International Business Machines Corporation

Hyundai Motor Company

Kia Corporation

Palani Sakthi

Charles Evan Beller

William G Dubyak

Kristen Maria Summers

Aaron K Baughman

Blaine H Dolph

Petr Novotny

Qi Zhang

Kamran R Khan

Carlos A Paez, Jr

Peter Kenneth Malkin

Nitin Gaur

Salman Abdul Baset

Linda M Boyer

Brenda Lynn Dietrich

Vijai Gandikota

Brian L Keith

Arnold Greenland

Sean M Fuoco

Yousuf Mohamed Ashparie

Joshua G Hong

Wesley T Chuang

Robert A Sheets

Chennakesavalu Govindasamy

Meilan Ding

William R Gee

Daniel W Bingham

Mohamed Nooman Ahmed

Donna N Eng Dillenberger

Andeep S Toor

A candidate document is received, for example, by a document filter. A determination is made based on the content of the candidate document, whether the candidate document is relevant to a document corpus. A determination is made based on the content of the candidate document, whether the candidate document is novel with respect to the document corpus. In response to determining that the candidate document is relevant to the document corpus and novel with respect to the document corpus, the candidate document is added to the document corpus to make at least a portion of the content of the candidate document available for a response to a search query.

Automated determination of document utility for a document corpus

An example operation may include one or more of receiving a request which identifies a data value, reading, from a distributed blockchain storage, one or more data other data values that are related to the identified data value and which are previously stored thereon, determining whether the identified data value satisfies one or more compliance attributes based on the one or more other data values, and generating an output based on the determination.

Compliance verification of connected data

Natural language processing is enhanced by linguistically extracting intelligence about a user. A history of user queries is analyzed by a natural language classifier to determine various user intents, and these intents are combined to form a user intent profile. The profile includes elements of sentiment, emotion and tone. The profile can be used in various ways including restricting access to documents in a collection, or refining a cognitive analysis of a query. For access restriction, a determination is made that the user intent is inconsistent with a document, and the user is denied access to the document. This determination involves a user intent score which is compared to a score of the document. For cognitive analysis, searching of reference documents is filtered by excluding documents based on the user intent. The searching includes a comparison of meta-data tags of the documents to the user intent.

Leveraging natural language processing to refine access control within collections

Embodiments are directed to interaction with an open-domain question and answer system by recognizing questions that are highly broad or abstract, and generating and processing a batch of questions expressing alternate, concrete instances of the more general, abstract question. Responses to all of the questions in the batch are considered as candidates, and the strongest general answers are returned. A weighted, ranked answer set, based on weighting individual concrete questions and scaling the answers proportional to the weight of the questions, is provided to the user. The approach, according to embodiments herein, addresses the need for responses to broad questions in which a response to any of a set of more concrete question instances may serve to answer the question.

Dispersed batch interaction with a question answering system

Batch interaction with a computerized question answering system can produce an answer that more closely relates to a user's information need. A batch of questions can be generated interactively, and provides a context for a first question received from a user. The batch of questions includes or more additional questions which have terms with a nonsynonymous semantic relation to a first term in the first question. A question answering system can process the batch of questions to determine candidate answers. An answer to the first question can be determined based, at least in part, on a combined ranking of the candidate answers.

Precision batch interaction with a question answering system

Dynamic semantic processing of text in a word processing application with engagement of question-answering system. A user provides a text stream to a computer system via an input source. The input text stream includes a first natural language statement. The system determines that the first natural language statement includes a fact-based component expressed in natural language form. The system identifies an initial span and an alternative span of the first natural language component, based on the determining. The system engages a question-answering (QA) system by providing the initial span and the alternative span of the first natural language component to the QA system, and by receiving, in response to the providing, a set of natural language results from the QA system. The system evaluates the initial span and the alternative span of the first natural language component based on the received natural language results.

