International Business Machines Corporation

Beata J Strack

Corville O Allen

Andrew Ronald Freed

Richard A Salmon

Dwi Sianto Mansjur

Kyle Croutwater

Julius Goth, Iii

An active learning framework is operative to identify informative questions that should be added to existing question-answer (Q&A) pairs that comprise a training dataset for a learning model. In this approach, the question-answer pairs (to be labeled as 'true' or 'false') are automatically selected from a larger pool of unlabeled data. A spatial-directed clustering algorithm partitions the relevant question-answer space of unlabeled data. A margin-induced loss function is then used to rank a question. For each question selected, a label is then obtained, preferably by assigning a prediction for each associated question-answer pair using a current model that has been trained on labeled question-answer pairs. After the questions are labeled, an additional re-sampling is performed to assure high quality of the training data. Preferably, and with respect to a particular question, this additional re-sampling is based on a distance measure between correct and incorrect answers.

Geometry-directed active question selection for question answering systems

A mechanism is provided in a data processing system for corpus quality analysis. The mechanism applies at least one filter to a candidate corpus to determine a degree to which the candidate corpus supplements existing corpora for performing a natural language processing (NLP) operation. Responsive to a determination to add the candidate corpus to the existing corpora based on a result of applying the at least one filter, the mechanism adds the candidate corpus to the existing corpora to form modified corpora. The mechanism performs the NLP operation using the modified corpora.

Corpus quality analysis

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New York, NY, United States of America

Beata J Strack