公开(公告)号：US09536522B1

公开(公告)日：2017-01-03

申请号：US14143011

申请日：2013-12-30

Applicant: Google Inc.

Inventor： Keith Hall ,  Dipanjan Das ,  Terry Yang-Hoe Koo ,  Fernando Pereira

IPC: G10L15/18 ,  G10L15/22 ,  G10L15/06 ,  G06F17/30

CPC classification number: G10L15/18 ,  G06F17/3066 ,  G06F17/30684 ,  G06N5/00 ,  G10L15/063 ,  G10L15/1822

Abstract: 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.

Abstract translation: 提供系统和技术，用于训练具有信息检索模型注释的自然语言处理模型。 可以通过机器学习，使用包括由信息检索模型添加的词性标注和注释的训练样本来训练自然语言处理模型。 自然语言处理模型可以生成词性，解析树，开始，内部和外部标签，提及分组和命名实体识别预测，在训练示例中具有文本的置信度分数。 可以使用训练示例中的信息检索模型注释和词性标签来确定预测的准确性，并且可以调整自然语言处理模型。 训练后，自然语言处理模型可用于对新颖的输入进行预测，如搜索查询和潜在搜索结果。 搜索查询和潜在搜索结果可能具有信息检索模型注释。

公开(公告)号：US09305544B1

公开(公告)日：2016-04-05

申请号：US14594900

申请日：2015-01-12

Applicant: Google Inc.

Inventor： Slav Petrov ,  Ryan McDonald ,  Keith Hall

IPC: G06F17/20 ,  G06F17/28 ,  G06F17/27 ,  G06F17/21 ,  G10L15/00 ,  G10L17/00 ,  G10L21/00 ,  G10L15/06

CPC classification number: G10L15/06 ,  G06F17/2705 ,  G06F17/2827 ,  G06F17/2854 ,  G10L15/1822

Abstract: A source language sentence is tagged with non-lexical tags, such as part-of-speech tags and is parsed using a lexicalized parser trained in the source language. A target language sentence that is a translation of the source language sentence is tagged with non-lexical labels (e.g., part-of speech tags) and is parsed using a delexicalized parser that has been trained in the source language to produce k-best parses. The best parse is selected based on the parse's alignment with lexicalized parse of the source language sentence. The selected best parse can be used to update the parameter vector of a lexicalized parser for the target language.