公开(公告)号：US20170098443A1

公开(公告)日：2017-04-06

申请号：US14872258

申请日：2015-10-01

Applicant: XEROX CORPORATION

Inventor： Himanshu Sharad Bhatt ,  SHOURYA ROY ,  Tanmoy Patra

IPC: G10L15/10 ,  G10L15/06 ,  G10L15/197 ,  G10L15/26

CPC classification number: G06F17/2785 ,  G06F16/35 ,  G10L15/26

Abstract: Methods and systems for training a conversation-classification model are disclosed. A first set of conversations in a source domain and a second set of conversation in a target domain are received. Each of the first set of conversations has an associated predetermined tag. One or more features are extracted from the first set of conversations and from the second set of conversations. Based on the similarity of content in the first set of conversations and the second set of conversations, a first weight is assigned to each conversation of the first set of conversations. Further, a second weight is assigned to the one or more features of the first set of conversations based on the similarity of the one or more features of the first set of conversations and of the second set of conversations. A conversation-classification model is trained based on the first weight and the second weight.

公开(公告)号：US20170220926A1

公开(公告)日：2017-08-03

申请号：US15014140

申请日：2016-02-03

Applicant: XEROX CORPORATION

Inventor： Shruti Chhabra ,  SHOURYA ROY ,  Ragunathan Mariappan

IPC: G06N3/08 ,  G06Q30/02 ,  G06Q50/00

CPC classification number: G06Q30/0269 ,  G06N7/005 ,  G06N20/00 ,  G06Q50/01

Abstract: The disclosed embodiments illustrate a method and a system for predicting future activities of a user on a social media platform. The method includes extracting a first time series of one or more historical activities performed by the user from a social media platform server. The method further includes receiving a second time series of one or more future events from a requestor-computing device. The method further includes determining a first set of forecast values and a second set of forecast values based on the first time series and/or the second time series, wherein the first set of forecast values is determined using an ARIMA technique, and the second set of forecast values is determined using a regression modelling technique. The method further includes predicting the future activities of the user based on the first set of forecast values and the second set of forecast values.