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The aim of this process is to find possible answers to the query from the collection of documents returned by the initial search. The responses are referred to as candidates hypothesis or answers. In this stage it is the case that quantity overrules accuracy. This is why it’s crucial.
The method is to generate a vast amount (thousands) possibilities, so that recall is near 100% to ensure there is a correct solution found. DeepQA determines and supports the correct answer in the subsequent steps. The outcome of the initial search is analysed making use of data extraction (IE) algorithms that can recognize specific entities as well as other terms in the documents given in the first search.
The outcome of this step is an inventory of entities that have been extracted from knowledge bases and text. In both instances they are represented using strings (the entities’ names). For KBs and KBs, the strings contain links to their respective sources within the KB from which they were taken (for instance, URIs for the semantic web resource). All of these entities are considered as hypotheses that are competing (candidate solutions) to be assessed in the following stages within the DeepQA pipeline. Buy Clean Email Lists.
Candidates for answers are identified by analyzing the sources using various methods that include recognizing titles of Wikipedia articles and the anchor text of Wikipedia links within the content of documents as well as the running Named Entity Recognition and keyphrase extraction algorithms.
Answers to questions from candidates are displayed in strings, mainly noun phrases to answer factoid questions. Candidates’ answers will be linked with their source in the event that they are possible, like to their the DBpedia URIs. In this instance, these items are identified as: General Foods, 1985, Post Foods, Battle Creek as well as Grand Rapids. In real life many possible answers are generated. Each one is evaluated as part of the hypothesis and evidence scoring. Buy New email lists.
Evidence scoring and hypotheses
DeepQA does not just create an assortment of answers. it isn’t enough. In order to be successful at Jeopardy!, Watson must be able to choose the correct answer. To achieve this goal, DeepQA treats each candidate answer as a different hypothesis. This is why DeepQA analyzes the evidence for each candidate answer using a variety of strategies. Evidence can be found in the KB (such as examples of the compatibility between the different hypothesis types and the type of answer which is specified in the hint). Additional evidence can be found in textual passages from possible answers.
Once evidence has been gathered, DeepQA triggers a large variety of analyses, attempting to defend and support different theories from different viewpoints. To accomplish this, DeepQA uses various strategies. One of the main sources of evidence to prove the validity to an answer’s kind. So, DeepQA checks the compatibility between the type of each candidate and the type of answer which is required by the question. Another important aspect is the type of answer.
The evidence source is provided through the analysis of the collected textual texts to verify the possible connection with the clue. The analytics family is known as passage scorers. To be able to comprehend those passages DeepQA employs NLP technologies (such as information
extraction (entity linking, paraphrasing, entity linking and more) to use textual entailment analysis.
Different answer scoring algorithms evaluate the quality of the answers from different viewpoints. For instance, geographic reasoning was utilized to determine the distance between the candidate entity and the locations within the clue temporal reasoning is used to evaluate temporal compatibility and other such factors. DeepQA’s DeepQA research team created hundreds of possible answers which provide an array of features that machines learning programs to determine the general confidence of the answer. This is completed at the end of the process (final merging and the ranking).
The majority of answers are incorrect because in Jeopardy!, only one answer is correct. To determine which answer is correct candidates’ answers are subject to an extensive evaluation process that includes gathering additional evidence for each answer, or hypothesis and applying a range of deep scoring algorithms to assess the evidence.
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Additional evidence is derived directly from the source. Therefore, DeepQA performs a search engine search for each candidate against the same index utilized for the main lookup, searching for a text passages that contain both the candidate answer as well as certain terms from the clue.
Answer scoring algorithms establish the level of confidence that the evidence backs the answers of candidates. The DeepQA framework encourages and supports the inclusion of multiple answer scorers, who take into consideration various aspects of evidence and generate scores that reflect the extent to which the evidence supports a possible answer to a specific question. The scorers evaluate the question, candidate answer, and the evidence from different angles and assign scores to the elements which reflect the different perspectives.
