Building Cognitive Applications With IBM Watson Services: Volume 7 .
Front cover Building Cognitive Applications with IBM Watson Services: Volume 7 Natural Language Understanding Sebastian Vergara Mohamed El-Khouly Mariam El Tantawi Shireesh Marla Lak Sri In partnership with IBM Skills Academy Program Redbooks
International Technical Support Organization Building Cognitive Applications with IBM Watson Services: Volume 7 Natural Language Understanding June 2017 SG24-8398-00
Note: Before using this information and the product it supports, read the information in “Notices” on page v. First Edition (June 2017) This edition applies to IBM Watson services in IBM Bluemix. Copyright International Business Machines Corporation 2017. All rights reserved. Note to U.S. Government Users Restricted Rights -- Use, duplication or disclosure restricted by GSA ADP Schedule Contract with IBM Corp.
Contents Notices . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .v Trademarks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vi Preface . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vii Authors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vii Now you can become a published author, too! . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ix Comments welcome. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ix Stay connected to IBM Redbooks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ix Chapter 1. Basics of Watson Natural Language Understanding service. . . . . . . . . . . . 1 1.1 Natural Language Understanding overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 1.1.1 How it works . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 1.1.2 Supported languages . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 1.1.3 Authentication . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 1.2 Service features . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 1.2.1 NLU Concepts. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 1.2.2 NLU Emotion. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 1.2.3 NLU Emotion (targets option) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20 1.2.4 NLU Entities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25 1.2.5 NLU Keywords . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30 1.2.6 NLU Relations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36 1.2.7 NLU Sentiment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41 1.2.8 NLU Categories . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49 1.2.9 NLU Language detection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54 1.3 Migrating from AlchemyLanguage to Natural Language Understanding. . . . . . . . . . . . 57 1.4 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57 Chapter 2. Creating a Natural Language Understanding service in Bluemix . . . . . . . 2.1 Requirements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2 Creating the NLU service instance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2.1 Creating the NLU service instance from the Bluemix website . . . . . . . . . . . . . . . 2.2.2 Creating the NLU service instance by using Cloud Foundry commands . . . . . . . 59 60 60 60 62 Chapter 3. Sentiment and personality analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.1 Getting started. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.1.1 Objectives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.1.2 Prerequisites . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.1.3 Expected results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2 Architecture . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.3 Two ways to deploy the application: Step-by-step and quick deploy . . . . . . . . . . . . . . 3.4 Step-by-step implementation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.4.1 Creating the Natural Language Understanding service instance . . . . . . . . . . . . . 3.4.2 Creating the Insights for Twitter service instance . . . . . . . . . . . . . . . . . . . . . . . . . 3.4.3 Creating the Personality Insights service instance . . . . . . . . . . . . . . . . . . . . . . . . 3.4.4 Coding the Node.js application . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.4.5 Pushing the application into Bluemix . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.4.6 Binding all the services to the Bluemix application (alternative option). . . . . . . . . 3.4.7 Testing the application . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65 66 66 66 67 68 69 69 69 70 73 75 79 80 85 Copyright IBM Corp. 2017. All rights reserved. iii
3.5 Quick deployment of application . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 87 3.6 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 91 Appendix A. Additional material . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 93 Locating the web material . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 93 Related publications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . IBM Redbooks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Online resources . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Help from IBM . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iv Building Cognitive Applications with IBM Watson Services: Volume 7 Natural Language Understanding 95 95 95 96
