Abstract: Embodiments relate to a system, program product, and method for knowledge resource management. A first document is subjected to a first semantic annotation and one or more entities, relations, and textual annotations of interest are identified. A neural model is built with the first document and trained with the first document and one or more of the first semantic annotations. An un-annotated document is applied to the neural model, and one or more second semantic annotations are produced. The un-annotated document is enriched with the produced second semantic annotation(s) and is subjected to adjudication. The neural model is selectively amended responsive to the adjudication.

Abstract: Embodiments relate to a system, program product, and method for dictionary membership management directed at identifying ambiguity in semantic resources. A dictionary of seed terms is applied to a text corpus and matching items in the corpus are identified. The linguistic properties for each matching item are characterized and a context pattern of each matching item is constructed. Each context pattern is applied to the dictionary and matching content between the seed terms and the context pattern is identified and quantified. Lexicon items from the dictionary that have anomalous behavior reflected in the quantification are identified. One or more seed words identified as having anomalous behavior are selectively removed from the dictionary.

Abstract: Embodiments relate to a system, program product, and method for information extraction and annotation of a data set. Neural models are utilized to automatically attach machine annotations to data elements within an unlabeled data set. The attached machine annotations are evaluated and a score is attached to the annotations. The score reflects a confidence of correctness of the annotations. A labeled data set is iteratively expanded with selectively evaluated annotations based on the attached score. The labeled data set is applied to an unexplored corpus to identify matching corpus data to populated instances of the labeled data set.

Abstract: Embodiments relate to a system, program product, and method for iterative expansion and application of a domain-specific dictionary. One or more dictionary instances are applied against a text corpus. The dictionary is iteratively expanded and selectively populated with one or more additional dictionary instances, including semantically similar instances to the applied dictionary instances and extension instances contextually related to the applied dictionary instances. The iteratively expanded dictionary is applied to an unexplored corpus to identify matching corpus data to populated instances of the dictionary.