In this example an extremely basic scoring system is the word overlap to show the number of keywords . Buy Clean mailing database.
commonality between the question and the evidence supporting the answer of each candidate. Another scorer will look for taxonomic connections between the answer of the candidate and the word-by-word
Enter the answer in a huge taxonomy of sorts. For instance what are General Foods of type Michigan City? It is not therefore the taxonomy score is zero. Another option in this instance is to look at the geographic closeness between the entities in the answer choices as well as Michigan as a state. Michigan. Battle Creek and Grand Rapids are both cities in Michigan therefore that’s why they’re included in these scenarios, it is possible to analyze the geographical proximity of the cities.
The score can be when the score is very high. The spatial analysis could be performed for certain entities, like companies and cities however, it is not the case for other like an event. Another option is to identify time-based
evidence. In this instance the answer to the question 1985 is far from the time specified as the answer (that is 1894) leading to a lower temporal score, whereas the date of foundation for General Foods is close, getting a high score for this particular feature.
One of the biggest difficulties with the hypothesis and scoring procedure is that it needs a massively large scale-out system that is parallel. In reality, in order to score hundreds of possible answers with hundreds of answer scorers each one relying on dozens documents supporting the answer, DeepQA must be able to perform millions of analyses simultaneously in just a few seconds. This is handled entirely with UIMA AS (Asynchronous Scaleout)1, which is an integration platform for semantics that lets you scale out on thousands of cores within an extremely parallel structure.
DeepQA blends the features in the final and utilizes them as feature vectors to represent every candidate. Buy Clean email listing Final merger and ranking.
The final stage of DeepQA is to grade each answer according to confidence. It is done by aggregating several evidence source and their analyses. This is achieved with machine learning methods using the historical records that contain clues and the answers. The majority of the data is available from more than 40 years of prior Jeopardy! matches, which provide millions of data points. Evidence scoring analytics give ample feature space to serve this goal. It’s not about learning the basics of. Through playing thousands of exercises, Watson learns how to analyze, apply, and blend their own algorithm to determine whether every piece of data is relevant or not.
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The scores for weighed evidence are then merged to determine the overall ranking for all possible answers. This is accomplished by developing an algorithm for logistic regression on the job of providing an accuracy score that is close to one (one) for positive answers as well as close to zero (zero) for all other candidates.
When confidence for every answer is calculated, the answers are ranked in accordance with and the best one is chosen if the confidence is higher than a game-specific threshold. The method used to play Watson in playing the Jeopardy! game is an important aspect of research. But, this book is not a study of that topic.
One of the most important aspects in the ranking process is similar answers are put together and the evidence supporting them is merged. This is referred to as final merging. Final merging can be described.
This is done by utilizing information from Knowledge Bases, for example specific semantic relationships or synonyms.
A regularized logistic regression method uses a regularized logistic regression algorithm to calculate every feature vector , and then assign one confidence score for the possible answer. It is trained with thousands of possible answers and question pairs, each of which is labeled to determine if it is right or not according to the question. This is done along and their features vectors and then learning to determine a likelihood of being an accurate answer. Buy Clean email id lists.
The historical data, which was compiled by thousands of clues was used to develop the algorithm that would be used in the Jeopardy! challenge. In the course of training the system takes what is the right answer (in this instance, Battle Creek) as an example positive and assigns the answer a weight of 1 (one). The other answers that are returned in the hypothesis generation process are believed to be negatives and are given an amount of zero (zero). Training is conducted with thousands of question-answer pairs.
The generated models are able they can assign different amounts of weights various features, and then apply them to unknown question-answer pairs in order to determine their level of confidence. In this case the person with the highest certainty can be identified as Battle Creek, which then is the answer. Since its confidence is extremely high (0.85), Watson will likely hit the game buzzer and try to answer the question.
Changes From DeepQA towards Watson Developer Cloud.
Watson Developer Cloud offers a variety of cognitive services that are available as RESTful APIs. The APIs are provided via IBM Bluemix that is the platform as service (PaaS) service offered by IBM.
This chapter traces the development in the Watson Developer Cloud, from the very beginning DeepQA architecture, through certain of the initial products such as Watson Engagement Advisor as well as Watson Oncology Advisor to the range of APIs currently available through IBM Bluemix. The focus is on discussing the lessons that were gained from the implementation of Watson technology in applications, and how these experiences influenced the IBM strategy for the product.
