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：US08909627B1

公开(公告)日：2014-12-09

申请号：US13661734

申请日：2012-10-26

Applicant: Google Inc.

Inventor： Dan Popovici ,  Terry Yang-Hoe Koo

IPC: G06F17/30

CPC classification number: G06F17/30646 ,  G06F17/30693

Abstract: Methods, systems, and apparatus, including computer programs are encoded on a computer storage medium, for fake skip evaluation of synonyms. In one aspect, a method includes determining, using query log data, that a particular search result selected by a user includes a query term included in an initial search query and a particular synonym that was generated for the query term using a particular synonym rule. The particular search result is selected by the user from among search results that were generated using an initial search query and one or more revised search queries that include the particular synonym. The method further includes determining, using the query log data, that a first search result is ranked above the particular search result, and includes the particular synonym for the query term. In response to these determinations, a fake skip count is incremented for the synonym rule that corresponds to the particular synonym.

Abstract translation: 包括计算机程序在内的方法，系统和装置被编码在计算机存储介质上，用于对同义词进行假跳过评估。 一方面，一种方法包括：使用查询日志数据确定用户选择的特定搜索结果包括包括在初始搜索查询中的查询词，以及使用特定同义词规则为查询词生成的特定同义词。 特定搜索结果由用户从使用初始搜索查询生成的搜索结果和包括特定同义词的一个或多个修订的搜索查询中选择。 该方法还包括使用查询日志数据确定第一搜索结果被排列在特定搜索结果之上，并且包括查询项的特定同义词。 响应于这些确定，对于与特定同义词对应的同义词规则，伪跳过计数增加。