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Training a natural language processing model with information retrieval model annotations

机译:使用信息检索模型注释训练自然语言处理模型

摘要

Systems and techniques are provided for training a natural language processing model with information retrieval model annotations. A natural language processing model may be trained, through machine learning, using training examples that include part-of-speech tagging and annotations added by an information retrieval model. The natural language processing model may generate part-of-speech, parse-tree, beginning, inside, and outside label, mention chunking, and named-entity recognition predictions with confidence scores for text in the training examples. The information retrieval model annotations and part-of-speech tagging in the training example may be used to determine the accuracy of the predictions, and the natural language processing model may be adjusted. After training, the natural language processing model may be used to make predictions for novel input, such as search queries and potential search results. The search queries and potential search results may have information retrieval model annotations.
机译:提供了用于利用信息检索模型注释来训练自然语言处理模型的系统和技术。可以使用训练示例通过机器学习来训练自然语言处理模型,该训练示例包括词性标签和信息检索模型添加的注释。自然语言处理模型可以在训练示例中生成带有文本置信度分数的词性,语法分析树,开始,内部和外部标签,提及分块和命名实体识别预测。训练示例中的信息检索模型注释和词性标记可以用于确定预测的准确性,并且可以调整自然语言处理模型。在训练之后,自然语言处理模型可以用于对新颖输入(例如搜索查询和潜在搜索结果)进行预测。搜索查询和潜在搜索结果可以具有信息检索模型注释。

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