International Business Machines Corporation

John Martin Prager

Eric William Brown

Jennifer Chu-Carroll

David Angelo Ferrucci

Adam P Lally

James William Murdock, Iv

James W Murdock, Iv

Anthony Tom Levas

Benjamin P Segal

Anni Rosa Coden

Dragomir Radkov Radev

Donna M Lamberti

Timothy P Winkler

Radha Mohan De

James J Fan

Branimir Konstantinov Boguraev

Kenneth James Barker

Sugato Bagchi

Michael A Barborak

Sujoy Roy

Mark A Nappari

Valerie Samn

Valentina Salapura

Jonathan Lenchner

Mark N Wegman

Murthy V Devarakonda

Jeffrey Owen Kephart

Brian Paul Gaucher

Rong Nickle Chang

Dario Gil

David O S Melville

Yael Ravin

Roy Jefferson Byrd

Edward Cholchin So

Hamed A Ellozy

Tejaswini Pedapati

Keerthana Boloor

Jennifer J Liang

David L Gardner

A method for determining an application error from a screenshot includes receiving, by a computing device, a computer application screenshot image indicating a computer error has occurred. The computing device analyzes the computer application screenshot image using a machine learning image analysis to determine one or more error regions in the computer application screenshot image. The computing device further processes the analyzed computer application screenshot to extract text from the one or more error regions in the computer application screenshot image. The computing device determines the application error based upon the extracted text. The computing device further automatically applies an automated error fix based upon the determined application error.

Application error analysis from screenshot

A method modifies a social media post in response to determining that a hashtag for the social media post diverges from how the hashtag is used by other social media posts. A computing device receives a social media post, which contains a hashtag, and analyzes the social media post using natural language processing and a sentiment analysis to determine a sense and sentiment of the social media post. The computing device receives a plurality of reference social media posts, where each of the reference social media posts contains the hashtag, and utilizes the natural language processing and the sentiment analysis to determine a sense and sentiment of the reference social media posts. In response to determining that the sense and sentiment of the social media post do not match the sense and sentiment of the reference social media posts, the computing device causes the social media post to be altered.

Protecting a social media post with a hashtag from divergence

A mechanism is provided in a data processing system for exploring knowledge. The mechanism receives a set of known facts. The mechanism traverses paths in an ontology for a domain of knowledge from known facts in the set of known facts to one or more hypotheses. The ontology includes a plurality of entity types and a plurality of relationships between the entity types. The mechanism presents one or more hypotheses to a user.

Leveraging an external ontology for graph expansion in inference systems

A mechanism is provided in a data processing system comprising at least one processor and at least one memory comprising instructions, which are executed by the at least one processor and configure the processor to implement a patient information extractor. The patient information extractor receives a query specification for executing a query on a patient electronic medical record (EMR). The query specification provides parameters indicating a methodology for extracting search results from the patient EMR. The patient information extractor retrieves the patient EMR from a patient registry. The patient information extractor automatically executes the query specification on the retrieved patient EMR to thereby extract the search results from the patient EMR in accordance with the parameters of the query specification. The patient information extractor automatically processes the extracted search results to generate a patient indicator value. The patient indicator value represents an answer to a question about the patient. A patient evaluation operation is performed based on the patient indicator value.

Extracting patient information from an electronic medical record

A method, system and computer program product for generating answers to questions. In one embodiment, the method comprises receiving an input query, decomposing the input query into a plurality of different subqueries, and conducting a search in one or more data sources to identify at least one candidate answer to each of the subqueries. A ranking function is applied to each of the candidate answers to determine a ranking for each of these candidate answers; and for each of the subqueries, one of the candidate answers to the subquery is selected based on this ranking. A logical synthesis component is applied to synthesize a candidate answer for the input query from the selected the candidate answers to the subqueries. In one embodiment, the procedure applied by the logical synthesis component to synthesize the candidate answer for the input query is determined from the input query.

Providing answers to questions using logical synthesis of candidate answers

A mechanism is provided in a data processing system for using paraphrase metrics for answering questions. The mechanism receives an input question and generating a candidate answer from a corpus of information. The candidate answer has a supporting passage from the corpus of information. The mechanism divides the input question into a first sequence of tokens and divides the supporting passage into a second sequence of tokens. The mechanism identifies a plurality of subsequences of tokens within the second sequence of tokens and applies a paraphrase metric to compare the first sequence of tokens to each of the plurality of subsequences of tokens to generate a plurality of paraphrase metric scores. The mechanism then determines a confidence score for the candidate answer based on a highest paraphrase metric score within the plurality of paraphrase metric scores.

Using paraphrase metrics for answering questions

Providing answers to questions. A LAT, a rankable criterion, and a probabilistic criterion are identified in a received query. A set of candidate answers to the question that are instances of the LAT identified in a first data source is generated. Based on the rankable criterion, the candidate answers are ranked. Each candidate answer is assigned a likelihood that the candidate answer satisfies the probabilistic criterion, based on a statistic of occurrences of terms related to the candidate answer and the probabilistic criterion in text passages from a second data source. One or more candidate answers are selected based on the rank and the likelihood of the candidate answers. In another aspect of the invention, a Boolean criterion is also identified in the query and the set of candidate answers is reduced by applying the Boolean criterion.

Providing answers to questions having both rankable and probabilistic components

Methods, systems, and devices provide semantically relevant information by analyzing an Electronic Medical Record (EMR) having structured data and unstructured data. In the analysis, a first set of medical concepts is identified from the unstructured data in the EMR, and a second set of medical concepts is identified from the structured data in the EMR. Relationships between medical concepts in the first set of medical concepts and the second set of medical concepts are automatically identified in a medical ontology by such methods, systems, and devices.

Deep semantic search of electronic medical records

A system for verifying factual assertions in natural language. Receiving a text input. Identifying, a verifiable factual statement in the text. Forming a query based on the verifiable factual assertion. Searching a corpus based on the query. Determining the veracity of verifiable factual assertion based on the search results.

Identification and verification of factual assertions in natural language

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Pomona, NY, United States of America

John Martin Prager