George Cartwright (1739-1819) was a soldier, trader and explorer who spent sixteen years travelling and working in Labrador in northern Canada. In 1754, he entered the Royal Military Academy at Woolwich in London before taking up a commission in the Indian army. In 1760, he served in the Seven Years' War, returning to England with the rank of Captain. After his army career, he turned to exploration and set himself up as a trader along the Labrador coast of northern Canada, making six expeditions from 1770-86 between Cape St Charles and Hamilton Inlet. This three-volume work, published in 1792, recounts the author's adventures along the Labrador coast, vividly portraying the land and the culture of the indigenous peoples. It also contains a brief autobiography and a glossary of unusual terms such as 'jerk', 'king-hairs', and 'lolly'.
The assessment of risks posed by natural hazards such as floods, droughts, earthquakes, tsunamis or cyclones is often based on short-term historical records that may not reflect the full range or magnitude of events possible. As human populations grow, especially in hazard-prone areas, methods for accurately assessing natural hazard risks are becoming increasingly important.
The book offers a detailed guide to temporal ordering, exploring open problems in the field and providing solutions and extensive analysis. It addresses the challenge of automatically ordering events and times in text. Aided by TimeML, it also describes and presents concepts relating to time in easy-to-compute terms. Working out the order that events and times happen has proven difficult for computers, since the language used to discuss time can be vague and complex. Mapping out these concepts for a computational system, which does not have its own inherent idea of time, is, unsurprisingly, tough. Solving this problem enables powerful systems that can plan, reason about events, and construct stories of their own accord, as well as understand the complex narratives that humans express and comprehend so naturally.
This book presents a theory and data-driven analysis of temporal ordering, leading to the identification of exactly what is difficult about the task. It then proposes and evaluates machine-learning solutions for the major difficulties.
It is a valuable resource for those working in machine learning for natural language processing as well as anyone studying time in language, or involved in annotating the structure of time in documents.
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