What made IBM chose to make commercial use of Watson and to make cognitive computing a key strategic element of its business are discussed within this section.
A brief overview of the DeepQA architecture is provided but more details are included in Chapter 3 “Introduction to the question-answering system” on page 29.
This chapter examines the most important capabilities that needed been developed in order to enable question-answering systems in real-world scenarios (and what the DeepQA architecture needed to change). Additionally, this chapter reviews the current Watson Conversation and Discovery services design and also a reference model to implement these solutions in the production environment. Buy Clean email id lists.
Around the same time Watson was playing in the Jeopardy! game show technological world was being impacted by three forces fundamental to technology (Figure 4-1).):
The advances in machine learning technology has opened new opportunities for applications in predictive analytics Natural processing of language (NLP) speech recognition, as well as computer vision.
The capability to be provided through APIs that are hosted on cloud platforms dramatically reducing the time-to-value of cognitive computing
The amount of information that is available , and the potential to utilize it to go beyond traditional analytics. Buy Clean email id lists.
Data is now a competitive advantage, but most of it is not visible to conventional computing platforms. Healthcare, each year is producing massive amounts of data when compared to previous years. Recognizing unstructured data in the form of images, text documents videos, or even raw sensor output offers an enormous amount of potential. Figure 4-2 illustrates the predictions for growth in data.
in 2014, IBM created Watson Group in 2014. Watson Group to market Watson technology. The work done by Oncologists at Memorial Sloan Kettering Cancer Centre was transformed into Watson Oncology Advisor. Watson Oncology Advisor. The efforts to arrange and maximize the value of text documents evolved into Watson Explorer as well as Watson Discovery Advisor.
The majority of companies are expected to succeed mostly on the customer experience. Unsatisfactory customer service results in customers leaving and, even more importantly it is that customers share their experiences on social media.
Young customers want self-service at their own terms, through the channels they prefer without the need to dial the standard 1-800 (toll-free) number. The market for virtual assistants is projected to reach $ (USD) one billion in the year 2018. The robotics for service and home automation market is worth billions of dollars. What will it be like to implement DeepQA in support of these applications?
Update of DeepQA architecture
In short, DeepQA generates and scores numerous hypotheses using an array of natural machine learning, language processing and reasoning algorithms that analyze and weigh evidence from both structured and unstructured structured content to arrive at the correct answer with the highest certainty.
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The main computational principle embraced DeepQA DeepQA is to accept and consider various interpretations of the question and to come up with a variety of plausible hypotheses or solutions and collect and analyze a variety of evidence pathways that could either be able to support or disprove those hypotheses.
In the step of question analysis Parsing algorithms break down questions into their grammatical elements. Other algorithms used in this step determine and label particular semantic entities, for example
names, places or dates, names, or places. Particularly, the type of item being requested when it is mentioned by any means it will be clearly determined. This is known as lexical answer kind (LAT) and it refers to the term used in the question.
that will indicate the type of answer you are looking for.
In the step of hypothesis generation, DeepQA does a variety of broad searches to determine the most likely of the possible possibilities of interpreting the question. These searches are made using an unstructured database (natural documents in the language) as well as structured information (available databases). Buy Clean emails.
as well as knowledge databases) and knowledge bases) that are fed and knowledge bases) that are fed to Watson and knowledge bases) that Watson receives during and knowledge bases) that Watson receives during. The emphasis on the present is to create the broadest set of hypotheses. These, for this purpose, are referred to as candidates answers.
In the stage of hypothesis and evidence scoring the candidates’ answers are scored before any additional evidence , using more thorough analysis algorithms.
In the process of merging and ranking the numerous possible answers are scored using numerous algorithms that produce many feature scores. Models that have been trained to evaluate the relative value of these features scores. The models are trained using algorithms for machine learning to determine using past performance, how to integrate all of these scores to create definitive, single-confidence numbers every candidate’s answer and determine the final ranking for all applicants. The answer that has the highest certainty is Watson’s final answer.
