Information Extraction Sample Clauses
Information Extraction. You shall not, without explicit prior written authorization from Frappe specifically permitting such an action, perform reverse look-up, trace or seek to trace any information on any other User or Customer of Service, including any account on the Service not owned by You, to its source, or exploit the Website, or any service or information made available or offered by or through the Website, in any way where the purpose is to reveal any information, including but not limited to personal identification or information, other than Your own information, as provided for by the Website.
Information Extraction. The information extraction was shared between two individuals. The themes of interest are: the study goal (noted for context); model type; model input data; model validation data. Records included mathematical models for the spread of ILIs (influenza like illnesses); mathematical models for the spread of human infectious diseases across the globe; mathematical models for the spread of human infectious diseases across localised regions, but which could be reparameterised to become global models; human infectious disease spread papers which refer to population movement data. Records were classified as one of four broad categories: metapopulation model; individual-based model; data analysis; or probabilistic model. Model input data could be broken down thematically into: epidemiological data; population data; and travel data. Epidemiological data concerns model parameters which describe the disease, such as the average length of infection. Population data relates to difference within the total modelled population, for example splitting the population into different age brackets or determining how many individuals live in a particular region. Travel data consists of information on travel patterns of individuals, either commuting or long-distance travel. Validation data should be from a source independent to all input data sources so that model outputs can be compared against it.
Information Extraction. Named Entity Recognition 13 4.2 Mathematical Formula Recognition 14 4.2.1 Segmentation 16 4.3 Formula Parsing 17 4.3.1 Projection Profile Cutting 17 4.3.2 Virtual Link Networks 18 4.3.3 Graph Rewriting 20 4.3.4 Baseline Parsing with Grammars 20