Yahoo! Inc.

Servicenow, Inc.

Jignesh Parmar

Rajesh Girish Parekh

Quaizar Vohra

Anil Kumar Madamala

Pavel Berkhin

Mitul Tiwari

Srivatsava Daruru

Olivier Nguyen

Soham Parikh

Ziaul Hasan Hashmi

Orlando Marquez

Patrice Bechard

Shounak Purkayastha

A method is provided for efficiently providing sentiments or other manual labels for textual training data. The method includes using an embedding model to project acquired user text to an embedding vector in an embedding space. Distances (e.g., cosine similarities) between this embedding vector and the embedding vectors determined for a plurality of already-label user text training examples are then determined. The already-labeled user text that has the shortest distance is determined and the label thereof is prospectively applied to the acquired user text and presented to a user for approval. The user can approve the prospectively applied label, in which case the newly acquired text is added to the training data with the prospectively applied label associated therewith for later use in training a language model. Alternatively, the user can decline the prospectively applied label and apply an alternative label to the newly acquired text.

Method for filtering and semi-automatically labeling training data

Embodiments of the invention relate to methods of presenting personalized search results pages to users, and to search engine systems and servers configured to implement such methods. For example, a method of presenting such a page to a user of a search engine includes steps of computing an engagement index of the user based on the distribution in time of that user's interactions with the search engine then presenting, in response to a query by the user, a personalized search results page to the user.

Personalizing sponsored search advertising layout using user behavior history

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Santa Clara, CA, United States of America

Jignesh Parmar