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Cognitive Applications
DeepQA system architecture

This section gives an outline of questions-answering (QA) system that was created to allow Watson in order to participate in the Jeopardy! game. The implementation is called DeepQA. DeepQA is a software structure that allows for deep content analysis and reasoning based on evidence. It is a powerful tool that makes use of advanced natural processing of language (NLP) as well as data retrieval and reasoning as well as machine-learning. 

The fundamental premise behind the research method which led to DeepQA is that real intelligence will be born through the creation and integration of a variety of algorithms that look at data from different angles. The effectiveness of the Watson answering system for questions is due on the integration a range of artificial intelligence techniques.

The DeepQA design approach views the problem of answering questions automatically as an enormously parallel hypothesis-generation and evaluation process. DeepQA is an application that can generate an array of possibilities and in each case, it creates an amount of confidence through analysing, gathering and evaluating evidence that is based on data available. France Oil Companies Email Lists.

The fundamental computational principle that is backed through DeepQA architecture is that DeepQA architecture is summarized as follows: following statements:

Consider and explore various interpretations of the issue. Find a myriad of possible solutions or hypotheses.

Examine and collect a variety of competing evidence-based pathways that may either support or debunk those hypotheses.

DeepQA’s DeepQA architecture was developed in a manner that is adaptable and allows for the integration of various of technologies, such as machine learning and natural language processing reasoning, knowledge representation as well as other AI technologies.

Each part of the system is based on assumptions regarding what the question could mean and what the possible answer could be, and why it could be the correct answer. DeepQA is built in conjunction with an architecture called Unstructured Information Management Architecture (UIMA) which is designed to allow the interoperability and scaling-out of deep analytics. Page 32, Figure 3, shows how to build the DeepQA structure at its higher scale. France Oil Companies Email Lists. The remainder of this chapter offer more details on the various architecture roles.

The fundamental assumption behind the DeepQA structure is that answers and the evidence to support each answer are collected from both well-structured (knowledge base) and

Unstructured (text) information like in Figure 3. DeepQA analyzes hundreds of candidates (also known as hypotheses) and for each is able to generate

evidence through an extensive set of natural machine learning, language processing or reasoning techniques. These algorithms collect and weigh the evidence against both structured and unstructured content to decide the right conclusion with the highest degree of confidence.

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Cognitive Applications 

DeepQA system architecture

This section gives an outline of questions-answering (QA) system that was created to allow Watson in order to participate in the Jeopardy! game. The implementation is called DeepQA. DeepQA is a software structure that allows for deep content analysis and reasoning based on evidence. It is a powerful tool that makes use of advanced natural processing of language (NLP) as well as data retrieval and reasoning as well as machine-learning.

 The fundamental premise behind the research method which led to DeepQA is that real intelligence will be born through the creation and integration of a variety of algorithms that look at data from different angles. The effectiveness of the Watson answering system for questions is due on the integration a range of artificial intelligence techniques.

Cognitive Applications 

DeepQA system architecture

This section gives an outline of questions-answering (QA) system that was created to allow Watson in order to participate in the Jeopardy! game. The implementation is called DeepQA. DeepQA is a software structure that allows for deep content analysis and reasoning based on evidence. It is a powerful tool that makes use of advanced natural processing of language (NLP) as well as data retrieval and reasoning as well as machine-learning. France Oil Companies Email address. The fundamental premise behind the research method which led to DeepQA is that real intelligence will be born through the creation and integration of a variety of algorithms that look at data from different angles. The effectiveness of the Watson answering system for questions is due on the integration a range of artificial intelligence techniques.

The DeepQA design approach views the problem of answering questions automatically as an enormously parallel hypothesis-generation and evaluation process. DeepQA is an application that can generate an array of possibilities and in each case, it creates an amount of confidence through analysing, gathering and evaluating evidence that is based on data available.

The fundamental computational principle that is backed through DeepQA architecture is that DeepQA architecture is summarized as follows: following statements:

Consider and explore various interpretations of the issue. Find a myriad of possible solutions or hypotheses.

Examine and collect a variety of competing evidence-based pathways that may either support or debunk those hypotheses. France Oil Companies Email address. 

DeepQA’s DeepQA architecture was developed in a manner that is adaptable and allows for the integration of various of technologies, such as machine learning and natural language processing reasoning, knowledge representation as well as other AI technologies.

