International Business Machines Corporation

Anthony W Eichenlaub

John Edward Petri

Adam Clark

Brian John Cragun

John Christian Unterholzner

Michelle A Schlicht

Cynthia M Murch

Jan M Nordland

Terrence Theodore Nixa

Embodiments herein include a NLP application used to coach a participant who violates a social norm during a conversation. For example, the NLP application can evaluate the textual representation of the conversation to determine if the participant is exhibiting a characteristic of autism or other medical disorder which violates a social norm, and if so, inform the participant. Once a characteristic of autism is identified, a coaching application may output text that informs the participant what particular characteristic he is exhibiting&#x2014;e.g., the participant is ignoring an attempt by another participant to change the topic of the conversation. In addition to providing notice, in one embodiment, the coaching application suggests a corrective action to the participant. For example, if the participant fails to provide an appropriate response to an emotional statement, the coaching action may suggest a sympathetic statement.

Coaching a participant in a conversation

A first question may be received. A first tag may be identified. The first tag may correspond to a first demographic trait. The first tag may be for use in providing a context for generating a first answer estimate to the first question. The first answer estimate may be generated using natural language processing and based on the first tag.

Demographic-based learning in a question answering system

Embodiments herein include a natural language computing system that provides a diagnosis for a participant in the conversation which indicates the likelihood that the participant exhibited a symptom of autism. To provide the diagnosis, the computing system includes a diagnosis system that performs a training process to generate a machine learning model which is then used to evaluate a textual representation of the conversation. For example, the diagnosis system may receive one or more examples of baseline conversations that exhibit symptoms of autisms and those that do not. The diagnosis system may annotate and the baseline conversations and identify features that are used to identify the symptoms of autism. The system generates a machine learning model that weights the features according to whether the identified features are, or are not, an indicator of autism.

Diagnosing autism spectrum disorder using natural language processing

A processor obtains a target knowledge graph that includes target nodes that represent concepts used within a target work and target edges between target nodes that represent links used within the target work to associate the concepts used therein with each other. The processor also obtains a background knowledge graph that includes background nodes that represent concepts used within a background work and background edges between background nodes that represent links used within the background work to associate the concepts used therein with each other. The processor compares a portion of the target knowledge graph to a portion of the background knowledge graph. Based on the comparison, the processor identifies a potential inconsistency between the background work and the target work.

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Rochester, MN, United States of America

Anthony W Eichenlaub