Microsoft Technology Licensing, LLC

Pak On Chan

Sharada Shirish Acharya

Mohit Sewak

Weisheng Li

Milenko Drinic

Ravi Kiran Reddy Poluri

William Blum

Michael Abraham Betser

Christian Rudnick

Sihong Liu

The technology described herein determines whether a candidate text is in a requested class by using a generative model that may not be trained on the requested class. The present technology may use of a model trained primarily in an unsupervised mode, without requiring a large number of manual user-input examples of a label class. The may produce a semantically rich positive example of label text from a candidate text and label. Likewise, the technology may produce from the candidate text and the label a semantically rich negative example of label text. The labeling service makes use of a generative model to produce a generative result, which estimates the likelihood that the label properly applies to the candidate text. In another aspect, the technology is directed toward a method for obtaining a semantically rich example that is similar to a candidate text.

Automatic labeling of text data

Computerized systems are provided for detecting one or more tables and performing information extraction and analysis on any given table. Information can be extracted from one or more cells or fields of a table and feature vectors representing individual cells, rows, and/or columns of the table can be derived and concatenated together. In this way, embodiments can use some or all of the 'context' or values contained in various feature vectors representing some or all of a single table as signals or factors to consider when generating a decision statistic, such as a classification prediction, for a particular cell.

Computerized information extraction from tables

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Redmond, WA, United States of America

Pak On Chan