Recommender Systems, Machine Learning and Multi-document Natural Language Processing
A number of technologies including Office Graph can ensure that relevant, fresh, information and documents are available to individuals and groups during the performance of their tasks. Items that can be recommended, that can be routed, sorted and presented, include documents, multimedia and data. Software such as Office Graph utilize sophisticated machine learning algorithms to connect people to relevant content, conversations and people around them, including based upon their multiple simultaneous interests, tasks, groups or roles.
Innovations are possible with regard to the determination of contextual, task-based, relevance for recommending, routing, sorting and presenting content to individuals and to groups, enhancing their performance or providing them with serendipitous discovery.
Multi-document natural language processing algorithms can provide new conveniences to individuals and to groups, processing collections of documents and multimedia utilized by individuals and by groups during their various tasks including those of business, education and e-participation scenarios. Multi-document natural language processing algorithms are interoperable with advanced machine learning algorithms including those utilized by software such as Office Graph. Multi-document natural language processing technology innovations include, but are not limited to, real-time fact checking, argument analysis, spin and persuasion detection and sentiment analysis.
Hyperlinks
Pingback: E-Participation, Decision Support Systems, Multi-document Natural Language Processing and Cognitive Bias Mitigation | Collaborative Software Community Group