Abstract: A method, computer program product, and computer system, for receiving a first set of ground truth instances from a first source. A second set of ground truth instances may be received from a second source. The first and second sets of ground truth instances may be weighed differently based on a level of trust associated with each of the first and second sources. The weighted first and second sets of ground truth instances may be applied in a machine learning task executed by a computer.

Abstract: A method, computer program product, and computer system, for receiving a first set of ground truth instances from a first source. A second set of ground truth instances may be received from a second source. The first and second sets of ground truth instances may be weighed differently based on a level of trust associated with each of the first and second sources. The weighted first and second sets of ground truth instances may be applied in a machine learning task executed by a computer.

Abstract: A method, computer program product, and computer system, for receiving a first set of ground truth instances from a first source. A second set of ground truth instances may be received from a second source. The first and second sets of ground truth instances may be weighed differently based on a level of trust associated with each of the first and second sources. The weighted first and second sets of ground truth instances may be applied in a machine learning task executed by a computer.

Abstract: A method, computer program product, and computer system, for receiving a first set of ground truth instances from a first source. A second set of ground truth instances may be received from a second source. The first and second sets of ground truth instances may be weighted differently based on a level of trust associated with each of the first and second sources. The weighted first and second sets of ground truth instances may be applied in a machine learning task executed by a computer.

Abstract: A mechanism is provided in a data processing system comprising a processor and a memory configured to implement a question and answer system (QA), for improving answers to an input question. An input question is received and parsed for a known set of critical information required to answer the input question. Responsive to one or more pieces of critical information failing to be identifiable in the input question, a user who submitted the input question is prompted for the one or more pieces of critical information via a graphical user interface (GUI).

Abstract: An approach is provided to analyze posts included in threads of one or more threaded discussions. The analysis identifies child posts that refer to a parent post that was selected a user of a user interface. A statement in the parent post is identified and that statement is analyzed using agreement data found in the child posts that refer to the parent post. The parent post and the analysis of the statement is displayed to the user at the user interface.

Abstract: An approach is provided to train a question answering (QA) system using answer keys based on forum content. In the approach, a question is selected from a post in a threaded discussion. An answer to the selected question is automatically identified from crowd-based sources, with the identified answer having a confidence level greater than a threshold. An answer key is built using the selected question and the identified answer. The QA system is automatically trained using the answer key.

Abstract: A method, computer program product, and computer system, for receiving a first set of ground truth instances from a first source. A second set of ground truth instances may be received from a second source. The first and second sets of ground truth instances may be weighed differently based on a level of trust associated with each of the first and second sources. The weighted first and second sets of ground truth instances may be applied in a machine learning task executed by a computer.

Abstract: A method, computer program product, and computer system, for receiving a first set of ground truth instances from a first source. A second set of ground truth instances may be received from a second source. The first and second sets of ground truth instances may be weighed differently based on a level of trust associated with each of the first and second sources. The weighted first and second sets of ground truth instances may be applied in a machine learning task executed by a computer.

Abstract: An approach is provided for evaluating subject matter experts (SMEs) in a question and answering (QA) system. In the approach, a number of responses are received at the QA system with each of the responses being a response to a common question and each of the responses being received from a SME. One of the responses is selected with the selected response being from one of the SMEs that is being evaluated. The approach evaluates the selected response by comparing the selected response to the responses received from the other SMEs. Based on the evaluation, the approach updates a SME confidence score that corresponds to the selected SME.

Abstract: An approach is provided for evaluating subject matter experts (SMEs) in a question and answering (QA) system. In the approach, a number of responses are received at the QA system with each of the responses being a response to a common question and each of the responses being received from a SME. One of the responses is selected with the selected response being from one of the SMEs that is being evaluated. The approach evaluates the selected response by comparing the selected response to the responses received from the other SMEs. Based on the evaluation, the approach updates a SME confidence score that corresponds to the selected SME.