Notices This information was developed for products and services offered in the US. This material might be available from IBM in other languages. However, you may be required to own a copy of the product or product version in that language in order to access it. IBM may not offer the products, services, or features discussed in this document in other countries. Consult your local IBM representative for information on the products and services currently available in your area. Any reference to an IBM product, program, or service is not intended to state or imply that only that IBM product, program, or service may be used. Any functionally equivalent product, program, or service that does not infringe any IBM intellectual property right may be used instead. However, it is the user’s responsibility to evaluate and verify the operation of any non-IBM product, program, or service. IBM may have patents or pending patent applications covering subject matter described in this document. The furnishing of this document does not grant you any license to these patents. You can send license inquiries, in writing, to: IBM Director of Licensing, IBM Corporation, North Castle Drive, MD-NC119, Armonk, NY 10504-1785, US INTERNATIONAL BUSINESS MACHINES CORPORATION PROVIDES THIS PUBLICATION “AS IS” WITHOUT WARRANTY OF ANY KIND, EITHER EXPRESS OR IMPLIED, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF NON-INFRINGEMENT, MERCHANTABILITY OR FITNESS FOR A PARTICULAR PURPOSE. Some jurisdictions do not allow disclaimer of express or implied warranties in certain transactions, therefore, this statement may not apply to you. This information could include technical inaccuracies or typographical errors. Changes are periodically made to the information herein; these changes will be incorporated in new editions of the publication. IBM may make improvements and/or changes in the product(s) and/or the program(s) described in this publication at any time without notice. Any references in this information to non-IBM websites are provided for convenience only and do not in any manner serve as an endorsement of those websites. The materials at those websites are not part of the materials for this IBM product and use of those websites is at your own risk. IBM may use or distribute any of the information you provide in any way it believes appropriate without incurring any obligation to you. The performance data and client examples cited are presented for illustrative purposes only. Actual performance results may vary depending on specific configurations and operating conditions. Information concerning non-IBM products was obtained from the suppliers of those products, their published announcements or other publicly available sources. IBM has not tested those products and cannot confirm the accuracy of performance, compatibility or any other claims related to non-IBM products. Questions on the capabilities of non-IBM products should be addressed to the suppliers of those products. Statements regarding IBM’s future direction or intent are subject to change or withdrawal without notice, and represent goals and objectives only. This information contains examples of data and reports used in daily business operations. To illustrate them as completely as possible, the examples include the names of individuals, companies, brands, and products. All of these names are fictitious and any similarity to actual people or business enterprises is entirely coincidental. COPYRIGHT LICENSE: This information contains sample application programs in source language, which illustrate programming techniques on various operating platforms. You may copy, modify, and distribute these sample programs in any form without payment to IBM, for the purposes of developing, using, marketing or distributing application programs conforming to the application programming interface for the operating platform for which the sample programs are written. These examples have not been thoroughly tested under all conditions. IBM, therefore, cannot guarantee or imply reliability, serviceability, or function of these programs. The sample programs are provided “AS IS”, without warranty of any kind. IBM shall not be liable for any damages arising out of your use of the sample programs. Copyright IBM Corp. 2017. All rights reserved. v
