Robert Boschgmbh

Xinyan Zhao

Zhe Feng

Haibo Ding

Systems and methods for training a machine-learning model for named-entity recognition. A rule graph is constructed including a plurality of nodes each corresponding to a different labeling rule of a set of labeling rules (including a set of seeding rules of known labeling accuracy and a plurality of candidate rules of unknown labeling accuracy). The nodes are coupled to other nodes based on which rules exhibit the highest sematic similarity. A labeling accuracy metric is estimated for each candidate rule by propagating a labeling confidence metric through the rule graph from the seeding rules to each candidate rule. A subset of labeling rules is then identified by ranking the rules by their labeling confidence metric. The identified subset of labeling rules is applied to unlabeled data to generate a set of weakly labeled named entities and the machine-learning model is trained based on the set of weakly labeled named entities.

Graph-based labeling rule augmentation for weakly supervised training of machine-learning-based named entity recognition

Methods and systems for performing semantic entity recognition. The method includes accessing a document stored in a memory and selecting from a general knowledge data repository, target domain information based on a specified target domain. The method also includes generating a plurality of weak annotators for the document based upon the selected target domain information and expert knowledge from a domain-specific expert knowledge data repository and applying the plurality of weak annotators to the document to generate a plurality of weak labels. The method further includes selecting at least one weak label from the plurality of weak labels as training data and training a semantic entity prediction model using the training data.

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Ann Arbor, MI, United States of America

Xinyan Zhao