Each part of the system is based on assumptions regarding what the question could mean and what the possible answer could be, and why it could be the correct answer. DeepQA is built in conjunction with an architecture called Unstructured Information Management Architecture (UIMA) which is designed to allow the interoperability and scaling-out of deep analytics. Page 32, Figure 3, shows how to build the DeepQA structure at its higher scale. The remainder of this chapter offer more details on the various architecture roles.

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The fundamental assumption behind the DeepQA structure is that answers and the evidence to support each answer are collected from both well-structured (knowledge base) and

Unstructured (text) information like in Figure 3. DeepQA analyzes hundreds of candidates (also known as hypotheses) and for each is able to generate

evidence through an extensive set of natural machine learning, language processing or reasoning techniques. These algorithms collect and weigh the evidence against both structured and unstructured content to decide the right conclusion with the highest degree of confidence.

The unstructured data utilized for the Jeopardy! system comprised a variety of

Thesauri, encyclopedias, and dictionaries newswire literary works, text corpora created from the internet, Wikipedia, and so on. Databases as well as taxonomies and

ontologies, for example DBpedia.

When the candidate answers are gathered, DeepQA scores each of the answers and attempts to identify which answer is correct by examining other evidence sources that are also derived from structured and unstructured sources of data. To accomplish this it makes use of machine learning to assess the effect on each source of evidence in the need to give high level of confidence for each answer. France Oil Companies Email id lists.

Training is carried out making use of historical data supplied by previous Jeopardy! games.

Despite its simplicity it is a powerful tool “minimal” structure can be utilized to solve fact-based questions, which make up most Jeopardy! questions. The answers to factoid questions are based on the factual information regarding a particular entity. The questions themselves are a challenge in determining the exact question being asked, and which parts of the clues are pertinent to determine the answer.

The Minimum DeepQA pipeline (Figure 3-4) is able to receive a query as input, then returns an answer as well as a confidence score in the output, it also includes the following elements:

Analysis of the question Primarily searched

Generation of hypotheses

The final merging of evidence and hypotheses and scoring

Investigating the deep QA pipeline using an illustration

This section will explain all the parts of the minimal DeepQA pipeline. The best way to present DeepQA by demonstrating an example of the way Watson could be able to answer the Jeopardy! question.

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The first step is to look at the question. This involves breaking the question into parts of speech and determining the various roles words and phrases within the sentence play. This analysis aids in determining the type of question being asked, and also what the question is asking. 

The analysis of questions is usually an element of natural language processing of issue. The goal is to have a thorough understanding of the question terms of the people that are involved, their relationships to the question, the categories that could be used for the responses, and so on.

When the clue is given as input, the next step of answering it is to conduct an analysis of the question. In this phase, DeepQA employs a combination of natural processing (NLP) analytics, which include extracting keyphrases from information the identification of the type of answer lexical along with question classification. France Oil Companies Email id.

Keyphrase extraction offers a list of keywords that can be used in composing the query to be used for primary search. In this instance the keywords include: 1894 C.W. Post and was created.

Information extraction (IE) is the process of the identification of entities and relationships within the clue. It is carried out through a mix of closed and open domain IE technologies, including rules-based and statistical information extraction systems, as well as semantic parsing. Jeopardy! questions can be a QA open-domain problem, and the extraction method must be able to deal with all kinds and relationships. In this example the entity was of type date and a relation to be created was identified.

It is also important to determine the kind of answer DeepQA is expected to produce The”lexical answer type” (LAT). The term “LAT” can be described as a term in the clue which indicates the

The type of type of the. In this instance the LAT is Michigan city.

Furthermore, the query is further classified according to an array of categories that generally are a result of somewhat different pipelines required to resolve the question. In this case this case, the type of question is known as a factoid since the answer expected can be described as an entity. France Oil Companies Email id.

Primary search

This can be described as an information-retrieval kind of task. The primary goal of the search is to locate an array of sources that are derived from structured or unstructured information and which include the possible results. The outcome of the primary search will be a set of data and entities of both corpora and knowledge bases.

The primary search is conducted by two methods:

from Unstructured data.The search is essentially the search engine query which combines the keywords discovered through question analysis. It is the result a texts. DeepQA utilizes a blend of various search engines, like Apache Lucene and Indri. France Oil Companies Email id.

Structured data. The search is an SQL as well as SPARQL query that is mapped to organized knowledge databases (KBs) that return the list of entities along with their names. The query is transformed from natural language into an organized query, which matches the results of the study to the structure of KBs which are utilized.

In the course of the initial research, hundreds of sources can be discovered in documents as well as KBs. For the Jeopardy! settings there is an Average of fifty sources is taken for each question. These sources are then utilized to generate the possible answers.

