International Business Machines Corporation

Joseph Lindsey Sharpe, Iii

Rahul P Akolkar

Kristi A Farinelli

Stefan Van Der Stockt

Brian E Bissell

Manali Jairam Chanchlani

Sarah Lynch

Sihang B Fang

Srijith N Prabhu

Bruce Raymond Slawson

Xinyun Zhao

Stefan A G Van Der Stockt

Alexander Block

Jagan M R Vujjini

Sai Karthik Reddy Ginni

A method, system and computer-usable medium are disclosed for automating the generation of an incorrect answer to a question suitable for a multiple choice exam. An input corpus of human-readable text associated with a subject domain is provided to a question generation system, where it is processed to generate a set of question-answer (QA) pairs. The set of QA pairs is then processed with the corpus of input text to extract a set of input keywords and concepts. A concept dependency graph is then used to perform disambiguation operations on the set of input keywords and concepts, and the reference keywords and concepts it contains, to generate a set of distractor words. The resulting set of distractor words is then processed with the set of QA pairs to generate a set of multiple choice question-answers that include various distractor answers.

Automated distractor generation by performing disambiguation operations

Technology for determining an amount of time to wait to retry requests to a representational state transfer (REST) server system for a REST resource, where the time to wait is always chosen to be a prime number of time units (for example, slots, milliseconds). While currently conventional systems will sometimes use a prime number of time units to wait for a retry request, various embodiments of the present invention will always, and invariably, use a prime number of time units. The REST resource may be, for example, a REST application programming interface (API) that is requested by and delivered to a client system using hypertext transfer protocol (HTTP).

Service retry collision prevention using prime numbers

An approach is provided in which an information handling system analyzes a set of historical form data corresponding to a set of completed forms. The set of historical form data includes information indicating a historical order at which a set of fields were completed in the set of completed forms. Next, the information handling system determines, based on the set of historical form data, a current order at which to complete a set of incomplete fields included in a current form. In turn, the information handling system displays the current form and a user interface on a display that indicates the current order at which to complete the incomplete fields on the current form.

Dynamic field entry permutation sequence guidance based on historical data analysis

A prescriptive meeting resource recommendation engine automatically learns participant and resource preferences in the context of given meeting input data using natural language features, and automatically recommends all relevant participants and resources (teleconferences, web meetings, links, etc.) to the meeting creator. The engine uses a feature data store to associate historical persons and historical resources with various natural language features, e.g., chargrams. As the host enters text in an invitation template (such as in the title field), the engine extracts current natural language features and computes current participant scores and current resource scores based on the current natural language features. A 'forgetfulness' routine is applied to the feature data store to phase out the influence of stale data. After receiving user confirmation, the system takes appropriate action such as electronically sending invitations to the recommended participants and making reservations for the recommended resources.

Prescriptive meeting resource recommendation engine using calendar invite content

A method, system, and computer program product are provided for classifying spoken audio content with a cognitive audio classifier by applying a set of distorted audio resources through a set of speech-to-text models STTi (STT. . . STTn) to get a set of interference coherence scores based on the transcript for each speech-to-text model STTi, thereby generating a measured baseline Mi (M. . . Mn) and a practical baseline Pi (P. . . Pn) that is associated with a coherence matrix for the audio effects AEj (AE. . . AEk) that were used to generate the distorted audio resources, thereby generating training data for use in training a cognitive audio classifier which classifies input spoken audio content to measure a quality of detected vocabulary elements from the spoken audio content under the set of audio distortion effects for each speech-to-text model STTi.

Cognitive audio classifier

A system, computer program product, and method are provided to leverage a taxonomy service to format ground truth data. An artificial intelligence platform processes ground truth data, including identification of one or more applicable taxonomy tags. The identified tags are filtered and applied to the ground truth data, thereby constructing an output string that incorporates the ground truth data together with one or more of the identified tags, effectively transforming the ground truth data. Application of the transformed ground truth data is employed to accurately identify the source and/or meaning of the natural language, and in one embodiment, to produce a physical action or transformation of a physical hardware device.

Natural language processing and classification

A method, system and computer-usable medium are disclosed for using a context dependency graph to automate the generation of an incorrect answer to a question suitable for a multiple choice exam. A reference corpus is used to generate a concept dependency graph that contains reference keywords and concepts associated with the subject domain of an input corpus. Relationships between the reference keywords and concepts within the concept dependency graph are identified. Once identified, they are used to process a set of input keywords and concepts extracted from the input corpus, and the reference keywords and concepts, to generate a set of distractor words. The resulting set of distractor words is then processed with a set of QA pairs associated with the input corpus to generate a set of multiple choice question-answers that include various distractor answers.

Automated distractor generation by identifying relationships between reference keywords and concepts

A mechanism is provided in a data processing system for article summarization. The mechanism analyzes an article to identify entities and relationships within the article. The article is an item of unstructured content. The mechanism performs information augmentation based on the identified entities and relationships using one or more cognitive services to collect augmented information from a corpus of information. The mechanism generates one or more visualization components based on the identified entities and relationships and the augmented information. The mechanism presents a summarization comprising the one or more visualization components to a user.

Automated article summarization, visualization and analysis using cognitive services

Embodiments provide a computer implemented method in a data processing system including a processor and memory storing instructions, which are executed by the processor to cause the processor to implement the method for providing an annotation policy for annotating a corpus including a plurality of electronic documents. The method includes: annotating an occurrence of a first term with a class in an electronic document; recommending a new annotation policy based on at least one annotation for the occurrence of first term; and storing the new annotation policy in a storage device.

Annotation policies for annotation consistency

A system, computer program product, and method are provided to leverage a taxonomy service to format ground truth data. An artificial intelligence platform processes ground truth data, including identification of one or more applicable taxonomy tags. The identified tags are filtered and applied to the ground truth data, thereby constructing an output string that incorporates the ground truth data together with one or more of the identified tags, effectively transforming the ground truth data. Application of the transformed ground truth data is employed to accurately identify the source and/or meaning of the natural language, and in one embodiment, to product a physical action or transformation of a physical hardware device.

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Waxhaw, NC, United States of America

Joseph Lindsey Sharpe, Iii