International Business Machines Corporation

Sean Thomas Thatcher

Edward Graham Katz

John A Riendeau

Charles Evan Beller

Alexander Charles Tonetti

William G Dubyak

Julie T Yu

Sean L Bethard

Kristen Maria Summers

Christopher Phipps

A method for adjusting device system settings in response to analysis of displayed content. In some embodiments, a processor may extract content from contents displayed on a screen of a device. The processor may analyze features of the content. The processor may classify the content as belonging to a content class, based on analysis of the content features, utilizing a machine learning model. The processor may update the device system settings based on the content class.

Adjusting system settings based on displayed content

An embodiment for expanding a search query is provided. The embodiment may include receiving a stopping criterion for stopping a search. The embodiment may also include receiving an initial search query. The embodiment may further include submitting the initial search query to an information retrieval system. The embodiment may also include identifying enrichment terms from the retrieved initial set of documents. The embodiment may further include generating a subsequent search query that includes one or more enrichment terms from the retrieved initial set of documents. The embodiment may also include submitting the subsequent search query to the information retrieval system. The embodiment may further include determining whether the stopping criterion is met, and in response to determining the stopping criterion is not met, iterating identifying, generating, submitting steps until the stopping criterion is met.

Iterative query expansion for document discovery

A system, computer program product, and method are provided for use with an intelligent computer platform to identify intent and convert the intent to one or more physical actions. The aspect of converting intent includes receiving content, identifying potential variants, and statistically analyzing the variants with a confidence assessment. The variants are sorted based on a protocol associated with the confidence assessment. A variant from the sort is selected and applied to a physical device, which performs a physical action and an associated hardware transformation based on the variant.

Identifying intent in dialog data through variant assessment

A method, system, and computer-usable medium are disclosed for answering general background questions on a topic from documents with glossary sections, A set of documents with glossaries is received from which a set of terms and associated glossary entries are extracted, where each term has a corresponding glossary entry. Association is performed of related glossary entries. The associations is based on a similarity algorithm to form glossary clusters where each glossary cluster refers to one or more glossary entries. A query with query terms tailored to general information is received. The glossary clusters are ranked relevance to the query terms to form a ranked set. A set of glossary clusters meeting a high ranked threshold is selected and provided.

System and method for question answering with derived glossary clusters

Embodiments are provided that relate to a computer system, a computer program product, and a machine-implemented method for assessing a statement by an entity to a recipient group, particularly in a social media environment, for an inconsistency with a previous (historical) statement to the same recipient group, and notifying the entity of the inconsistency. In certain embodiments, an alternative statement is provided.

Assessment of inconsistent statements to a recipient group

A system and a computer program product are provided for evaluating question-answer pairs in an answer key by generating a predicted answer to a test question based on the answer key modification history for comparison matching against a generated answer that is generated in response to the test question, and then comparing the predicted answer and generated answer to determine an accuracy score match indication therebetween so as to present an indication that the answer key may have a problem if there is a match between the predicted answer and generated answer.

Exploiting answer key modification history for training a question and answering system

Embodiments relate to an intelligent computer platform to create a document specific error correction model for amending OCR values. An image of a document is received and OCR is applied to the received image. Text is extracted from at least one static content field and the extracted text is compared to stored text from known static content. Responsive to a deviation identified in the comparison, a document specific error correction model is created and leveraged to correct OCR output. The model generates one or more variants for the dynamic content field associated with the static content field having the identified deviation. The generated variant(s) is subject to processing and one of the variants is selected as amended document content. A new document version is created from the amendment, the new document version including corrected OCR output.

Optical character recognition error correction model

An approach is provided in which an information handling system analyzes a message tone vector corresponding to a message against a target tone vector corresponding to a recipient of the message. The message tone vector includes a set of message tone attributes and the target tone vector includes a set of target tone thresholds. The information handling system, in response to determining a difference between the message tone vector and the target tone vector, creates a variant message by substituting words in the message with one or more similar words based on the target tone vector. In turn, the information handling system sends the variant message to the recipient.

Cognitive system for declarative tone modification

A device includes a processor configured to, in response to determining that an input phrase includes a first term that is included in a term hierarchy, generate a second phrase by replacing the first term in the input phrase with a second term included in the term hierarchy. The processor is configured to determine that interactive response (IR) training data indicates that the input phrase is associated with a user intent indicator. The processor is configured to determine that user interaction data indicates that a first proportion of user phrases received by an IR system correspond to the user intent indicator. The processor is configured to update speech-to-text training data based on the input phrase and the second phrase so that a second proportion of training phrases of the speech-to-text training data correspond to the user intent indicator. The second proportion is based on the first proportion. A speech-to-text model is trained based on the speech-to-text training data.

Speech-to-text training data based on interactive response data

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 improved search query generation system, the method comprising inputting a natural language question; parsing the natural language question into a parse tree; identifying argument positions comprising one or more argument position terms; for each argument position: comparing a head term's discriminator score against a threshold discriminator score; and if the head term surpasses the threshold discriminator score, adding the head term as a required term to an improved search query; and outputting the improved search query.

System and method for generating improved search queries from natural language questions

The temporal stability of an answer from a deep question answering system is predicted using a natural language classifier. A training corpus is divided into time-ordered slices having uniform granularity. A series of candidate answers to a training question is generated based on the slices, and a temporal profile for the series is identified by associating candidate answers with respective temporal intervals. The temporal profile is translated to a temporal stability value (representing a time period) using a temporal stability model. The classifier is trained using such training questions correlated with respective temporal stability values. Thereafter, when a user submits a natural language query to the deep question answering system, the query is also applied to the classifier which determines its temporal stability. The temporal stability is presented to the user with the answer to give a sense of how long the answer can be deemed reliable.

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Stone Ridge, VA, United States of America

Sean Thomas Thatcher