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Hypothesis generation

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. France Oil Companies Email database.

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.

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.  France Oil Companies Email database. 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.

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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. France Oil Companies Email directory.

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 in

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. France Oil Companies Email directory. 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 in

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.

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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.

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. France Oil Companies Email details.

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 as

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. France Oil Companies Email details.

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

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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. Best France Oil Companies Email lists.

Why should we commercialize Watson

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. Best France Oil Companies Email lists.

The amount of information that is available , and the potential to utilize it to go beyond traditional analytics

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.

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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. Biggest France Oil Companies Email lists.

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. Biggest France Oil Companies Email lists.

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).

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. Biggest France Oil Companies Email lists. 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.

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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.

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. Major France Oil Companies Email lists.

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. Major France Oil Companies Email 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

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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.

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. Major France Oil Companies Email address.

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. Major France Oil Companies Email address.

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. What’s needed is a way to scale the process

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. Watson Retrieve and Rank service is a

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 (Figure 4-22). It is important to note that the scoring and generation of evidence processes are removed from DeepQA.  Major France Oil Companies Email address. 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. Best France Oil Companies Email id database.

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).

Watson Discovery service

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. Best France Oil Companies Email id database.

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.

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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. Best France Oil Companies Email directory.

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. 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.  Best France Oil Companies Email directory.

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.

A supervised-learning algorithm studies the data from training and creates an inferred function that can be used to map new instances.

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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. 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. Best France Oil Companies Email details.

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. Best France Oil Companies Email details.  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).). 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. Best France Oil Companies Email details.

In addition, they can recognize names of entities that belong to the basic categories like company, person and place of residence, however they do not have the capability of recognizing particular distinctions, for instance, names of insurance companies, banks or their services. To become an expert in a specific subject in a specific field or area, some Watson services need to be taught. Because the majority of Watson services are built on a supervised learning method, there is a chance to train them by providing manually identified data.

For instance, to improve Watson understanding of financial texts an archive of news about financial markets can be taken in by Watson and experts in the field can have to mark instances of insurance companies, banks products, and their relationships.

Image recognition is also trained to recognize things such as company logos that are displayed in images. Natural Language Classifier is able to recognize financial news stories from blog articles. Machine translation is able to be trained to decrease the chance of error when translating financial news across different languages. The more data for training taken in, the greater the likelihood of accuracy for Watson software in understanding the entities and relationships of the type you want.

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Watson Conversation

Through IBM Watson Conversation, the IBM Watson Conversation service, you can develop your own application as well as user agent which can understand natural language inputs and interact with your users , mimicking the real-life human conversation. Conversation utilizes deep learning methods to reply to customers in a manner that mimics the interaction between human beings. With the rise and popularity of chatbots they can be used to create a Conversation service is able to be developed in one go and be accessible across a variety of chat platforms, including Facebook Messenger, Slack, Twitter Direct Messages (DMs) and many more.

It is essential to train the service to be able to discern that the words spoken by the user are genuine and to respond in a manner that is appropriate. This is accomplished by giving examples of how the user could express an intention for example for example, turning

Something is off, request directions to the train station, inquire about what the weather is going to be like in the coming days, and so on. Biggest France Oil Companies Email details.

Then, you teach the service to recognize the most important elements of input that determine the way it will be responding to user input. These entities, which are marked by the at symbol, (@), are the categories of words, and

words that affect phrases that influence the. They may include specific appliances that users may require control over, locations they may require directions for the location, etc.

Then, you can utilize dialog to allow the service to interact with users according to the intention and entities they have identified in their inquiries. With dialog nodes, you are able to tell the service to give straightforward answers when it senses certain motives, or to ask clarification questions when it’s missing crucial details, or to help users through more complex procedures using the sophisticated capabilities in the conversation service. Biggest France Oil Companies Email details.

One of the biggest challenges in creating a conversational interface is anticipating all possible ways that your users might attempt to connect via your chatbot. The Better

A component of the Conversation service gives you a history of user conversations. This history can be used to enhance your chatbot’s understanding of input from users.

These are the steps to follow to follow when making use of these steps for using the Conversation application and making it adaptable to the specific domain:

Create a Conversation Service instance.

Create a workspace within the Watson Conversation service instance. Biggest France Oil Companies Email details.

Learn the Conversation Service instance so that it can detect concepts that are present in inputs from users (intents or entities):

Learn to train the Conversation service with natural language examples of every possible intention. Five examples are needed for a minimum training. However, providing numerous examples will yield more precise outcomes, particularly in cases where they are diverse and reflect feedback from users.

