Modifying a set of instructions based on bootstrapped knowledge acquisition from a limited knowledge domain

    公开(公告)号:US10726338B2

    公开(公告)日:2020-07-28

    申请号:US15349119

    申请日:2016-11-11

    摘要: Mechanisms for automatically modifying a set of instructions based on an expanded domain specific knowledge base are provided. The mechanisms generate a domain specific knowledge base comprising a set of entities and corresponding domain specific attributes and expand the domain specific knowledge base to include values for the domain specific attributes through an automated bootstrap learning process that performs natural language processing and analysis of natural language content using a set of pre-condition annotated action terms. The mechanisms receive electronic content comprising an initial set of instructions to perform an operation and evaluate the initial set of instructions based on the expanded domain specific knowledge base to identify a missing instruction. The mechanisms modify the initial set of instructions to include an additional instruction based on the missing instruction and thereby generate a modified set of instructions.

    Evaluating user responses based on bootstrapped knowledge acquisition from a limited knowledge domain

    公开(公告)号:US10832591B2

    公开(公告)日:2020-11-10

    申请号:US16266676

    申请日:2019-02-04

    摘要: Mechanisms for training a human user to perform an operation and provided. The mechanisms generate a domain specific knowledge base comprising a set of entities and corresponding domain specific attributes and expand the domain specific knowledge base to include values for the domain specific attributes through an automated bootstrap learning process that performs natural language processing and analysis of natural language content using a set of pre-condition annotated action terms, thereby generating an expanded domain specific knowledge base. The mechanisms evaluate an input from another device identifying an action associated with an entity in the set of entities, based on a retrieved domain specific attribute value and the retrieved pre-condition annotation from the expanded domain specific knowledge base. The mechanisms output a notification to a user computing device indicating whether the input is correct or incorrect to thereby train a user associated with the user computing device.

    Evaluating User Responses Based on Bootstrapped Knowledge Acquisition from a Limited Knowledge Domain

    公开(公告)号:US20190180643A1

    公开(公告)日:2019-06-13

    申请号:US16266676

    申请日:2019-02-04

    摘要: Mechanisms for training a human user to perform an operation and provided. The mechanisms generate a domain specific knowledge base comprising a set of entities and corresponding domain specific attributes and expand the domain specific knowledge base to include values for the domain specific attributes through an automated bootstrap learning process that performs natural language processing and analysis of natural language content using a set of pre-condition annotated action terms, thereby generating an expanded domain specific knowledge base. The mechanisms evaluate an input from another device identifying an action associated with an entity in the set of entities, based on a retrieved domain specific attribute value and the retrieved pre-condition annotation from the expanded domain specific knowledge base. The mechanisms output a notification to a user computing device indicating whether the input is correct or incorrect to thereby train a user associated with the user computing device.

    Generating ground truth for questions based on data found in structured resources

    公开(公告)号:US10482180B2

    公开(公告)日:2019-11-19

    申请号:US15816089

    申请日:2017-11-17

    摘要: Ground truth for a cognitive system is generated from a structured resource such as a table by identifying a subject of the structured resource and field headers. Linguistic analysis is performed on a given header to establish an interrogative context, and a question is generated relating to the subject based on the interrogative context, including an implementation of one or more mathematical operators. The question is generated using a question template, and has a question phrase based on the interrogative context, an operator phrase based on the selected operator, and a keyword phrase based on the subject. An answer to the question is determined by carrying out a computation that applies the selected operator(s) to one or more of the data values, to form a question-and-answer pair that is added to the ground truth. A filtering step is preferably used to ensure that the question-and-answer pair is valid.

    GENERATING GROUND TRUTH FOR QUESTIONS BASED ON DATA FOUND IN STRUCTURED RESOURCES

    公开(公告)号:US20190155904A1

    公开(公告)日:2019-05-23

    申请号:US15816089

    申请日:2017-11-17

    IPC分类号: G06F17/27 G06F17/30 G06F17/24

    摘要: Ground truth for a cognitive system is generated from a structured resource such as a table by identifying a subject of the structured resource and field headers. Linguistic analysis is performed on a given header to establish an interrogative context, and a question is generated relating to the subject based on the interrogative context, including an implementation of one or more mathematical operators. The question is generated using a question template, and has a question phrase based on the interrogative context, an operator phrase based on the selected operator, and a keyword phrase based on the subject. An answer to the question is determined by carrying out a computation that applies the selected operator(s) to one or more of the data values, to form a question-and-answer pair that is added to the ground truth. A filtering step is preferably used to ensure that the question-and-answer pair is valid.