Trademarks IBM, the IBM logo, and ibm.com are trademarks or registered trademarks of International Business Machines Corporation, registered in many jurisdictions worldwide. Other product and service names might be trademarks of IBM or other companies. A current list of IBM trademarks is available on the web at “Copyright and trademark information” at http://www.ibm.com/legal/copytrade.shtml The following terms are trademarks or registered trademarks of International Business Machines Corporation, and might also be trademarks or registered trademarks in other countries. AlchemyAPI Bluemix developerWorks Global Business Services IBM IBM MobileFirst IBM Watson IBM Watson IoT Redbooks Redbooks (logo) Redpapers SPSS Tivoli Watson Watson IoT The following terms are trademarks of other companies: Java, and all Java-based trademarks and logos are trademarks or registered trademarks of Oracle and/or its affiliates. Other company, product, or service names may be trademarks or service marks of others. vi Building Cognitive Applications with IBM Watson Services: Volume 7 Natural Language Understanding
Preface The Building Cognitive Applications with IBM Watson Services series is a seven-volume collection that introduces IBM Watson cognitive computing services. The series includes an overview of specific IBM Watson services with their associated architectures and simple code examples. Each volume describes how you can use and implement these services in your applications through practical use cases. The series includes the following volumes: Volume 1 Getting Started, SG24-8387 Volume 2 Conversation, SG24-8394 Volume 3 Visual Recognition, SG24-8393 Volume 4 Natural Language Classifier, SG24-8391 Volume 5 Language Translator, SG24-8392 Volume 6 Speech to Text and Text to Speech, SG24-8388 Volume 7 Natural Language Understanding, SG24-8398 Whether you are a beginner or an experienced developer, this collection provides the information you need to start your research on Watson services. If your goal is to become more familiar with Watson in relation to your current environment, or if you are evaluating cognitive computing, this collection can serve as a powerful learning tool. This IBM Redbooks publication, Volume 7, introduces the Watson Natural Language Understanding service. This service is a collection of text analysis functions that derive semantic information from your content. This book includes a basic description of several of the Natural Language Understanding service features and provides sample code snippets to demonstrate their use. This book includes an example of an application that integrates the Watson Natural Language Understanding service with the Watson Personality Insights and Insights for Twitter services to create a simple application to analyze Tweets from a Twitter handle. You can develop and deploy the sample applications by following along in a step-by-step approach and using provided code snippets. Alternatively, you can download an existing Git project to more quickly deploy the application. Authors This book was produced by a team of specialists from around the world, working in collaboration with the IBM International Technical Support Organization. Sebastian Vergara is an Expert Certified Architect in IBM Sales & Distribution, IBM Uruguay. His areas of expertise include cloud computing, DevOps, Design Thinking, and cognitive computing. He has over 8 years of experience in the IT industry. Sebastian led several projects to design and build cognitive solutions, such as the development of a transactional virtual assistant for an international bank and a cognitive chatbot for a major pharmaceutical company in Latin America that uses Watson Natural Language Classifier, Text to Speech, Natural Language Understanding, Visual Recognition, and other Watson technologies. Sebastian teaches at the Engineering College in the Universidad de la República Uruguay (UdelaR) where he introduces students to architecture and design, integration, cloud computing, and trending technologies. Copyright IBM Corp. 2017. All rights reserved. vii
Mohamed El-Khouly is a Certified IT Specialist in IBM Cloud Services, IBM Egypt. Mohamed has 15 years of experience in various roles in IT Services, including Software Development, Project Management, Testing, and Services Delivery. Mohamed currently focuses on cloud and analytics services, which include IBM Bluemix , IBM SPSS Modeler, IBM SPSS Statistics, and IBM Cognitive services. Mariam El Tantawi is a Certified IT Specialist in Actualizing IT Solutions, IBM Egypt. Mariam is a Senior Software Developer; her areas of expertise include cloud computing, predictive analytics, text analytics, and cognitive computing. She has over 4 years of experience in the IT industry. Mariam participated in and led several projects to design and build cognitive solutions, such as an application that helps graduates to find and explore career paths and career-related hiring entities and jobs, depending on their field of study. Mariam was part of the team that developed a question answering system for Dubai Road and Transport Authority (RTA). The solution enables users to ask questions related to the services provided by RTA in their native language. It integrates