The evolution towards Watson Developer Cloud
How has the initial DeepQA design, which was originally intended in order to run the Jeopardy! game, evolved to function within the Customer Service area? Consider the following problem (Figure 4 and 6)). Jeopardy! is an open-domain factoid-based question-answering issue, Customer Service is a closed-domain problem that can be solved by multiple classes of questions which include Factoid Descriptive No/Yes, Procedural How To as well as Procedural Troubleshooting.
The initial insight is that the distribution of questions in a closed domain issue such as Customer Service differs from that of Jeopardy!, which makes the task of tackling multiple classes of questions the most tenable challenge.
The Jeopardy! case, there was no effort made to anticipate the questions or create databases of answers.
For 13% of tested questions, the results did not provide any clear clues to the nature of the answer, and the students have to rely on the context in order to decide the type of answer needed.
8.7% are what you can see in the graph shown in Page 47, Figure 4. It shows what’s called a the long tail distribution. The topics are sufficient to be able to concentrate on that is able to cover enough. Buy Clean emails.
ground. By focusing on the most frequently used areas (the left and right towards right) will only cover 10% information.
A myriad of subjects including hats, insects, writers,, to fruits and more, are all a fair game. In the case of these thousands of topics there are thousands of questions that could be asked, and later asked in a myriad of ways.
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Existing structured knowledge that is stored in database (DBs) also known as knowledge bases (KBs) can be used to to bridge the gap between meaning and interpretation of multiple natural languages (NL) documents. But, due to the wide scope of the domain and the expressive language that is used in questions as well as in content and in content, pre-built DBs aren’t suited for providing answers to a large number of questions. Instead, the emphasis is on NL understanding.
For the Customer Service domain In the Customer Service domain, you will see a longer and more slender head (Figure 4-8). A typical distribution may include 12 types of questions that represent 80percent of the questions. The types can be varied.
From chats (greetings as well as other social activities) from chit-chat (greetings and other social activities) to commonly asked from chit-chat (greetings and other social interactions) to frequently asked. Actually, they aren’t all questions and they are often referred to as expressions. Customers expect high precision.
(100 percent in the majority of cases) for dealing with shorter head. So, knowledge that maps to the pre-identified utterances is essential. Buy Clean lists
Therefore, if the speech is detected as a response, it should be retrieved from a structured database and delivered for the individual (Figure 4.7-9).
The evolution of questions analysis
This article discusses the development of the analysis of questions.
When playing Jeopardy!, which is mostly a factoid problem for answering questions the type of lexical answer (LAT) can be found within 87% of the questions. For reference that the word “LAT” is a specific word within the question that defines the type of answer. Within the Customer Service domain, having an exact word in the sentence which represents the type is uncommon.
Another important aspect of inquiry analysis that is relevant to the Customer Service domain is customer
emotions (Figure emotion (Figure 4-14, on the page). An excellent customer service representative is able to feel with the client; a virtual agent should be able to do the same. Watson Tone Analyzer is a Watson Tone Analyzer service
Utilizing linguistic analysis, it can detect three types of tones that can be found in texts: emotional as well as social tendencies and the writing style. Tone Analyzer Tone Analyzer service can be utilized to analyze the what is going on in conversations and communication to ensure that the response is in an the right way. Buy Clean email database.
In the end, user’s context is crucial to triggering the right responses (Figure 4.15). Bill Smith who is a “gold” member who communicates using a mobile device is expected to receive a customized response. The system should keep that context throughout every phase of interaction , and even across multiple interactions. The implication of this requirement is that a one-turn interaction is usually not enough. The system needs to be able to provide the needed information, guide the user through a set of steps or both.
Watson Dialog service Watson Dialog service was developed to allow developers to automate the process of branching conversations between users with the app. The Dialog service allowed applications to utilize natural language to respond to user inquiries such as cross-sell or up-sell, help users navigate procedures or applications or “hand-hold” users during difficult tasks. The Dialog service was able to track and store information about the user’s profile to gain more information about the person, then guide them through procedures in accordance with the user’s specific situation, or forward the information of the user to a back-end application to assist the user in taking actions and receive the assistance required.