    Evaluating User Responses Based on Bootstrapped Knowledge Acquisition from a Limited Knowledge Domain

    公开(公告)号:US20180137775A1

    公开(公告)日:2018-05-17

    申请号:US15349057

    申请日:2016-11-11

    摘要: Mechanisms for training a human user to perform an operation and provided. The mechanisms generate a domain specific knowledge base comprising a set of entities and corresponding domain specific attributes and expand the domain specific knowledge base to include values for the domain specific attributes through an automated bootstrap learning process that performs natural language processing and analysis of natural language content using a set of pre-condition annotated action terms, thereby generating an expanded domain specific knowledge base. The mechanisms evaluate an input from another device identifying an action associated with an entity in the set of entities, based on a retrieved domain specific attribute value and the retrieved pre-condition annotation from the expanded domain specific knowledge base. The mechanisms output a notification to a user computing device indicating whether the input is correct or incorrect to thereby train a user associated with the user computing device.

    Modifying a Set of Instructions Based on Bootstrapped Knowledge Acquisition from a Limited Knowledge Domain

    公开(公告)号:US20180137420A1

    公开(公告)日:2018-05-17

    申请号:US15349119

    申请日:2016-11-11

    IPC分类号: G06N5/02 G06F17/28

    摘要: Mechanisms for automatically modifying a set of instructions based on an expanded domain specific knowledge base is provided. The mechanisms generate a domain specific knowledge base comprising a set of entities and corresponding domain specific attributes and expand the domain specific knowledge base to include values for the domain specific attributes through an automated bootstrap learning process that performs natural language processing and analysis of natural language content using a set of pre-condition annotated action terms. The mechanisms receive electronic content comprising an initial set of instructions to perform an operation and evaluate the initial set of instructions based on the expanded domain specific knowledge base to identify a missing instruction. The mechanisms modify the initial set of instructions to include an additional instruction based on the missing instruction and thereby generate a modified set of instructions.

    Modifying a set of instructions based on bootstrapped knowledge acquisition from a limited knowledge domain

    公开(公告)号:US11556803B2

    公开(公告)日:2023-01-17

    申请号:US16839677

    申请日:2020-04-03

    摘要: Mechanisms for automatically modifying a set of instructions based on an expanded domain specific knowledge base is provided. The mechanisms generate a domain specific knowledge base comprising a set of entities and corresponding domain specific attributes and expand the domain specific knowledge base to include values for the domain specific attributes through an automated bootstrap learning process that performs natural language processing and analysis of natural language content using a set of pre-condition annotated action terms. The mechanisms receive electronic content comprising an initial set of instructions to perform an operation and evaluate the initial set of instructions based on the expanded domain specific knowledge base to identify a missing instruction. The mechanisms modify the initial set of instructions to include an additional instruction based on the missing instruction and thereby generate a modified set of instructions.

    Evaluating user responses based on bootstrapped knowledge acquisition from a limited knowledge domain

    公开(公告)号:US10217377B2

    公开(公告)日:2019-02-26

    申请号:US15349057

    申请日:2016-11-11

    摘要: Mechanisms for training a human user to perform an operation and provided. The mechanisms generate a domain specific knowledge base comprising a set of entities and corresponding domain specific attributes and expand the domain specific knowledge base to include values for the domain specific attributes through an automated bootstrap learning process that performs natural language processing and analysis of natural language content using a set of pre-condition annotated action terms, thereby generating an expanded domain specific knowledge base. The mechanisms evaluate an input from another device identifying an action associated with an entity in the set of entities, based on a retrieved domain specific attribute value and the retrieved pre-condition annotation from the expanded domain specific knowledge base. The mechanisms output a notification to a user computing device indicating whether the input is correct or incorrect to thereby train a user associated with the user computing device.