Microsoft Technology Licensing, LLC

Timothy McDonnell Converse

Giovanni Lorenzo Thione

Chad P Walters

David D Ahn

Valerie Nygaard

Tracy Holloway King

Rion Langley Snow

James Michael Reinhold

Andrea Ryerson Burbank

Riccardo Turchetto

Joanna Mun Yee Chan

Scott A Waterman

Christian Biemann

Feng Pan

Systems, methods, and computer storage media having computer-executable instructions embodied thereon that facilitate generation of snippets. In embodiments, text features within a keyword-sentence window are identified. The text features are utilized to determine break features that indicate favorability of breaking at a particular location of the keyword-sentence window. The break features are used to recognize features of partial snippets such that a snippet score to indicate the strength of the partial snippet can be calculated. Snippet scores associated with partial snippets are compared to select an optimal snippet, that is, the snippet having the highest snippet score.

Generating snippets based on content features

Word sense probabilities are compressed for storage in a semantic index. Each word sense for a word is mapped to one of a number of 'buckets' by assigning a bucket score to the word sense. A scoring function is utilized to assign the bucket scores that maximizes the entropy of the assigned bucket scores. Once the bucket scores have been assigned to the word senses, the bucket scores are stored in the semantic index. The bucket scores stored in the semantic index may be utilized to prune one or more of the word senses prior to construction of the semantic index. The bucket scores may also be utilized to prune and rank the word senses at the time a query is performed using the semantic index.

Efficiently representing word sense probabilities

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Timothy McDonnell Converse