given the requisite time to grow & mature into a new competency—at a fraction of the cost— while delivering on user value throughout the whole process
- 20. Kyruus, Inc. CONFIDENTIAL. DO NOT DISTRIBUTE Pareto principle Focus on the outsized gains
- 21. Kyruus, Inc. CONFIDENTIAL. DO NOT DISTRIBUTE Do the work Eyes before AIs
- 22. Kyruus, Inc. CONFIDENTIAL. DO NOT DISTRIBUTE NLP is ultimately about understanding your users The heart of NLP
- 23. Kyruus, Inc. CONFIDENTIAL. DO NOT DISTRIBUTE Simple Query pediatric cardiologist 46220
- 24. Kyruus, Inc. CONFIDENTIAL. DO NOT DISTRIBUTE Query segmentation / query understanding pediatric cardiologist 46220 age_group_id: 5 specialty_id: 1 location_id: 142
- 25. Kyruus, Inc. CONFIDENTIAL. DO NOT DISTRIBUTE Some Ideas Remember the fundamentals
- 26. Kyruus, Inc. CONFIDENTIAL. DO NOT DISTRIBUTE Remember the fundamentals Pediatric Cardiologist 46220
- 27. Kyruus, Inc. CONFIDENTIAL. DO NOT DISTRIBUTE Some Ideas Use every tool in the toolbox
- 28. Kyruus, Inc. CONFIDENTIAL. DO NOT DISTRIBUTE Use every tool in the toolbox pediatric cardiologist 46220[ ]
- 29. Kyruus, Inc. CONFIDENTIAL. DO NOT DISTRIBUTE Some Ideas Facets are features t
- 30. Kyruus, Inc. CONFIDENTIAL. DO NOT DISTRIBUTE Facets are features pediatric cardiologist 46220 46221 46222 46223 adolescent geriatric
- 31. Kyruus, Inc. CONFIDENTIAL. DO NOT DISTRIBUTE Some Ideas Honorable mentions
- 32. Kyruus, Inc. CONFIDENTIAL. DO NOT DISTRIBUTE Honorable mentions Wikipedia et. al. as source of truth “Near me” PMI
- 33. Kyruus, Inc. CONFIDENTIAL. DO NOT DISTRIBUTE Final words Focus and have fun
- 34. Kyruus, Inc. CONFIDENTIAL. DO NOT DISTRIBUTE Resources Ted Sullivan @ Lucidworks Giovanni Fernandez-Kincade Berlin Buzzwords Haystack Conference Activate Conference SparkNLP Slack Relevant Search -- book & Slack
- 35. Kyruus, Inc. CONFIDENTIAL. DO NOT DISTRIBUTE Thank you
Editor's Notes
- This isn’t a master class on what to do exactly. This is intended to stir your creativity, prod at some things that you hadn’t thought about before, and get you minded in the right direction.
- This isn’t a master class on what to do exactly. This is intended to stir your creativity, prod at some things that you hadn’t thought about before, and get you minded in the right direction.
- Bad news: you are the reason they can’t get to it Good news: you are the only way they will be able to get to it Their life is in your hands
- Bad news: you are the reason they can’t get to it Good news: you are the only way they will be able to get to it Their life is in your hands
- Bad news: you are the reason they can’t get to it Good news: you are the only way they will be able to get to it Their life is in your hands
- Word embeddings: GloVe Word2Vec Bag of words FastText
- Polysemy: I got the invite to do this talk I got anxious Hope you can say afterwards, “I got it”
- The group grew: Bert, Ernie, Big Bird, etc.
- It’s getting a bit out of hand
- As much as your users likely love Sesame Street… they don’t care about how bleeding edge your solution is They’ll be grouchier than Oscar when your solution doesn’t work. They’ll be happy as Elmo when it does—regardless of how.
- As much as your users likely love Sesame Street… they don’t care about how bleeding edge your solution is They’ll be grouchier than Oscar when your solution doesn’t work. They’ll be happy as Elmo when it does—regardless of how.
- Cute, lovable puppet characters notwithstanding
- There is likely a very fat initial part of your tail wherein you can get outsized gains and improvements. You won’t solve all the issues, but you’ll solve 80% of them with only 20% of the work or investment. Or perhaps you’ll solve the problems that equate to 80% of the user value, company bottom-line, etc. The point is this: focus on the wins, not the hows; the value, not the tech.
- You can probably spend 1-2 days tops and comb through some logs to find areas of opportunity for your application. If you can’t see it clearly then how could you spec it clearly?
- Dismax
- E.g. Dismax Optimize what you have before you build new, costly tech that needs to be optimized “Pediatric cardiologist 46220” will have a greater chance of being properly tuned to relevance once we appropriate our data and shift to the terms-centric approach found in a dismax query Suggested reading: Relevant Search by Doug Turnbull and John Berryman
- It’s not cheating to use your app layer, other technologies, etc. Redis + Zip
- It’s not cheating to use your app layer, other technologies, etc. Redis + Zip Maybe instead of just ranking higher on a zip match you want to filter on it. Regex and modify your query to be a facet. Or maybe you don’t want to filter out other zips but have concentric rings of sorting done based on your user’s submitted zip code. Adding this layer is a trivial amount of work for your engineers, it’s a trivial amount of impact to your infrastructure, e.g. Redis storage, and it’s a trivial amount of added latency to the overall request time—but it’s a non-trivial upgrade to your user’s overall experience.
- Facets are features. They’re facts about your data, simple truths that help you navigate it. This is why users use them when your precision isn’t good and your recall is really high: facets are the features they wish they’d given you or that you’d discerned. This being the case, you could be very aggressive and stuff keywords, highly-sought-after terms and phrases, into a special junk drawer for your documents and have those fields boosted in your dismax query. If nothing else, it can help you see the shape of your data a bit better in terms of density & distribution which then will help you to best facilitate the “cheapLP” solution needed to get users to the data Note, of course, that facets can be one of the first stops for your new ML/NLP engineer to get relevant feature data for their models Ted Sullivan from Lucidworks
- Wikipedia: “heart attackers” isn’t a phrase, “heart attack” is, leverage for phrase recognition Don’t build a huge algorithm to know what “near me” means -- there are plenty of fat head areas of opportunity Point-wise mutual information is a means of measuring associations in information theory, e.g. “heart” and “attack” vs. “heart attack”
- The reality is that there are a LOT of problems to solve If you don’t focus on one at a time then you probably won’t get to any of them. Sesame Street is smart in that they take one letter and one number at a time and teach it to kids. Boiling the ocean is frustrating. Solving real user problems is a lot of fun, especially if you do it in a way that is cost-effective and creates a rapidity or momentum to your stream of value-delivery