The intent, the entities, the emotion and context are merged to determine the next step of the pipeline for answering questions (Figure 4-16 , on pages 51). In all the above scenarios, the right answer could be determined without the necessity of the generation of hypotheses answering scoring, answer scoring, or ranking, which will ensure excellent accuracy and satisfaction for the customer in the short portion of the distribution of questions.
Within the Customer Service domain, while the short head accounts for more spoken words than the distribution of the Jeopardy! clues The long tail remains an important component (Figure 4-8 ) on the 47th page). In most cases, the more difficult long tail queries are the most difficult for both the customers and support personnel.
Service documents, desk ticket and other articles could be available in a variety of formats, including PDF documents, Microsoft Word documents, web pages, and many more.
to convert them to a shared format as well as the capability to divide these documents into pertinent answers (Figure 4.20 to). The
In the end, as within the DeepQA pipeline, performing primary search and scoring and rank candidate answers is required (Figure 4-21). Watson Retrieve and Rank is a service that provides ranked answers.
Apache Solr cluster in the cloud that includes a custom query builder designed to work with natural language inquiries, a set feature scorers that evaluate the potential answer overlap between candidates and queries, and an algorithm based on machine learning which can be trained using questions from the particular area. Retrieve and Rank provides the solution to this long-tail. Buy Clean email database.
It is important to note that the scoring and generation of evidence processes are removed from DeepQA. Making cognitive systems clear and evidence-based is an essential objective of IBM especially in fields such as healthcare. For customer service the demands for speedy implementation to speed up time-to-value are greater than the potential for improvement in efficiency that evidence generation could bring. Like all things, there are tradeoffs when you use cognitive methods in your work.
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Microservices and robust tools evolved from DeepQA
Machine perception layer Decision making layer
Machine perception allows it to draw signals from the environment around it, revealing the vast majority of data that would be not visible to traditional computer systems. These signals are fed into the decision-making layer, which can be based on conventional business process logic or more sophisticated predictive analytics.
Following that, the next stage in the development of Watson Developer Cloud was to organize these machine perception capabilities to align them with key usage scenarios and to provide powerful tools to facilitate configuration and domain adaption all to reduce time-to-value for customers.
Watson Conversation service
The majority of this chapter is devoted to the use case of engagement which later evolved into the Watson Conversation service.
Watson Conversation allows you to build the prototype, test, and deploy the bot or virtual agent on mobile devices and messaging platforms like Slack or physical robots.
Conversation comes with a visual dialogue builder that allows you to create conversations that are natural between your users and apps, without any prior coding knowledge.
Watson Conversation orchestrates the capabilities that are represented through Natural Language Classifier, Natural Language Understanding and Dialog and displays them in one instrument. Additional capabilities, including Tone Analyzer, Speech to Text as well as Text to Speech integrations, are in the works for the near future (although developers are able to orchestrate these features themselves using the application layer).
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Watson Discovery service (Figure 4-26 on page 56) lets users gain value from unstructured data through converting, normalizing as well as enriching the data. With the help of an easier query language users can search through the data and use existing data sets that have been enhanced like those in the Discovery News collection. Discovery News primarily includes English news sources in the English language that are constantly updated and include more than 300,000 new blogs and articles being added each day in over 100,000 different sources.
Watson Discovery orchestrates the capabilities which are represented by Document Conversion Retrieve and Rank Natural Language Understanding, and others, making them available using a single tool.
Summary of Evolution
In bringing it all together, the purpose to achieve Watson Developer Cloud is to bring all of it together. Watson Developer Cloud is to offer the most flexible and robust platform to build intelligent applications that are deep in industrial domains (Figure 431). The microservices architecture allows developers to imagine a wide variety of applications that can be created by mixing and combining services. Conversation and Discovery offer clear guidelines for the most significant applications. Watson Knowledge Studio gives you the tools to show Watson the distinctive aspects of your field. Although the initial DeepQA technology and architecture have been modernized and improved but the “fingerprints” are available all over Watson Developer Cloud. Watson Developer Cloud. Buy Clean email listing.
