Microsoft Technology Licensing, LLC

Timothy J Hazen

Nicolae Duta

Diamond Bishop

Mohammad Babaeizadeh

Peter Longo

Alexandre Rochette

Gabriel Arien Desgarennes

Niel Chah

Abhishek Kumar

Soyoung Peraud

A classifier may be trained with less than all datasets manually annotated with labels. A small subset of verbatims may be manually labeled with topic labels as seeds. Data augmentations can be used to acquire seed verbatim sets for known topics and to assign temporary pseudo labels to the rest of the verbatims based on their vector space proximity to the labeled seed verbatims. The training may involve classification epochs during which embeddings are updated with the assumption that the pseudo labels are ground-truth labels. The training may also involve labeling epochs during which the updated embeddings are used to update the vectors corresponding to the verbatims, and pseudo labels are updated based on updated vector coordinates in the vector space. As the training process progresses through the epochs, the embeddings will converge.

Classifier assistance using domain-trained embedding

Methods and systems are provided for creating a calendar event using context. A natural language expression including at least one of words, terms, and phrases of text may be received at a calendar event creation module from an application. The calendar event creation module may identify one or more slots in the text of the natural language expression related to the calendar event using a first grammar module and a second grammar module. The one or more slots identified by the first grammar module and the second grammar module that indicate a calendar event may be compared to determine whether there is a match between the one or more identified slots. If a match is found, at least one calendar event using the one or more slots identified by the first grammar module and the second grammar module may be created.

Creating a calendar event using context

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Timothy J Hazen