Demystifying IBM Watson: Uncover the Power of Cognitive Solutions
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Successful organizations recognize that information is a strategic asset, capable of strengthening decision making, improving efficiency, reducing risk, and enhancing customer relationships. With the tremendous surge in the volume and diversity of data, leveraging this information across the entire enterprise is a business imperative that cannot be ignored. IBM Watson harnesses the power of cognitive exploration, machine learning, and natural language processing to answer your most pressing questions, strengthen decision making, scale expertise, uncover key information in unstructured data, and reveal previously undiscovered data patterns and relationships. In this SlideShare, we discuss: Trends in cognitive solutions Use cases for IBM Watson Real-world Watson success stories Getting started on the path to cognitive solutions
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Demystifying IBM Watson: Uncover the Power of Cognitive Solutions
- 1. Demystifying IBM Watson: Uncovering the Power of Cognitive June 2017
- 2. 2 About Perficient Perficient is the leading digital transformation consulting firm serving Global 2000 and enterprise customers throughout North America. With unparalleled information technology, management consulting, and creative capabilities, Perficient and its Perficient Digital agency deliver vision, execution, and value with outstanding digital experience, business optimization, and industry solutions.
- 3. 3 Perficient Profile • Founded in 1997 • Public, NASDAQ: PRFT • 2016 revenue $487 million • Major market locations: Allentown, Atlanta, Ann Arbor, Boston, Charlotte, Chicago, Cincinnati, Columbus, Dallas, Denver, Detroit, Fairfax, Houston, Indianapolis, Lafayette, Milwaukee, Minneapolis, New York City, Northern California, Oxford (UK), Southern California, St. Louis, Toronto • Global delivery centers in China and India • Nearly 3,000 colleagues • Dedicated solution practices • ~95% repeat business rate • Alliance partnerships with major technology vendors • Multiple vendor/industry technology and growth awards
- 4. 4 Speaker Introduction CHRISTINE LIVINGSTON Director, IBM Watson Perficient
- 5. 5 Demystifying IBM Watson: Uncovering the Power of Cognitive
- 6. 6 • Introduction • What is Watson? ⎼ Cognitive Computing ⎼ Watson Explorer ⎼ Watson Developer Cloud • Case Studies • Getting Started with Watson Agenda
- 7. 7 Watson Practice Overview Delivering Innovative Solutions through Watson’s Groundbreaking Cognitive Computing Capabilities • IBM Watson Talent Partner • Nearly 20 delivery professionals • Leveraging experience in Analytics, Big Data, Unstructured Content Management, Enterprise Search, Digital Experience and Business Optimization • Watson Explorer Enterprise • Watson Explorer Advanced • Watson Developer Cloud • Watson Knowledge Studio • BlueMix • SoftLayer • Cognitive Readiness Evaluations • Cognitive Solution Business Case Dev • Watson Explorer Foundational Enterprise Search Implementations • Watson Explorer Advanced Quickstart • Watson Explorer Advanced Content Analytics Implementations • Customized Content Analytics Solutions • Custom 360º Applications • Virtual Agent Implementations • Watson Developer Cloud Integration Services • Watson Knowledge Studio Domain Modeling Practice Overview Key Technologies Offerings and Services 2017 Beacon Award Winner for an Outstanding Watson Cognitive Solution
- 10. 10 Watson: A Cognitive Platform Understand The ability to understand structured and unstructured data, text-based or sensory in context and meaning, at astonishing speed and volume. Reason The ability to form hypotheses, make considered arguments and prioritize recommendations to help humans make better decisions. Learn Ingest and accumulate data and insight from every interaction continuously. Trained, not programmed, by experts to enhance, scale and accelerate their expertise.
- 11. 11 Watson Developer Cloud Cognitive APIs Watson for Oncology Enables physicians to make evidence-based treatment decisions to improve care WEX Advanced Uncovers the meaning and context of human language with unstructured information Watson Explorer (WEX) Helps organizations discover, understand, & virtually integrate their data into a unified view Watson Knowledge Studio Enables SMEs to teach Watson to “read,” extracting relevant entities and relationships Watson: A Cognitive Platform
- 12. 12 Why is Cognitive Important? The volume, variety and veracity of data, 80% of it unstructured is growing at a rate impossible to keep up with. Customers have a wider range of choices than ever before and are expecting innovative, relevant and personalized engagement. Companies must engage customers on their terms - in a consistent, natural, and intuitive way. Cognitive is the new competitive advantage for enterprises focused on enhancing the customer experience.
