公开(公告)号：US20200342181A1

公开(公告)日：2020-10-29

申请号：US16929203

申请日：2020-07-15

Applicant: Oath Inc.

Inventor： Joel Tetreault ,  Ellie Pavlick

IPC: G06F40/56 ,  G06F16/335 ,  G06F16/34 ,  G06F40/35 ,  G06F40/253

Abstract: The present teaching relates to automatic formality classification and transformation of online text items. In one example, a request is received for determining a formality level of a text item in an online communication. One or more linguistic features are extracted from the text item. Contextual information with respect to the online communication is extracted. A formality level of the text item is determined based on the one or more linguistic features and the contextual information. The formality level represents a degree of formality of the text item. The formality level is provided as a response to the request.

公开(公告)号：US10193833B2

公开(公告)日：2019-01-29

申请号：US15059596

申请日：2016-03-03

Applicant: OATH INC.

Inventor： Joel Tetreault ,  Aasish Pappu ,  Edo Liberty ,  Liangliang Cao ,  Meizhu Liu ,  Ellie Pavlick ,  Gilad Tsur ,  Yoelle Maarek

IPC: G06F17/24 ,  H04L12/58 ,  G06F17/27 ,  H04L29/08

Abstract: An electronic message composition support system, method and architecture is provided. Techniques including machine learning and natural language processing techniques are used to extend message composition capability and support and to provide feedback to a user regarding an error, condition, etc. detected in the user's message before the user sends the message, e.g., while the user is composing the message using a messaging application's user interface.

公开(公告)号：US11010687B2

公开(公告)日：2021-05-18

申请号：US15224434

申请日：2016-07-29

Applicant: Oath Inc.

Inventor： Yashar Mehdad ,  Joel Tetreault

IPC: G06N20/00 ,  G06F16/24 ,  G06F40/211 ,  G06F16/2457

Abstract: Methods and apparatus for detecting abusive language are disclosed. In one embodiment, a set of character N-grams is ascertained for a set of text. Feature values for a plurality of features of the set of text are determined, based, at least in part, on the set of character N-grams. A computer-generated model is applied to the feature values for the plurality of features to generate a score for the set of text, where the model includes a plurality of weights, each of the weights corresponding to one of the features. It may then be determined whether the set of text includes abusive language based, at least in part, on the score.