International Business Machines Corporation

Nuance Communications, Inc.

Salim E Roukos

Pavan Kapanipathi Bangalore

Dinesh Garg

Martin Franz

Nandana Sampath Mihindukulasooriya

Alexander Gray

Alfio Massimiliano Gliozzo

L Venkata Subramaniam

Radu Florian

Girija Yegnanarayanan

Jeffrey Scott Sorensen

Jennifer A Mallette

Gaetano Rossiello

Ibrahim Abdelaziz

John Ferdinand Pitrelli

Saswati Dana

Brian William Delaney

Imed Zitouni

Neal Snider

Dinesh Khandelwal

G P Shrivatsa Bhargav

Robert E Donovan

Anjali Shah

Srinivas Ravishankar

Scott McCarley

A computer-implemented method according to one embodiment includes identifying a natural language query; translating the natural language query into an intermediate representation; converting the intermediate representation into one or more query triples; and performing relation linking between each of the one or more query triples and a plurality of knowledge base triples.

Implementing relation linking for knowledge bases

Methods, systems, and computer program products for zero-shot entity linking based on symbolic information are provided herein. A computer-implemented method includes obtaining a knowledge graph comprising a set of entities and a training dataset comprising text samples for at least a subset of the entities in the knowledge graph; training a machine learning model to map an entity mention substring of a given sample of text to one corresponding entity in the set of entities, wherein the machine learning model is trained using a multi-task machine learning framework using symbolic information extracted from the knowledge graph; and mapping an entity mention substring of a new sample of text to one of the entities in the set using the trained machine learning model.

Zero-shot entity linking based on symbolic information

One or more computer processors parse a received natural language question into an abstract meaning representation (AMR) graph. The one or more computer processors enrich the AMR graph into an extended AMR graph. The one or more computer processors transform the extended AMR graph into a query graph utilizing a path-based approach, wherein the query graph is a directed edge-labeled graph. The one or more computer processors generate one or more answers to the natural language question through one or more queries created utilizing the query graph.

Knowledge base question answering

Generating error event descriptions by receiving a set of error messages associated with an error event, generating a tokenization of at least one line of the set of error messages, providing the tokenization to an attention head according to a context of the tokenization, providing an output of the attention head as input to a generative model, generating a description of the error event according to the output, and providing the description to a user.

Generating error event descriptions using context-specific attention

Cascaded models may be applied to extract facts from a medical text. A first model may be applied to at least a portion of the medical text. The first model extracts at least one first medical fact. The at least one first medical fact is linked to at least first text in the at least a portion of the medical text. A second model may be applied to the first text. The second model extracts at least one second fact that is an attribute of the at least one first medical fact.

Methods and apparatus for extracting facts from a medical text

In accordance with the present invention, a method for providing generation of speech includes the steps of providing input to be acoustically produced, comparing the input to training data or application specific splice files to identify one of words and word sequences corresponding to the input for constructing a phone sequence, using a search algorithm to identify a segment sequence to construct output speech according to the phone sequence and concatenating segments and modifying characteristics of the segments to be substantially equal to requested characteristics. Application specific data is advantageously used to make pertinent information available to synthesize both the phone sequence and the output speech. Also, described is a system for performing operations in accordance with the disclosure.

Phrase splicing and variable substitution using a trainable speech synthesizer

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Yorktown Heights, NY, United States of America

Salim E Roukos