International Business Machines Corporation

Avinash Asthana

Edward J Biddle

Shikhar Kwatra

Diwesh Pandey

Sujatha B Perepa

Tathagato Bose

John Ferdinand Pitrelli

Vijai Gandikota

Adaptive content masking is provided. Content visibility customizations are applied to a view of shared information by respective participants during screen sharing in a web conferencing meeting using content visibility control settings. Content visibility is adjusted via adaptive learning based on frequency and type of screen sharing previously completed between a content presenter and respective participants of the web conferencing meeting. Content masking is applied adaptively based on monitoring the shared information and correlating the shared information with respective participants by retrieving meeting information prior to the web conferencing meeting and retrieving screen capture attributes from metadata of the web conferencing meeting that are mapped to the shared information being displayed on screens corresponding to respective participants.

Adaptive content masking during web conferencing meetings

Embodiments provide a computer implemented method in a data processing system including a processor and a memory, the memory including instructions that are executed by the processor to cause the processor to implement a system for generating a type system. The method includes: receiving a document corpus; identifying frequently occurring words from the document corpus, disregarding stop words; extracting a conceptual text for each frequently occurring word from a structured information database; performing a cluster analysis on each conceptual text to identify possible entity types; performing a frequency analysis on possible entity types to select at least one entity type; identifying a relation type between entities in the document corpus; and generating the type system including entity types and relation types.

Generation of domain specific type system

Concepts for managing a supervised machine learning model of a set of documents are presented. A system obtains annotated versions of the documents, the documents being annotated by annotators. A conflict between a plurality of annotations of the annotated versions of the documents is identified. The machine learning model includes a set of entities and relations defining relationships between entities. The identified conflict is resolved by at least one of identifying the correct annotation between the conflicting options, splitting the annotated text into two separate entities or relations, generating a new entity at the same or a less specific hierarchical level as the entities or relation in conflict, and/or changing an annotation of the annotated version of the document.

Supervised machine learning models of documents

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London, United Kingdom

Avinash Asthana