several Watson services, such as Conversation, Text to Speech, and Speech to Text. Mariam is a frequent speaker at IBM conferences and she teaches university courses sponsored by IBM Skills Academy Programs in the areas of predictive analytics and business analytics. Mariam holds two IBM patents. Shireesh Marla is a Mobile Solution Architect in IBM Global Business Services , India. His areas of expertise include IBM MobileFirst , Android, IBM Bluemix cloud development platform, IBM Watson Analytics, and Cognitive Computing. He has over 13 years of experience in the IT industry and has been involved in mobile application technologies and cloud computing technologies. Shireesh has been part of the team in building a bot for a digital satellite network entertainment company in South Africa. It uses IBM Watson Natural Language Classifier, Speech to Text, and Text to Speech APIs. Lak Sri currently serves as a Program Director in IBM developerWorks , part of the IBM Digital Business Group organization. Lak leads innovation in the developer activation space. He was the Technical Leader for the Building Cognitive Applications with IBM Watson Services Redbooks series. Lak led the development of the IBM Cloud Application Developer Certification program and the associated course. Earlier he worked as a Solution Architect for Enterprise Solutions in Fortune 500 companies using IBM Tivoli products. He also built strategic partnerships in education and IBM Watson IoT . Lak is an advocate and a mentor in several technology areas, and he volunteers in planning and supporting local community programs. The project that produced this publication was managed by Marcela Adan, IBM Redbooks Project Leader, ITSO. Thanks to the following people for their contributions to this project: Swin Voon Cheok Ecosystem Development (EcoD) Strategic Initiative, IBM Systems Juan Pablo Napoli Skills Academy Worldwide Leader, Global University Programs Teja Tummalapalli IBM Digital Business Group viii Building Cognitive Applications with IBM Watson Services: Volume 7 Natural Language Understanding
Now you can become a published author, too! Here’s an opportunity to spotlight your skills, grow your career, and become a published author—all at the same time! Join an ITSO residency project and help write a book in your area of expertise, while honing your experience using leading-edge technologies. Your efforts will help to increase product acceptance and customer satisfaction, as you expand your network of technical contacts and relationships. Residencies run from two to six weeks in length, and you can participate either in person or as a remote resident working from your home base. Find out more about the residency program, browse the residency index, and apply online at: ibm.com/redbooks/residencies.html Comments welcome Your comments are important to us! We want our books to be as helpful as possible. Send us your comments about this book or other IBM Redbooks publications in one of the following ways: Use the online Contact us review Redbooks form found at: ibm.com/redbooks Send your comments in an email to: redbooks@us.ibm.com Mail your comments to: IBM Corporation, International Technical Support Organization Dept. HYTD Mail Station P099 2455 South Road Poughkeepsie, NY 12601-5400 Stay connected to IBM Redbooks Find us on Facebook: http://www.facebook.com/IBMRedbooks Follow us on Twitter: http://twitter.com/ibmredbooks Look for us on LinkedIn: http://www.linkedin.com/groups?home &gid 2130806 Explore new Redbooks publications, residencies, and workshops with the IBM Redbooks weekly newsletter: e?OpenForm Stay current on recent Redbooks publications with RSS Feeds: http://www.redbooks.ibm.com/rss.html Preface ix
x Building Cognitive Applications with IBM Watson Services: Volume 7 Natural Language Understanding
1 Chapter 1. Basics of Watson Natural Language Understanding service This chapter introduces the IBM Watson Natural Language Understanding service. The Natural Language Understanding service is a collection of text analysis functions that derive semantic information from your content. You can input text, HTML, or a public URL, and leverage sophisticated natural language processing techniques to get a quick high-level understanding of your content and obtain detailed insights. This chapter provides basic information about how to use the service features, provides usage examples, and includes simple code examples in the following technologies: Node.js Java Node-RED Note: A runtime environment that is specific to each technology is required in order to run the snippets provided in this chapter. The purpose of the snippets is to serve as code examples for future reference. Therefore, documentation about the installation and configuration of the technology-specific runtime environment is not included. The following topics are covered in this chapter: Natural Language Understanding overview Service features Migrating from AlchemyLanguage to Natural Language Understanding References Copyright IBM Corp. 2017. All rights reserved. 1
