International Business Machines Corporation

Allen Ginsberg

Charles Evan Beller

Elinna Shek

Chengmin Ding

Edward Graham Katz

Alexander Charles Tonetti

Adam D Dumey

Method and apparatus improves the quality of responses from an automatic dialogue system by dynamically adjusting response thresholds. More particularly, the automatic dialogue system may dynamically determine response threshold values in response to user feedback. The response threshold values may be used to evaluate a confidence value. The confidence value may be assigned to or otherwise associated with an input class, or user intent. The system may automatically adjust the response threshold values to provide a better user experience as the amount of user-interaction with the system increases.

Dynamic adjustment of response thresholds in a dialogue system

According to one embodiment, a method, computer system, and computer program product for retraining a classifier-based automatic dialogue system with recorded user interactions is provided. The present invention may include receiving recorded interactions, where the interactions are between a user and an automatic dialogue system; determining, based on the recorded interactions, whether to pair a given input with one or more classes; pairing inputs with one or more classes; assessing the reliability of the paired inputs and classes; determining whether the reliable paired inputs and classes can be consistently mapped; and merging all consistently mapped reliable pairs with an initial training set.

Automatic identification of retraining data in a classifier-based dialogue system

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 a media artifact annotation system, the method comprising inputting one or more relationships; for each of the one or more relationships, extracting, through an entity argument extraction module, one or more entity arguments; constructing, through a media query construction module, a media query using the one or more entity arguments; submitting the media query to a media search corpus; receiving search results comprising one or more media artifacts from the media search corpus; passing, through an annotation module, the search results to an annotator; receiving, through a response input module, one or more responses regarding each of the one or more media artifacts from the annotator; if the response for the media artifact is confirmatory, applying an annotation to the media artifact; and if the response for the media artifact is rejection, applying no annotation to the media artifact.

System and method for rapid annotation of media artifacts with relationship-level semantic content

A system, computer program product, and method are provided to automate a natural language processing system to facilitate an artificial intelligence platform defining a relationship between dialogue and post dialogue activity. Dialogue is detected and analyzed, including identification of key words and phrases within the dialogue. Post dialogue actions, including physical actuation of a hardware device and an associated temporal proximity of the action and the dialogue, are monitored. The hardware device receives an instruction from a processing unit that relates to the analyzed dialogue and the hardware device changes states and/or actuates another hardware device. The system constructs a hypothesis, i.e., a relationship from the identified key phrase drawn from the analyzed dialogue and the monitored post action dialogue. A dialogue tree containing identified terms and associated post dialogue actions is dynamically modified with one or more new identified terms and the associated post dialogue actions.

Leveraging natural language processing

Natural language processing of raw text data for optimal sentence boundary placement. Raw text is extracted from a document and subject to cleaning. The extracted raw text is examined to identify preliminary sentence boundaries, which are used to identify potential sentences in the raw text. One or more potential sentences are assigned a well-formedness score. A value of the score correlates to whether the potential sentence is a truncated/ill-formed sentence or a well-formed sentence. One or more preliminary sentence boundaries are optimized depending on the value of the score of the potential sentence(s). Accordingly, the processing herein is an optimization that creates a sentence boundary optimized output.

Processing of string inputs utilizing machine learning

According to one embodiment, a method, computer system, and computer program product for retraining a classifier-based automatic dialog system with recorded user interactions is provided. The present invention may include receiving recorded interactions, where the interactions are between a user and an automatic dialog system; determining, based on the recorded interactions, whether to pair a given input with one or more classes; pairing inputs with one or more classes; assessing the reliability of the paired inputs and classes; determining whether the reliable paired inputs and classes can be consistently mapped; and merging all consistently mapped reliable pairs with an initial training set.

Growing community of inventors

St. Petersburg, FL, United States of America

Allen Ginsberg