The Conversation service with natural language examples from every possible entity. You can add the number of synonyms you can imagine the user to be able to utilize. The Improve interface lets you to fine-tune this process further, including additional synonyms as you try your dialog.

Create a flow of conversation that outlines the different stages of the dialog. Apply logic-based conditions to assess the terms outlined in the response of the user.

Try your dialog out using the embedded chat feature in your Conversation workspace. You can track exactly how Watson’s Watson Conversation service interprets the dialog, the intents and entities it recognizes, and then improve the training data it has in real-time.

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Watson Language Translator

Watson Language Translator service Watson Language Translator service translates texts from one language to another. It is available to any program that benefits from instantaneous, specific to the domain capabilities.

The following models of linguistics are available with Watson Language Translator: Watson Language Translator service:

News

The focus is on the news and transcriptions. You can translate English into and out of Arabic, Brazilian Portuguese, French, German, Italian, Japanese, and Spanish. It is also possible to translate Spanish into or from French. Biggest France Oil Companies Email database.

Conversational

Aimed at colloquialisms that are used in conversation. Translate English into and out of Arabic, Brazilian Portuguese, French, Italian, and Spanish.

Patents

The focus is on legal and technical terminology. Translation of Brazilian Portuguese, Chinese, and Spanish into English.

Language Translator service Language Translator service can be trained over time to offer greater accuracy when translating. To achieve this, it is necessary for the service to gain knowledge through previous translators. Watson Language Translator takes specific terms and phrases into consideration for example, names of products or people to ensure they are accurately translated.

Watson Language Translator service provides an opportunity to make the service capable of performing specific translations for a particular domain by altering the models that are already in place. Biggest France Oil Companies Email database.  It is a great way to improve the quality of existing models to include the context needed and increase their quality according to the context of each.

Perhaps, for instance, you’re creating an online translator for customer service and have terms specific to your company that you would like to be handled in a certain manner in conversations. You could also create an option for your engineers in one nation to search for patent information in a different language. Typically, you submit patents for a particular technology. You can make use of the data you have created to make an individual dictionary as well as the custom translation model that you can use in the Language Translator service.

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Both files should be encoded with UTF-8. Watson Language Translator service currently has three methods to input sources to modify the models of translation.

Forced glossary

Forced glossary is an assortment of phrases and terms with their translations to the language of the target. Forced glossaries substitute the current terms using their translations from an input file. They are utilized within the TMX format and are compatible with Language Translator. Language Translator Service.

Parallel corpus

Parallel corpus can be used in various applications other than Language Translator, including building the translation model entirely from scratch. As part of Language Translator service, it includes words or phrases that act as alternative suggestions for translation that you would like your translation company to think about. It is utilized to improve the model provided to include terms and contexts of phrases that may not be in the original model. Biggest France Oil Companies Email directory.

Contrary to the forced dictionary, these parallel corpuses can be utilized to train existing models, adding words and phrases of the input file into the existing training data , rather than replacing it. They are not able to alter the domain data in the initial file.

The parallel corpus can also be utilized to create the TMX format along with Language Translator.

To be able to train a custom model for training, a parallel corpus must include at least five thousand terms as well as translation pair. Biggest France Oil Companies Email directory.

Monolingual corpus

Monolingual Corpus is a plain text file encoded with UTF-8 which contains the body of text in the language you want to translate and is related to what you’re translating.

A monolingual corpus is an instance of language that the service can analyze and then use to enhance the quality of translation overall for instance, in order to improve it so that it is more human natural, fluid, and natural. To be able to train a customized model, a single-language corpus document should contain at least 1,000 words.

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Watson Natural Language Classifier

The Natural Language Classifier service applies cognitive computing methods to provide the best-matching classes that are predefined for short text inputs like phrases or sentences.

To make use of this Natural Language Classifier service in your application, you need to prepare the classifier using these steps:

Make training-related data ready

Train and create the classifier

Question the trained classifier

Review the results and make changes to the information

Make training-related data ready

To prepare the data for training To prepare the training data, follow these steps:

Labels for classes are easy to recognize.

Class labels are labels that express the intention in the text input. The class labels represent the result of an expertly trained classifier. Major France Oil Companies Email directory.

Collect an appropriate text.

Find the appropriate texts of each class’s label for purposes of training. These texts will provide examples of the classifier for each class, and serve as training information. These examples should be identical to the input of the text which will be supplied to the classifier during production.

