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    Training a similar passage cognitive system using ground truth from a question answering cognitive system
    3.
    发明授权
    Training a similar passage cognitive system using ground truth from a question answering cognitive system 有权
    从接受认知系统的问题训练使用地面真相的类似段落认知系统

    公开(公告)号：US09495648B1

    公开(公告)日：2016-11-15

    申请号：US14966603

    申请日：2015-12-11

    申请人： International Business Machines Corporation

    发明人： John M. Boyer ,  Kshitij P. Fadnis ,  Collin J. Murray ,  Justin A. Ziniel

    IPC分类号： G06F17/00 ,  G06F17/20 ,  G06N99/00 ,  G06N7/00

    CPC分类号： G06N99/005 ,  G06F17/2785 ,  G06F17/30654 ,  G06F17/30684 ,  G06N5/04

    摘要： A mechanism is provided in a data processing system comprising at least one processor and a memory comprising instructions which, when executed by the at least one processor, causes the at least one processor to train a similar passage cognitive system. The mechanism receives a question and answer key for a question answering cognitive system, the question and answer key comprising a list of question and answer specification pairs. Each question is a text string and each answer specification references one or more text passages from a corpus of information. The mechanism uses the question and answer key to generate a similar passage map for the similar passage cognitive system, the similar passage map comprising a list of text relation pairs. Each text relation pair comprises a sample input text component and a list comprising one or more sample output text components. The mechanism trains a similar passage machine learning model of the similar passage cognitive system using the similar passage map.

    摘要翻译： 在包括至少一个处理器和存储器的数据处理系统中提供一种机制，所述指令在所述至少一个处理器执行时使所述至少一个处理器训练类似的通道认知系统。 该机制接收到一个回答认知系统的问题的答案和答案，问题和答案包括问题和答案规范对的列表。 每个问题都是一个文本字符串，每个答案规范引用一个或多个文本语料库的信息。 该机制使用问答键来生成类似的段落认知系统的类似的传递图，类似的传递图包括文本关系对的列表。 每个文本关系对包括样本输入文本分量和包括一个或多个样本输出文本分量的列表。 该机制使用类似的通道图来训练类似的通道认知系统的类似通道机器学习模型。

    IDENTIFYING TEXT FOR LABELING UTILIZING TOPIC MODELING-BASED TEXT CLUSTERING
    4.
    发明申请
    IDENTIFYING TEXT FOR LABELING UTILIZING TOPIC MODELING-BASED TEXT CLUSTERING 审中-公开

    公开(公告)号：US20190155947A1

    公开(公告)日：2019-05-23

    申请号：US15816170

    申请日：2017-11-17

    申请人： International Business Machines Corporation

    发明人： Man Chu ,  Steven M. Pritko ,  Zhe Zhang ,  Justin A. Ziniel

    IPC分类号： G06F17/30 ,  G06N99/00 ,  G06N5/04 ,  G06N3/00 ,  G10L15/22 ,  G10L15/18 ,  G06F17/21 ,  G06F17/27

    摘要： Software that selects portions of unlabeled text for labeling, by performing the following operations: (i) receiving a set of unlabeled input text for classification with respect to a particular domain, wherein the domain includes a labeled corpus for which topics of a set of topics correspond to labels from the corpus, and wherein the topics include statistical probability distributions of words in the corpus; (ii) performing topic modeling on the input text to associate portions of the input text with respective classifications, wherein the classifications include statistical probability distributions of topics of the set of topics in the respective portions of the input text; and (iii) applying a machine learning-based selection strategy to the portions of the input text and their respective classifications to identify one or more portions of the input text for labeling.

    Ground Truth Improvement Via Machine Learned Similar Passage Detection
    8.
    发明申请
    Ground Truth Improvement Via Machine Learned Similar Passage Detection 审中-公开

    公开(公告)号：US20170169355A1

    公开(公告)日：2017-06-15

    申请号：US14966802

    申请日：2015-12-11

    申请人： International Business Machines Corporation

    发明人： John M. Boyer ,  Kshitij P. Fadnis ,  Collin J. Murray ,  Justin A. Ziniel

    IPC分类号： G06N99/00 ,  G06N5/02

    CPC分类号： G06F17/2785 ,  G06F16/90332 ,  G06N5/04

    摘要： A mechanism is provided in a data processing system to improve ground truth in a question answering cognitive system. The mechanism trains a similar passage machine learning model for a similar passage cognitive system using a question and answer key to form a trained similar passage machine learning model. The question and answer key comprises a list of question and answer specification pairs forming a ground truth for the question answering cognitive system. Each question is a text string and each answer specification references one or more text passages from a corpus of information. Responsive to a search event, the mechanism sends at least one text input to the similar passage cognitive system operating in accordance with the trained similar passage machine learning model, wherein the text input comprises a given question text string or a given text passage from the question and answer key, and receives from the similar passage cognitive system a response list of references to text passages from the corpus of information. Responsive to an answer acceptance event for at least one text passage from the response list, the mechanism supplements the question and answer key with the at least one text passage to form a supplemented question and answer key. The mechanism trains a question answering machine learning model of the data processing system using the supplemented question and answer key such that the question answering cognitive system operates in accordance with the trained question answering machine learning model.