- 13. 13 Column Value Patient Joe Brown Date of Birth 02/13/1972 Date Admitted 02/05/2014 Structured Data High degree of organization, such as a relational database “The patient came in complaining of chest pain, shortness of breath, and lingering headaches…smokes 2 packs a day… family history of heart disease…has been experiencing similar symptoms for the past 12 hours….” Unstructured Data Information that is difficult to organize using traditional mechanisms Structured vs. Unstructured Data
- 14. 14 explorer India In May 1898 India In May celebrated anniversary in Portugal In May, Gary arrived in India after he celebrated his anniversary in Portugal Portugal 400th anniversary celebrated Gary In May 1898 Portugal celebrated the 400th anniversary of this explorer’s arrival in India This evidence suggests “Gary” is the answer BUT the system must learn that keyword matching may be weak relative to other types of evidence arrived in arrival in Legend Keyword “Hit” Reference Text Answer Weak evidenceRed Text Answering complex natural language questions requires more than keyword evidence Analyzing Unstructured Content
- 15. 15 27th May 1498 Vasco da Gama landed in arrival in explorer India Para- phrases Geo- KB Date Match Stronger evidence can be much harder to find and score … … and the evidence is still not 100% certain Search far and wide Explore many hypotheses Find judge evidence Many inference algorithms On the 27th of May 1498, Vasco da Gama landed in Kappad Beach 400th anniversary Portugal May 1898 celebrated In May 1898 Portugal celebrated the 400th anniversary of this explorer’s arrival in India. Kappad Beach Legend Temporal Reasoning Reference Text Answer Statistical Paraphrasing GeoSpatial Reasoning Leverage multiple algorithms The Watson Difference
- 16. 16 Watson Explorer Enterprise Edition Connect data silos, enable 360° views, enable true enterprise search • Search naturally using everyday language with Natural Language Query (NLQ) • Improve search results without manual oversight using unsupervised machine learning algorithms • Combine content and data from many different systems throughout and outside the enterprise • Unique indexing structure enables the exact information needed by the user to be delivered • Transition to cognitive exploration with the Watson Developer Cloud
- 17. 17 Watson Explorer Advanced Edition Transform unstructured data to structured data, uncover relationships and correlations • Advanced content analytics to aggregate, analyze and visualize unstructured content to reveal hidden insights and patterns • Content analytics provides insights to answer why and how • Analyze structured and unstructured information sources • Leverage results in WEX or traditional analytics tools such as IBM Cognos • Annotate and enrich with domain models that can be rapidly developed using Watson Knowledge Studio • Transition to cognitive analytics with the Watson Developer Cloud
- 18. 18 Alchemy Language Concept Insights Relationship Extraction Speech to Text Language Translation Visual Recognition Retrieve & Rank Dialog Alchemy DataNews Tone Analyzer Personality Insights Text to Speech Natural Language Classifier Document Conversion Tradeoff Analytics Language Vision Speech Conversation Explore & Discover Data & Insights Watson Developer Cloud Services
- 19. 19 Observe Interpre tDecide Evaluate Observe InterpretDecide Evaluate Master Practice Apprentice Study Traditional Learning Curve Learning Curve with Watson How Watson Drives Value Watson enhances the cognitive process of professionals to strengthen decision making in the moment. Watson scales expertise by elevating the consistency and objectivity of decision making across an organization. Scales Accelerates Watson captures the expertise of top performers and accelerates the development of that expertise in others. Enhances
- 20. Usage Patterns
- 21. 21 Customer Service and Engagement Agent Assist • Provide 360° views • Deliver consistent and accurate answers • Efficiently scale expertise to novice agent • Personalize the customer experience Virtual Agents • Provide self-service options • Guide customers through transactions • Engage customers through several mediums Integrated Voice Solutions • IVR replacement/enhancement • “Active listening” Customer Service Interaction Analysis • Support multiple channels (social media, call center, email exchanges) • Understand customer tone and sentiment • Uncover hidden trends and relationships
- 22. 22 Scripted vs. Cognitive Conversations • Driven by a pre-defined conversation flow • Expects key phrases or words • Functions best on structured data • Best for short and simple tasks • Relatively quick to implement Scripted Conversations • Driven by conversational intents rather than expected flow • Trained to understand natural language • Operates on both structured and unstructured data • Learns over time • Capable of a wide range of tasks • Training time varies by complexity Cognitive Conversations