Document preparation with argumentation support from a deep question answering system

Embodiments can provide a computer implemented method for harvesting training data for a training set for use by a system capable of answering questions, the system comprising a processor and a memory comprising instructions executed by the processor, the method comprising receiving, from a user, an input question; processing the input question and returning, to the user, a result set comprising one or more ranked hypotheses and one or more ranked evidence passages corresponding to the one or more ranked hypotheses; receiving, from the user, an indication that one of the one or more ranked hypotheses is to be designated a watched hypothesis; adding the input question and the watched hypothesis to a to-be-vetted question/answer (QA) pair set comprising one or more to-be-vetted QA pairs; vetting each of the one or more to-be-vetted QA pairs in the to-be-vetted QA pair set through a first-pass automatic vetting procedure; if a vetted QA pair passes the first-pass automatic vetting procedure, adding the vetted QA pair to the training set; and retraining one or more ranking models based upon the training set.

Harvesting question/answer training data from watched hypotheses in a deep QA system

Embodiments can provide a computer implemented method, in a data processing system comprising a processor and a memory comprising instructions which are executed by the processor to cause the processor to implement an enhanced corpus management system, the method comprising: identifying one or more functional domain categories; ingesting one or more incoming documents to form an open-domain corpus; for each functional domain category, identifying one or more representative documents to establish a seed sub-corpus; calculating a degree of fit score between each of the one or more incoming documents and the one or more established functional domain category seed sub-corpora; and assigning one or more of the incoming documents to one or more of the functional domain categories based upon the degree of fit score to create an enhanced corpus.

Corpus management by automatic categorization into functional domains to support faceted querying

Methods, computer program products and systems for generating at least one factual question from a set of seed questions and answer pairs. One method includes: obtaining at least one seed question and answer pair from the set of seed question and answer pairs; extracting a set of features associated with the at least one seed question and answer pair using at least one common analysis system (CAS) in a set of CASs and a specific knowledge base; generating a set of candidate questions from the extracted set of features using a logistic regression algorithm and the specific knowledge base; and ranking each candidate question relative to a remainder of candidate questions in the set of candidate questions based on the extracted set of features and the at least one seed question and answer pair.

Natural language question expansion and extraction

An example system may include a processor and memory, wherein the processor is configured to perform one or more of acquire a first biometric sample of a user, compare the first biometric sample of the user against a pre-recorded second biometric sample, in response to the match, access shared data of the user stored on a blockchain, generate a question based on the shared data, receive an answer to the question from the user, and authenticate the user based on the answer to the question and the first biometric sample.

Consented authentication

An example system may include a processor and memory, wherein the processor is configured to perform one or more of receive a first biometric sample of a user from a terminal, execute a smart contract to compare the first biometric sample against a pre-recorded second biometric sample stored on a blockchain, in response to the match, acquire shared data of the user from a communication service provider, execute a smart contract to generate a question based on the shared data, receive an answer to the question from the user and execute a smart contract to record the answer on the blockchain, and execute a smart contract to authenticate the user based on the answer to the question and the first biometric sample.

A computer-implemented method comprising: receiving, by a computing device, biometrics data of a user via a user device as part of a request to access a secure device; applying, by the computing device, a non-invertible function to the biometrics data to scramble the biometrics data; determining, by the computing device, whether the scrambled biometrics data matches a pre-registered version of the scrambled biometrics data; and providing, by the computing device, an authentication message to the secure device requesting authentication of the user based on determining that the scrambled biometrics data matches the pre-registered version of the scrambled biometrics data, wherein the providing the authentication message provides a user of the user device with access to the secure device.

Identity verification using biometric data and non-invertible functions via a blockchain

An embodiment of the invention may include a method, computer program product and system for generating follow-up questions based on machine learning utilizing a computing device. The embodiment may include receiving an input question from a user. The embodiment may include parsing the received input question to extract input question components. Parsing utilizes natural language processing techniques. The embodiment may include executing trained question component models to predict follow-up question components. The extracted input question components are utilized as inputs to the trained question component models. The embodiment may include combining the predicted follow-up question components to generate one or more follow-up questions. The embodiment may include returning the one or more follow-up questions to the user.

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Palatine, IL, United States of America

Palani Sakthi