The most important characteristic that cognitive systems possess is their capacity to adapt and learn in the course of time. Instead of being programmed in a specific way cognitive systems learn through how they interact with clients and from their experiences in their surroundings. Machine learning allows computers to have the ability to acquire knowledge and perform actions without being explicitly programmed. This means that computer models get better with time as it learns from its mistakes and gaining new experience (being presented with fresh information). Machine-learning models are developed by constructing them. the models are constructed using
Fixed source like an open source, for instance, Wikipedia and are applied to different or similar domains, such as for instance, the Travel domain. This is referred to by the term domain adaption.
In this chapter, we will introduce the idea of domain adaptation as well as the steps to be followed to adapt different Watson solutions to particular domains.
The chapter lists Watson services that are able to be taught, which is, they can be tailored to specific domains and the ones that aren’t. For Watson services that are able to learn, this section offers an outline of the procedure to follow in order to train each of the Watson services.
The chapter also provides an outline on Watson Knowledge Studio as well as other Watson services that needed models created using Watson Knowledge Studio to be adapted to a new field.
The introduction to the domain adaption
As with humans, our cognitive systems require to be taught to comprehend new domains of knowledge and to perform new tasks. For instance understanding medical records to detect medical conditions and prescriptions require a thorough understanding of the medicines and illnesses. To be able to do this human beings go to college, earn an MD degree and, after many years of education and research and becoming doctors.
In the same way, cognitive systems need to be taught for mastery in certain areas. Training is conducted by subject experts in the subject (SMEs) who provide supervision by humans and domain-specific bases that represent entities and relationships of interest to the newly created domain. The same process should be followed when applying Watson technologies to particular domains.
Domain adaptation comprises the essential steps to modify an open-domain system to a particular area (a closed-domain).
Supervised learning is one the primary types that machine-learning. The input data (also called training examples) has a label and the purpose of learning is being capable of predicting the label of new unpredictability of instances.
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A supervised-learning algorithm studies the data from training and creates an inferred function that can be used to map new instances.
The use of supervised learning is quite common when it comes to classification issues as the purpose is typically to help the system learn to recognize a classification system. For instance spam classifiers are an algorithm that analyzes the contents of an email subject line to determine whether an email received is spam or not. The spam classifier gets taught by using a labeled set of training which contains sufficient examples of legitimate and spam email. Buy Clean email leads. After training to a certain extent, the classifier must be able to differentiate genuine emails from spam. Another example of input data could include transactions from the past for all bank customers. Transactions are classified as legitimate or fraudulent when they are processed. The aim of the process is to determine for each new transaction whether it’s fraudulent or not.
Another approach to adapt Watson to new areas is to incorporate knowledge bases. Knowledge bases (KB) is an information structure that contains the structured information that is used by computers to make inferences. The knowledge bases are utilized in various ways, from basic dictionary to first-order logic assertions. Knowledge bases and the supervised learning process complement each other. In general, the greater information is available is, the less training required for the system to perform well in a particular task. The right mix of knowledge is essential to an efficient domain adaption process.
The process of adapting a cognitive system to a closed-domain system requires an iterative procedure that includes continual improvements to boost the efficiency of the program. The goal of this process is to achieve an increasing accuracy in performing tasks like introducing new functions and conducting tests on the software, identifying ways to enhance the system’s performance, conducting headroom analysis and looking for solutions to the most frequently occurring mistakes. The process involves the collaboration of experts from the domain as well as data scientists, natural processing (NLP) experts and machine learning developers.
IBM Watson Developer Cloud and domain adaptation
IBM Watson Developer Cloud is an online marketplace hosted by IBM Watson where service providers across all size and sectors are able to access the resources needed to create applications that are powered by Watson services. Developers are able to combine Watson service (and other services offered by IBM Bluemix) together with other logic in order to build applications that are cognitive (Figure 5-1).). Buy Clean email leads. Watson Developer Cloud offers the developer toolkit, education tools, and the ability to access Watson APIs. This lets IBM Watson technology available as an application development platform in the cloud, enabling an international community of software applications to create a new generation of applications that incorporate Watson cognitive computing.