- 23. 23 Cognitive Use Cases by Industry Insurance • Claims Processing and Analysis • Policy & Underwriting Support • Policy & Benefits Management • Customer Self Service • Customer 360° View Banking • Cognitive Underwriting • Loan Origination • Fraud Management & Compliance • Customer Care & Insight • Customer 360° View Retail • Guided Shopping • Simplified, Multi-Channel Ordering • Customer Loyalty Programs Manufacturing • Early Defect Warning • Manufacturing Production Quality & Control • Warranty Analysis Energy & Utilities • Power Grid Management • Energy Consumption Management Travel & Transportation • Self-Service Options • Online Ticketing & Reservations Healthcare • Streamlined Care Management • Patient Population Identification • Readmission Rates • Diagnostic Assistance • Medical Coding
- 24. Case Studies
- 25. 25 Improved patient readmissions prediction accuracy with IBM Watson and SPSS Analyzed structured and unstructured data Created a predictive readmission model Understand underlying causes of readmission Prediction accuracy moved to 93% with Watson Midwestern Healthcare Provider
- 26. 26 Uncovering insights in complaint data to improve customer service Analyzed Unstructured Customer Complaint Data Normalized Issues from Customer Complaints Provided Insights into Most Common Issues and Customer Service Gaps Identified Critical Engineering Issues Prioritize Future Engineering Releases Healthcare Imaging Manufacturer
- 27. 27 A Watson conversation that transforms data into insights Chat-Based Mobile Watson Application Transformed a Manual Analysis and Data Evaluation Process Provide Data-Driven Insights and Natural Language Answers Generates Insights for Brand Managers to Focus on Improving Sales and Customer Experience Consumer Insights Agency
- 28. 28 A Watson Digital Concierge Reshaped the User Experience Autonomously Handles Tier-1 Requests (60% Upon Initial Release) Supports Software Activation and Maintenance Tasks 300% Increase in Web Traffic http://bot.autodesk.com/Try It Out: 90% 99% lower support costs shorter resolution times North American Software Company
- 29. 29 63%reduced AHT Interactive Agent for Healthcare Providers Cognitive Agent Converses with Providers to Verify Benefits Seamlessly Manages Member Information Inquiries Transformed a Tedious IVR System Drastic Reduction in Live Agent Requests Call Time Reduced from 8 to 3 Minutes Midwestern Healthcare Payer
- 30. 30 Patient population identification and care management optimization Watson Analyzed the Patient EHR Accurately identified patients presenting a specific disease state Streamlined the care management process Care managers are now able to see more patients and spend more time with patients 41% 75% improvement in patient identification accuracy decrease in EHR review time Healthcare Provider
- 32. 32 Getting Started with Watson Rapidly iterate through Watson’s application in your organization, define measurable goals for your cognitive analytics implementation, and begin your cognitive journey. Ideate on and discover the possibilities of cognitive analytics and industry applications for your organization. Rapidly prototype to envision future potential. IBM Watson Workshop IBM Watson Innovation Lab 3-4 WeeksHalf- to Full-Day
- 33. 33 Questions? Type your question into the chat box
- 34. 34 Next up: [Webinar] How Watson & BPM are Transforming Insurance Tuesday, June 20th [Webinar] Accelerate your Time to a Successful Deployment with DevOps Thursday, June 15th Follow Us Online • Perficient.com/SocialMedia • Facebook.com/Perficient • Twitter.com/Perficient_IBM • Blogs.perficient.com/IBM
- 35. Thank You
- 36. Appendix
- 37. 37 Watson enhances the cognitive process of professionals to strengthen decision making in the moment Observe Interpre tDecide Evaluate Observe InterpretDecide Evaluate Watson scales expertise by elevating the consistency and objectivity of decision making across an organization. Scales Accelerates Watson captures the expertise of top performers and accelerates the development of that expertise in others. Master Practice Apprentice Study Traditional Learning Curve Learning Curve with Watson Enhances How Watson Drives Value
- 38. 38 • Improve self-service options through natural language interfaces, reducing the number of calls received • Provide 360° insight into customer, product, tickets, etc. • Personalize the client experience with deep insights into preferences and interaction history • Deliver consistent and accurate answers • Efficiently scale expertise to novice agents • Additional insights identified through analysis of all existing knowledge and problem history – Which problems / issue areas take long to solve? – Trends and deviations? Peaks? – Has the same or a similar problem already occurred? – Any issues known with this entity / product / …? – Who do I need to contact (Who solved it before?) – Related cases / workarounds Contact Center Agents Watson Explorer Applications and Data Sources Watson Developer Cloud Empower agents to better respond to requests and improve conversion rates Watson Agent Assist
