Abstract: Methods and systems for dynamically updating machine learning models such as text classifiers. One of the methods includes: receiving first data; producing a first machine learning model using the first data; releasing the first machine learning model for use; receiving second data after receipt of the first data; determining that the second data has a difference metric relative to the first data that exceeds a difference threshold; retraining the first machine learning model using at least part of the second data, the retraining producing a second machine learning model; and releasing the second machine learning model for use.

Abstract: Methods, and systems for named entity recognition and disambiguation. One of the methods includes: receiving a document; extracting a plurality of mentions within the document; clustering the plurality of mentions to produce at least one cluster; identifying a plurality of candidate entities for the cluster; pairing each candidate entity and the cluster to create a plurality of candidate entity-cluster pairings; generating features for each candidate entity-cluster pairing; and selecting an entity, from the plurality of candidate entities, for the cluster based at least in part on features of a candidate entity pairing.

Abstract: Training an artificial intelligence model to determine whether a text document is written about a particular concept. A set of key terms that relate to the particular concept for which training of the artificial intelligence model is of interest is identified. The set of key terms is expanded into a key term superset comprising terms that include the set of key terms and a number of additional terms that have meanings related to a number of key terms in the set of key terms. Candidate documents having a selected likelihood of being relevant to the concept using the terms in the key term superset are identified based on an occurrence of the terms in the candidate documents. Training documents are generated from the candidate documents and unlabeled documents in which the unlabeled documents have not been selected based on the unlabeled documents having a likelihood of being relevant to the concept.

Abstract: Methods and systems for dynamically updating machine learning models such as text classifiers. One of the methods includes: receiving first data; producing a first machine learning model using the first data; releasing the first machine learning model for use; receiving second data after receipt of the first data; determining that the second data has a difference metric relative to the first data that exceeds a difference threshold; retraining the first machine learning model using at least part of the second data, the retraining producing a second machine learning model; and releasing the second machine learning model for use.