公开(公告)号：WO2014209810A2

公开(公告)日：2014-12-31

申请号：PCT/US2014/043511

申请日：2014-06-20

Applicant: ALIBABA GROUP HOLDING LIMITED

Inventor： DONG, Xinghua ,  WU, Kewen ,  HUANG, Peng ,  LIN, Feng

IPC: G06F17/27

CPC classification number: G06F17/2775 ,  G06F17/275 ,  G06F17/2795 ,  G06F17/2827

Abstract: The present disclosure is related to a method and an apparatus of mining synonymous phrases. The method comprises: obtaining, according to a parallel text corpus, a first phrase-alignment relationship from phrases of a current language to phrases of an intermediate language, and a second phrase-alignment relationship from the phrases of the intermediate language to the phrases of the current language; obtaining, for a target phrase of current language, a first set of aligned phrases of the intermediate language that are aligned with the target phrase of the current language based on the first phrase-alignment relationship; obtaining a second set of aligned phrases of the current language that are aligned with selected phrase(s) in the first set of aligned phrases based on the second phrase-alignment relationship; and obtaining synonymous phrases for the target phrase from the second set of aligned phrases.

Abstract translation: 本公开涉及挖掘同义短语的方法和装置。 该方法包括：根据平行文本语料库获得从当前语言的短语到中间语言的短语的第一短语对齐关系，以及从中间语言的短语到短语的第二短语对齐关系 当前语言; 针对当前语言的目标短语获得基于第一短语对齐关系与当前语言的目标短语对准的中间语言的第一组对齐短语; 基于第二短语对齐关系，获得与第一组对齐短语中的所选短语对准的当前语言的第二组对齐短语; 并从第二组对齐的短语中获取目标短语的同义词。

公开(公告)号：WO2015170191A3

公开(公告)日：2015-11-12

申请号：PCT/IB2015/001443

申请日：2015-04-21

Applicant: ALIBABA GROUP HOLDING LIMITED

Inventor： HUANG, Kaiming ,  WU, Kewen ,  HUANG, Peng ,  LI, Bo ,  LIN, Feng

IPC: G06Q30/02

Abstract: Candidate promotion keywords are selected. Features of the candidate promotion keywords are extracted. The features include at least one of a search engine feature, an effect feature of non-directed traffic, and a text feature. The features of the candidate promotion keywords are used as input data of a pre-established keyword screening model, and superior promotion keywords are obtained according to a prediction result of the keyword screening model.