- 39. 39 Channel proliferation has consumers expecting instantaneous personalized, high quality interactions regardless of the contact channel the consumer chooses. Watson Virtual Agent offers customers a cognitive, conversational self-service engine that can provide answers and take action through a variety of channels at scale. What is Watson Virtual Agent, and what can it do for you and your customers? Watson Virtual Agent on IBM Marketplace Watson Virtual Agent Business Problem: Solution: Learn More: • Personalized, Contextual digital assistant that can take action on customer’s request • Pre-Trained Natural Language Understanding conversations for customer service domain • Customer Service-focused dialog flows across a range of complexities • Conversation Tooling & Dashboard for managing customer experiences • Software-as-a-Service solution with continuous delivery of enhancements and new content Quantitative Benefits • Absorb Deflected Contacts from higher cost channels • Increased first-contact resolution • Increased revenue through re-tasking Human reps • Decreased Agent-Agent Transfers Qualitative Benefits • Satisfy customer demand through the channel they choose • Consistent Omnichannel Customer Experience • Increases in Lifetime Value, Net Promoter Score
- 40. 40 How Watson Explorer Learns Knowledge Curation Ground Truth Creation Annotator Development Annotator Evaluation Runtime Deployment Rules-Based • Uses rules to perform natural language analysis • Better approach with smaller number of entities, simple relationships • Easy to trace and debug • May require programming for the rules, sometimes with a significant learning curve • Difficult to maintain as the complexity grows Machine Learning • Uses inferences and statistical models to perform natural language analysis • Taught by examples without the need to write code • Better approach with large number of entities, complex relationships • Somewhat opaque to the developer and can be more difficult to debug • Requires creating ground truth Watson Explorer Content Analytics Studio Watson Knowledge Studio DEV SME • Cloud Based • Intuitive, No Code Writing • Collaborative • Cost Effective • Enables a Hybrid Approach • Supervised Training Yields High Precision • Use Existing WEX CA Studio Annotators to “Bootstrap” Machine Learning • Lower Training Time by Factor of 2 Watson Knowledge Studio Benefits Data Source Crawl and Contextualize Position for access Analyze Output For an animated overview, please go to: https://www.youtube.com/watch?v=ymUFadN_MO 4 John Smith works for IBM. He has been in Big Blue for 20 years. Person ORG Requires transforming unstructured data to structured data and preparing it before it can be used for further analysis. What is Annotation? (Text Analytics)
- 41. 41 Teaching Watson – Watson Knowledge Studio
- 42. 42 Teaching Watson – WEX Advanced
Editor's Notes
- Watson is a collection of cognitive computing solutions and platforms, providing cognitive capabilities such as relating, reasoning, and perceiving. 3. IBM Watson is a cognitive system that can make sense of unstructured information Cognitive systems mimic how humans reason and process information. IBM Watson is an example of a cognitive system. It offers deep natural language processing that focuses much more on context rather than following a set of pre-defined rules. It assesses as much context as possible from both the question posed and from its available knowledge base to form a response. https://www.ibm.com/developerworks/community/blogs/5things/entry/5_things_to_know_about_cognitive_systems_and_ibm_watson7?lang=en
- We faced a lot of technical challenges but at the center of the problem is dealing with the many was you can express the same meaning in natural language. NL is often very sensitive to context and is often incomplete, tacit and ambiguous. Simplified approaches can easily lead you astray. These next two examples should help motivate our approach. Consider this question. <Read it> Now consider that based simply on keywords it would be straight-forward to pick up this potentially answer-bearing passage. <read green passage> This is a great hit from a keyword perspective in shares many common terms – May, Arrived, Anniversary, Portugal, India etc. and by using keyword evidence should give good confidence that Gary is the explorer in question. And whose to say Garry is not an Explorer. After all, we are all explorers in our own special way. In fact, the next sentence might read – and then Gary returned home to explore his attic looking for a lost photo album. Such a sentence would be legitimate evidence that Gary can be classified as an Explorer. Classifications are tricky, we humans are very flexible in how we classify things – we are willing to accept all sorts of variations in meaning to make language work. Of course in this case, the famous explorer Vasco De Gama is the correct answer but how would a computer know that for sure. A computer system must learn to dig deeper, to find, evaluate and weigh different kinds of evidence – ultimately finding the answer that is best supported by the content. Consider this…<next slide>