1.1 Natural Language Understanding overview With Natural Language Understanding, developers can analyze semantic features of input text and extract metadata from content, such as categories, concepts, emotion, entities, keywords, metadata, relations, semantic roles, and sentiment. With custom annotation models developed using IBM Watson Knowledge Studio, you can further customize the service to identify domain-specific entities and relations in your content. Natural Language Understanding can be useful in many scenarios that demand rapid analysis of unstructured text without requiring in-depth natural language processing expertise. For example, you can monitor sentiment and emotion in customer support chat transcripts, or you can quickly categorize blog posts and sort them based on general concepts, keywords, and entities. 1.1.1 How it works Figure 1-1 shows a high-level flow of the Natural Language Understanding service. What it does You can input: And the service will output: Any publicly accessible URL Plain text or HTML content Extracted metadata in JSON format Figure 1-1 Natural Language Understanding high-level flow The flow is as follows: 1. You input the following types of text to be analyzed: – Any publicly accessible URL – Plain text or HTML content 2. The service will output this information: – Extracted metadata in JSON format 2 Building Cognitive Applications with IBM Watson Services: Volume 7 Natural Language Understanding
Figure 1-2 shows an overview of code snippets, in Java and Node.js, to call the API and process the response in these steps: 1. Initializes the Natural Language Understanding service instance, passing the credentials (username and password) and version. 2. Calls the /Analyze endpoint with the text, HTML, or a public URL to be analyzed; the Natural Language Understanding service features indicate the text analysis functions that the API should perform (the Keywords feature is shown in Figure 1-2 as an example). You can also specify the options for each feature. 3. Processes the API response returned in JSON format. The table at the bottom of Figure 1-2 shows the Natural Language Understanding (NLU) service features and the options for each feature. Java Node.js 1 NaturalLanguageUnderstanding service new tanding. VERSION DATE 2017 02 27 ,username,password); Initialize NLU service var natural language understanding new NaturalLanguageUnderstandingV1({ 'username' : your username here, 'password' : your password here, 'version date' : NaturalLanguageUnderstandingV1.VERSION DATE 2017 02 27 }); String url "www.ibm.com"; KeywordsOptions keywords new KeywordsOptions.Builder() .sentiment(true) .emotion(true) .limit(3) .build(); 2 Call Analyze with input parameters Specify as many features as needed Keywords feature limit 10 emotion : true sentiment :true } } nlu.analyze(parameters, function(error, response) Features features new Features.Builder() .keywords(keywords) .build(); AnalyzeOptions parameters new AnalyzeOptions.Builder() .url(url) .features(features) .build(); 3 Process API response in JSON format System.out.println(response); Categories var parameters { url : 'http://www.ibm.com', features : { keywords : { Concepts Emotion Entities Keywords limit targets emotion emotion document sentiment model Keywords feature options function(error, response) { if (error) { onError(error, response); } else { console.log(JSON.stringify(response, null, 2)); var keywords response.keywords; Metadata Relations Semantic Roles Sentiment model entities targets sentiment keywords document limit limit NLU features NLU features options limit Figure 1-2 Calling the NLU service and processing response Chapter 1. Basics of Watson Natural Language Understanding service 3
1.1.2 Supported languages Table 1-1 shows the features that are supported by each language. For updated information, see the Supported languages web page. Categories X X Xb X X X X Xb X X X X Xb X X X Xc X X X Xc X X X X X X X X X French German X Japanese Concepts X X Emotion X English Entitiesa Metadata X X Keywords Relationsa X Sentiment X Arabic Italian Semantic roles Table 1-1 Features supported by each language Xc X X X Xc X Portuguese X Russian X Spanish X Swedish Xb X X X Xc a. You can build Watson Knowledge Studio custom models for entities and relations in English, French, German, Italian, Portuguese, and Spanish. You can use some of these languages in Natural Language Understanding or you can customize the models. b. These languages are supported only through custom models in IBM Watson Knowledge Studio. c. These languages are supported in the public service, but not in Bluemix Dedicated. You can indicate the language to use for analysis with the ISO 639-1 code. This code overrides automatic language detection performed by the service. Valid codes are as follows: ar en fr de it ja pt ru es sv Arabic English French German Italian Japanese Portuguese Russian Spanish Swedish 1.1.3 Authentication You authenticate to the Natural Language Understanding service with Basic Authentication in each request. To get the username and password, you must create a service instance and retrieve the credentials. For information, see Chapter 2, “Creating a Natural Language Understanding service in Bluemix” on page 59. 4 Building Cognitive Applications with IBM Watson Services: Volume 7 Natural Language Understanding
