公开(公告)号：US09311299B1

公开(公告)日：2016-04-12

申请号：US13955491

申请日：2013-07-31

Applicant: Google Inc.

Inventor： Slav Petrov ,  Dipanjan Das ,  Ryan McDonald ,  Joakim Nivre ,  Oscar Tackstrom

IPC: G06F17/28 ,  G06F17/27

CPC classification number: G06F17/28 ,  G06F17/271 ,  G06F17/2785 ,  G06F17/2827

Abstract: A method and system are provided for a part-of-speech tagger that may be particularly useful for resource-poor languages. Use of manually constructed tag dictionaries from dictionaries via bitext can be used as type constraints to overcome the scarcity of annotated data in some instances. Additional token constraints can be projected from a resource-rich source language via word-aligned bitext. Several example models are provided to demonstrate this such as a partially observed conditional random field model, where coupled token and type constraints may provide a partial signal for training. The disclosed method achieves a significant relative error reduction over the prior state of the art.

Abstract translation: 为可能对资源贫乏的语言特别有用的词性标签器提供了一种方法和系统。 通过bitext使用手工构建的字典字典可用作类型约束来克服某些情况下注释数据的稀缺性。 额外的令牌约束可以从资源丰富的源语言通过字对齐的bitext进行投影。 提供了几个示例模型来证明这一点，例如部分观察到的条件随机场模型，其中耦合的令牌和类型约束可以提供用于训练的部分信号。 所公开的方法相对于现有技术的现有技术实现了显着的相对误差减小。

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.

公开(公告)号：US10394830B1

公开(公告)日：2019-08-27

申请号：US15068072

申请日：2016-03-11

Applicant: Google Inc.

Inventor： Sasha Blair-Goldensohn ,  Kerry Hannan ,  Ryan McDonald ,  Tyler Neylon ,  Jeffrey C. Reynar

IPC: G06F16/2457 ,  G06F16/93 ,  G06F16/248 ,  G06F16/9535

Abstract: A method, a system and a computer program product for ranking reviewable entities based on sentiment expressed about the entities. A plurality of review texts are identified wherein each review text references an entity. A plurality of sentiment scores associated with the plurality of review texts are generated, wherein each sentiment score for a review text indicates a sentiment directed to the entity referenced by the review text. A plurality of ranking scores for the plurality of entities are generated wherein each ranking score is based at least in part on one or more sentiment scores associated with one or more review texts referencing the entity. A plurality of search results associated with the plurality of entities are displayed based at least in part on the ranking scores.

公开(公告)号：US09317559B1

公开(公告)日：2016-04-19

申请号：US13858670

申请日：2013-04-08

Applicant: Google Inc.

Inventor： Sasha Blair-Goldensohn ,  Kerry Hannan ,  Ryan McDonald ,  Tyler Neylon ,  Jeffrey C. Reynar

IPC: G06F17/30 ,  G06F7/00

CPC classification number: G06F17/30699 ,  G06F17/30867

Abstract: A method, a system and a computer program product for ranking reviewable entities based on sentiment expressed about the entities. A plurality of review texts are identified wherein each review text references an entity. A plurality of sentiment scores associated with the plurality of review texts are generated, wherein each sentiment score for a review text indicates a sentiment directed to the entity referenced by the review text. A plurality of ranking scores for the plurality of entities are generated wherein each ranking score is based at least in part on one or more sentiment scores associated with one or more review texts referencing the entity. A plurality of search results associated with the plurality of entities are displayed based at least in part on the ranking scores.