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Combine classes with the text.

The training information is created by comparing text to their classes. In order to train your classifier you create the training CSV file to be utilized when the classifier is developed.

Train and create the classifier

Before you can build the classifier first, it is necessary to create a Natural Language Classifier service instance must be set up. After you have created an instance of the Natural Language Classifier service instance you can create a classifier that is connected to that service’s instances. Enter the name of the classifier, along with the training CSV file, and after that add the learning CSV file.

This is known as bootstrap classification. This classification (Figure 5 and 8) is able to be verified through subject matter specialists (SMEs) to determine its accuracy using additional data, referred to as testing data. They can also assist in, if required, rectifying classification errors. Major France Oil Companies Email address.

This step is extremely dependent on the quality of training data gathered from the prepare data stage and is continuously enhanced based on the accuracy target and other data sets.

Question the trained classifier

Once the classifier has been taught, you can ask questions about it. The API provides a result with details about the classes in that classifier, which has most confidence. Other pairs of class-confidence pairs are listed in order of decreasing of confidence. The confidence number represents the percentage of confidence, and higher numbers indicate greater confidence.

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Review the results and make changes to the information

The initial method of evaluating is by confirming the results with SMEs and adjusting the classification when accuracy isn’t aligned with the desired result. It is also possible to include feedback from customers, and provide an opportunity for customers to provide feedback on the results of classification. Figure 5-9 gives a general outline of the process.

The goal of this step within the method is increase the output of the classifier.

Find out if there is a weak or wrong confidence cases of input text.

Modify or transform user’s terms to create generic, representative text.

Match the text with their class label. Major France Oil Companies Email address details.

Incorporate new information to the training data from the beginning and create an entirely new classifier.

Repeat the process until the quality of classification decreases to a specific lower limit.

Watson Retrieve and Watson Retrieve and

The Retrieve and Rank services can bring important information out of a set of documents. The goal to use the Service is to assist you locate papers that’s more pertinent than the ones you find using standard methods of information retrieval. Major France Oil Companies Email address details.

The most frequent customers of Retrieve and Rank Retrieve and Rank services are professionals who deal with customers like support personnel and contact center representatives, or field technician. Examples of how to use Retrieve and Rank could be an experienced technician who can find solutions within the dense manuals of products as well as a help desk representative who can quickly find answers to speed up the time it takes to answer calls.

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This Retrieve and Rank service is available for immediate use, provided by IBM however it is also able to be customized to improve outcomes.

This Retrieve as well as the Rank service brings together two elements of information retrieval into one service powered by Apache Solr and a sophisticated machine-learning capability. 

Combination provides users with more relevant results through automatically reclassifying them with these machine-learning algorithms. Its Retrieve part is based upon Apache Solr. The Rank component makes use of machines learning methods and functions as the element that is developed by the user in order to perform adaption to a particular domain.

Do these steps:

Start the service Retrieve and Rank in the Bluemix instance in Bluemix.

Create a Solr cluster.

A Solr cluster will manage your collections of search results that you can make later. Major France Oil Companies Email address database.

Create a collection , and then add the documents you plan to look up.

An Solr collection is an digital index of the information within your documents. Collections are a method to separate data in the cloud. At this point you can create an account, link them with the configuration as well as upload and index your files.

Develop and train the ranker.

To show those documents that are most pertinent at top of your search results the Retrieve and Rank service makes use of the machine-learning component known as the ranker. It is possible to send your questions to the ranker who has been trained. The ranker is taught from previous the examples and can then sort results from queries it has not previously seen. In the aggregate, the results are known as ground truth.

There are a variety of ways to learn for the Retrieve and Rank services by hand by using APIs, semi-automatically by using scripts provided or an online user interface. Whatever method of training is utilized, the user must establish the base reality to guide the process of training. Major France Oil Companies Email address database.

Find some information.

While you wait for the ranker’s training, you are able to search for your documents. The search, which is based on it’s Retrieve part in the Retrieve and Rank service is not utilizing the machine-learning ranking features. It’s a normal Solr inquiry. The query will return the best 10 results that are relevant to you from Solr.

Rerun the results.

Once the ranker is trained after which you can query the ranker to check the results reranked since the ranker has been properly trained. The query will provide the results of your reranked search as JSON format. You can examine these results with the results you obtained with the basic search you performed in step 5.