Abstract: An approach is provided for evaluating subject matter experts (SMEs) in a question and answering (QA) system. In the approach, a number of responses are received at the QA system with each of the responses being a response to a common question and each of the responses being received from a SME. One of the responses is selected with the selected response being from one of the SMEs that is being evaluated. The approach evaluates the selected response by comparing the selected response to the responses received from the other SMEs. Based on the evaluation, the approach updates a SME confidence score that corresponds to the selected SME.

Abstract: An approach is provided to analyze posts included in threads of one or more threaded discussions. During the analysis, the approach identifies a parent post in one of the threads and identifies possible answers included in one or more child posts that refer to the parent post. The identified possible answers are scored and at least one of the identified possible answers is returned as an answer to the parent post.

Abstract: An approach is provided to train a question answering (QA) system using answer keys based on forum content. In the approach, a question is selected from a post in a threaded discussion. An answer to the selected question is automatically identified from crowd-based sources, with the identified answer having a confidence level greater than a threshold. An answer key is built using the selected question and the identified answer. The QA system is automatically trained using the answer key.

Abstract: An approach is provided to analyze posts included in threads of one or more threaded discussions. The analysis identifies child posts that refer to a parent post that was selected a user of a user interface. A statement in the parent post is identified and that statement is analyzed using agreement data found in the child posts that refer to the parent post. The parent post and the analysis of the statement is displayed to the user at the user interface.

Abstract: Mechanisms are provided for generating an answer for an input question when the answer is not directly present in a corpus of information. An input question is received from a computing device and analyzed to determine whether the input question is requesting an answer that is calculable. In response to a determination that the input question is requesting an answer that is calculable, one or more constituent data values are retrieved, from a corpus of information, for calculating the requested answer to the input question. A value corresponding to the requested answer is calculated based on the one or more retrieved constituent data values and is then output as the requested answer to the input question.

Abstract: Mechanisms are provided for dynamically selecting a subset of candidate answer sources for use by a question and answer (QA) system. The QA system receives an input question for which an answer is sought and generates one or more queries based on the input question. The QA system selects a subset of candidate answer sources, from a group of candidate answer sources, based on a ranked listing of candidate answer sources. The ranked listing of candidate answer sources is generated based on characteristics of previous candidate answers generated by the QA system for previously input questions. The QA system applies the one or more queries to the subset of candidate answer sources and generates at least one candidate answer for the input question based on results of applying the one or more queries to the subset of candidate answer sources.

Abstract: An approach is provided for evaluating subject matter experts (SMEs) in a question and answering (QA) system. In the approach, a number of responses are received at the QA system with each of the responses being a response to a common question and each of the responses being received from a SME. One of the responses is selected with the selected response being from one of the SMEs that is being evaluated. The approach evaluates the selected response by comparing the selected response to the responses received from the other SMEs. Based on the evaluation, the approach updates a SME confidence score that corresponds to the selected SME.

Abstract: A mechanism is provided in a data processing system comprising a processor and a memory configured to implement a question and answer system (QA), for improving answers to an input question. An input question is received and parsed for a known set of critical information required to answer the input question. Responsive to one or more pieces of critical information failing to be identifiable in the input question, a user who submitted the input question is prompted for the one or more pieces of critical information via a graphical user interface (GUI).

Abstract: Mechanisms are provided for dynamically selecting a subset of candidate answer sources for use by a question and answer (QA) system. The QA system receives an input question for which an answer is sought and generates one or more queries based on the input question. The QA system selects a subset of candidate answer sources, from a group of candidate answer sources, based on a ranked listing of candidate answer sources. The ranked listing of candidate answer sources is generated based on characteristics of previous candidate answers generated by the QA system for previously input questions. The QA system applies the one or more queries to the subset of candidate answer sources and generates at least one candidate answer for the input question based on results of applying the one or more queries to the subset of candidate answer sources.