- Here we see the same question on the right <read it again> To identify and gain confidence in better evidence, the system must parse the question, determining its grammatical structure and identify the main predicates like celebrated and arrived along with their main arguments (that is their subjects and objects, etc) for example -- who is doing the celebrating, and who is doing the arriving AND for each of these actions where and when are they happening. This would further require the system to attempt to distinguish places, dates and people from each other and from other words and phrases in the question. On the right side, we see a passage containing the RIGHT answer BUT with only one key word in common -- “MAY”. <read the green passage> Given just that one common and very popular term, the system must look at a huge amount of unrelated stuff to even get a chance to consider this passage and then must employ and weigh the right algorithms to match the question with an accurate confidence, for example in this case <click> Temporal reasoning algorithms can relate a 400th anniversary in 1898 to 1498, Statistical Paraphrasing algorithms can help the computer learn from reading lots of texts that landed in can imply arrived in and finally with Geospatial reasoning using geographical databases the system may learn that Kappad Beach is in India and if you arrive in Kappad Beach you have therefore arrived in India. And still, all of this will admit numerous errors since few of these computations will produce 100% certainty in mapping from words, to concepts to other words. Just as an example, what if the passage said “considered landing in” rather than “landed in” or what if it the question said “arrival in what he thought to be India?”. Question Answering Technology tries to understand what the user is really asking for and to deliver precise and correct responses. But Natural language is hard … the authors intended meaning can be expressed in so many different ways. To achieve high levels of precision and confidence you must consider much more information and analyze it more deeply. We needed a radically different approach that could rapidly admit and integrate many algorithms, considering lots of different bits of evidence from different perspectives, AND that could learn how to combine and weigh these different sorts of evidence ultimately determining how strongly or weakly they support or refute possible answers.
- NLQ is a key capability that is in beta and will be available in June. This module allows end users to phrase queries in everyday natural language and automatically employs stopword removal based on dictionary and parts of speech while simplifying long queries. Additionally the module also enables machine learning driven results where unsupervised machine learning algorithms can be employed to improve the quality of search results. Domain Models developed using Watson Knowledge Studio to annotate and enrich content can be integrated with Watson Explorer when both WKS and v11.0.1 are released. Note that WKS is sold separately. AppBuilder that enables development of unified information applications will feature several new capabilities such as out of-the-box reports for understanding search and usage metrics as well as Proactive notifications to follow changes in results for saved search queries. A new connector for IBM’s Websphere Portal Server (WPS) as well as the latest versions of IBM Web Content Manager (WCM) is also packaged with the release.
- NLQ is a key capability that is in beta and will be available in June. This module allows end users to phrase queries in everyday natural language and automatically employs stopword removal based on dictionary and parts of speech while simplifying long queries. Additionally the module also enables machine learning driven results where unsupervised machine learning algorithms can be employed to improve the quality of search results. Domain Models developed using Watson Knowledge Studio to annotate and enrich content can be integrated with Watson Explorer when both WKS and v11.0.1 are released. Note that WKS is sold separately. AppBuilder that enables development of unified information applications will feature several new capabilities such as out of-the-box reports for understanding search and usage metrics as well as Proactive notifications to follow changes in results for saved search queries. A new connector for IBM’s Websphere Portal Server (WPS) as well as the latest versions of IBM Web Content Manager (WCM) is also packaged with the release.