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After you have analyzed the results that have been reranked You can further improve the results by repeating steps 4, 5 and 6. You may also add additional documents, as explained in step 3 to expand the scope of your search. Continue the procedure until you’re 100% satisfied with the results. It could take several times of refinement and changing the ranking.

Watson Visual Recognition

Watson Visual Recognition Watson Visual Recognition service uses deep learning algorithms to analyse images for objects, scenes faces, faces, and any other types of content. The result contains keywords that give information about the information. An integrated set of classes provide extremely accurate results, without any needing any training.

You can also create and train an individual classifier. With a custom classifier you can teach to use the Visual Recognition service to classify images according to the needs of your business. When you create an individual classifier, you can utilize this Visual Recognition service to recognize images that aren’t available with a pre-trained classification. Largest France Oil Companies Email lists.

Watson Visual Recognition Watson Visual Recognition service can learn from examples of images you upload to build the classifier. Each of the examples is then compared with the other files you upload as you build the classifier. The positive examples are saved as classes. These classes are combined together to form a single classifier, however they are returned with their individual scores.

A brand new classifier that you create can be taught by a number of compressible (.zip) file types, which include files with positive or negative pictures (.jpg as well as .png). It is required to provide at least two compressed files. These could be two examples of positive or negative files, or two negative examples. sample file.

Figure 5-13 illustrates an example of how to train images for a custom Visual Recognition classifier to recognize and classify breeds of dogs. The user can prepare ZIP files that include positive examples of dog breeds, such as Beagle, Golden Retriever, and Husky. Users can also create an archive of ZIP files that contain instances of negative animals which aren’t dogs, such as jaguars, cats, lions and others. Largest France Oil Companies Email lists.The compressed files that contain instances that are positive can help build classes that define what the classifier will be. The suffix you select for each positive example parameter will be used to define the class name in the new classifier. The suffix _positive_examples must be included. There is no limit to the amount of positive files that you are able to upload within a single phone call.

The compressed file that contains negative examples will not be used to establish a class in the classifier created however, it defines the characteristics of the classifier that it is not. Negative examples must contain images that do not represent the subject matter of any positive example. You are allowed to only specify one negative example file per single phone call.

Watson Text to Speech

Speech to Text Speech to Text Service transforms speech into text in accordance to the language the user has specified. It can transcribing speech from a variety of languages and audio formats into text with low latency. The service makes use of speech recognition for converting Arabic, English, Spanish, French, Brazilian Portuguese, Japanese, and Mandarin speech into text. Largest France Oil Companies Email lists.

Speech to Text Speech to Text service was created for a large general target audience. The vocabulary of the service is several words that are commonly commonplace in conversations. This model is able to provide precise recognition for a range of applications. However, it may not be aware of specific terms related to specific domains.

To tailor Speech to Text for a specific area, a new model for language is required to reflect the specificities of the domain, in terms of the word pronunciations and vocabularies.

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By using the language model customizable interface, you are able to increase your accuracy in speech recognition in areas like law, medicine as well as information technology and more.

Customization lets you extend and modify the vocabulary of the base model, incorporating specific domain-specific information and terminology. After you’ve provided details for your specific domain and then create an individual model of the language that is based on the information, you can make use of the model in conjunction with your application to offer custom speech recognition.

The most common model of working using Speech to Text customization includes the following steps:

Create a custom language model.

The POST/v1/customizations method to build a new custom model of a language. Largest France Oil Companies Email address.

Incorporate data from corpora into the model of a custom language.

The most popular method of adding information (domain-specific phrases) to a customized model is to add some or many corpora into the model.

Corpus are simple text document that employs terminology that comes from the domain in context. The service creates the vocabulary for a customized model by removing words from corpora that aren’t in the base vocabulary. You can add additional corpora to your custom model.

You use the POST /v1/customizations//corpora/ method to add a corpus to a custom model. Largest France Oil Companies Email address.

Watson Speaks Text

Watson Text to Speech is an API for speech synthesizers which transforms text written in a way that produces audible speech. It’s multilingual. Therefore, it takes input from text and outputs audio files in several languages. Text input can be text in plain font or written into Speech Synthesis Markup Language (SSML). In addition, it outputs a variety of speech styles, pronunciations of pitch, speech rate. The Voices feature can synthesize text into audio that is available in various languages that include English, French, German, Italian, Japanese, Spanish along with Brazilian Portuguese. The service has at minimum one female or male voice, and sometimes both in each language, as well as diverse dialects, like US as well as UK English as well as Castilian, Latin American, and North American Spanish. The audio utilizes a proper intonation and cadence.