1.2 Service features To use the Natural Language Understanding (NLU) service, send API requests to the Analyze endpoint with the input text, HTML, or a public URL, specify one or more of the supported service features, and specify the options for the features or accept the default options. This section includes a basic description of several NLU service features and provides sample code snippets to demonstrate their use. 1.2.1 NLU Concepts The NLU Concepts feature identifies high-level concepts that might not be directly referenced in the input text. Concept-related API functions understand how concepts relate. Concepts that are detected typically have an associated link to a DBpedia resource. See the following input and response examples. Input Text: Machine learning is the science of how computers make sense of data using algorithms and analytic models. Response Concepts tags: Computer Machine learning Artificial intelligence Computer science Alan Turing Scientific method Psychology Learning Use case example: Clustering articles Concepts tagging allows you to perform high-level analysis of the content. This feature can help you to cluster news articles based on concepts, and study or analyze articles associated with specific concepts. A use case might be the extraction of concepts from an online article by using, for example, the following URL as input to the API: http://www.bbc.com/news/technology-38595480 NLU Concepts flow Figure 1-3 on page 6 shows the basic flow: 1. Input (call the API with input parameters): Pass the NLU service instance credentials (username and password), for authentication, and URL to the news article to be analyzed. 2. Processing (analyze the input text with the Concepts feature). 3. Response (returns a response in JSON format): – Text: Name of the concept. – Relevance: Score for the concept in the range of 0 - 1. A score of 1 means the concept is highly relevant; 0 means it is not relevant. – dbpedia resource: Link to the DBpedia resource that is associated with the concept. Chapter 1. Basics of Watson Natural Language Understanding service 5
1. Input parameters 2. Processing 3. Response Response in JSON format INPUT "concepts": [ { {“ username ”, ” password ”, “http://www.bbc.com/news/technolog y-38595480” } Concepts feature "text": "Artificial intell
an overview of specific IBM Watson services with their associated architectures and simple code examples. Each volume describes how you can use and implement these services in . wh ich include IBM Bluemix , IBM SPSS Modeler, IBM SPSS Statistics, and IBM Cognitive services. Mariam El Tantawi is a Certified IT Specialist in Actua lizing IT .
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Product Analysis for IBM Lotus Domino, IBM Lotus Notes, IBM Lotus iNotes, IBM Lotus Foundations, IBM Lotus Quickr, IBM Lotus Sametime, IBM Lotus Connections, and IBM LotusLive. This report is intended for Organizations, Vendors, and Investors who need to make informed decisions about the Email and Collaboration market. Figure 1: Worldwide IBM .
This IBM Redpaper publication addresses IBM Patterns for Cognitive Systems topics to anyone developing, implementing, and using Cognitive Solutions on IBM Power Systems servers. Moreover, this publication provides documentation to transfer the knowledge to the sales and technical teams. This publication describes IBM Patterns for .
The Building Cognitive Applications with IBM Watson Services series is a seven-volume collection that introduces IBM Watson cognitive computing services. The series includes an overview of specific IBM Watson services with their associated architectures and simple code examples.
viii Building Cognitive Applications with IBM Watson Services: Volume 5 Language Translator Muhammad Zain Mustafa is an Advanced Analytics Consultant in IBM Global Business Services , IBM Pakistan. He has performed a variety of technical jobs in IBM. His vast experience helped Zain to build a solid understanding of the principles necessary to extract
viii Developing Node.js Applications on IBM Cloud Ahmed has authored several IBM Redbooks publications, including Building Cognitive Applications with IBM Watson Services: Volume 2 Conversation, SG24-8394, and Essentials of Cloud Application Development on IBM Bluemix, SG24-83742. Mohamed Ewies is a Certified Expert IT Specialist and IBM Certified Application Developer
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