- We offer a collection of cognitive APIs across broad categories of Language, Speech, Vision, Discover, Conversation and Data & Insights. Application starter kits demonstrate how APIS can be combined to solve real world business problems. For example, Conversation agent combines Dialog and NLC to help developers build engaging conversational bots. Customization - We are introducing customization capability across all our services. Visual recognition service allows a developer to train its own image datasets. Across all the modalities of speech, language and vision, we will have the ability to train on corpus of data. Watson APIs are delivered on a cloud-based, open platform, and with Watson, you can build cognition into your digital applications, products, and your and your clients’ operations, using any one or combination of the available APIs. For example, Natural Language Classifier API enables developers without a background in machine learning or statistical algorithms to create machine-learning, natural language interfaces for their applications. Tone analyzer helps individuals understand the linguistic tone of their writing. This API uses linguistic analysis to detect and interpret emotional, social, and writing cues that are located within the text, and also offers rhetorical suggestions for an author to improve the intended tone. Retrieve and rank helps users find the most relevant information for their query by using a combination of search and machine learning algorithms to detect “signals” in the data. – cognitive building blocks – to leverage capabilities including relationship extraction, personality analysis, tone analysis, concept expansion, and trade-off analytics, among others. Each API is capable of performing a different task, and in combination, they can be adapted to solve any number of business problems or create deeply engaging experiences. And we continue to add new and expanded cognitive capabilities to the platform.
- Key messages: + To understand enhancement, we first need to understand how people make decisions on the job. + We observe the situation around us. + We interpret the situation based on experience and knowledge. + We evaluate available information to surface insights and choices. + We decide on the best course of action.
- Key messages: + To understand enhancement, we first need to understand how people make decisions on the job. + We observe the situation around us. + We interpret the situation based on experience and knowledge. + We evaluate available information to surface insights and choices. + We decide on the best course of action.
- Watson Virtual Agent on IBM Marketplace - https://www.ibm.com/marketplace/cloud/cognitive-customer-engagement/us/en-us
- We have multiple Watson offerings and each one may learn a little differently. Our offering developers take great care in leveraging the approaches that fit each solution best. - Watson Explorer uses a native rules-oriented approach to configuring the annotation capability but can leverage both rules based and machine learning based models. Annotation is a technique for identifying the structure within unstructured data, enabling the recognition of key text entities (nouns, verbs, and other language elements, along with specially named or formatted entities) and the relationships between those entities. After identifying the important elements it contains, the unstructured data can then be analyzed to derive key insights. Annotation can be done through both rules based and machine learning based systems. In a rules based system, programming is typically used to define how things relate to each other. That process is easy to trace and debug but it generally requires programming and it gets difficult to maintain as the complexity of the data grows. This methodology works well when the entities and relationships can be defined with a fewer number of rules. Rules based systems also work well when starting with a small set of test data. Watson’s sophisticated tooling for rules-based annotation eliminates the need for most programming. If there are a lot of entities and the relationships are more complex, leveraging machine learning (ML) may be a better annotation approach. Machine Learning uses examples instead of explicit rules so annotation developers don't need to write code. ML has actually been around since the 1950's so it's not brand new but it uses inferences and statistical models to perform natural language analysis. While this process enables business subject matter experts and developers to work together in order to leverage examples to annotate the unstructured data, the disadvantages are that the logic it creates in the annotation process may be difficult to expose after training, making debugging harder, and it can require a fair amount of time from key business experts. Machine learning begins with a training expert creating a set of carefully selected examples called the "ground truth". The system processes the ground truth to derive the specific context it provides. Then the system is given a new set of test data to annotate. The training expert reviews the results, and provides corrective feedback. Another set of test data is then provided, and if needed, more corrective feedback is given to the system. After a number of iterations, the system gets smarter until it performs as required, signalling that the training is complete. Machine learning requires that the initial set of examples is large and diverse enough for the learning model to be able to identify entities and relationships. As all things change over time, this training cycle should be repeated periodically to keep the system's performance at an optimal level. Watson Knowledge Studio (WKS) is one of IBM's offerings that utilizes ML. It is cloud based during training, but after training, the results can be moved inside the enterprise to process unstructured data locally. It enables developers and subject matter experts to work in a collaborative fashion to create sophisticated annotation capability without writing code. WKS can create both rules based and machine learning models. Models created with WKS can be used with Watson Explorer and the Watson AlchemyLanguage and Watson Discovery APIs. Annotations from Watson Explorer and AlchemyLanguage and pre-defined dictionaries can be used to pre-annotate a WKS model. - Using supervised ML can yield high levels of precision, and can dramatically lower training time. In many cases, a combination of ML and rules-oriented approaches is the best and most accurate approach. Once the team is satisfied with the results of the annotation exercise the model is leveraged across the entire data set.