Abstract: Information extraction from domain-specific documents can include performing named entity recognition thereon. A regular expression tagger can tag regular expressions in the domain-specific documents without pre-annotated training sets for the regular expression tagger. Defined domain-specific terms can be augmented with terms and phrases related to the defined domain-specific terms based on rules specified for desired information about named entities to be extracted. A dictionary-based tagger can tag the defined and augmented domain-specific terms and phrases in the plurality of domain-specific documents. Conflicts of named entity recognition between the regular expression tagger and the dictionary-based tagger can be resolved. The desired information about named entities can be extracted. Classification can be performed to identify whether the domain-specific documents are related to the desired information.

Abstract: Deep Convolutional Neural Networks (CNNs) can be used to identify a non-linear control valve event in a control loop of a chemical process. Identifying the non-linear control valve event can be based on a predetermined feature of a red, green and blue (RGB) image, where the RGB image is obtained by merging respective matrices assembled from continuous wavelet transform coefficients derived from chemical plant operation controller signals. Once the non-linear control valve event is identified in a control loop, controller settings can be modified to minimize the non-linear control valve event and/or a root cause investigation can be conducted to address the issue.