Filling Knowledge Gaps in a Broad-Coverage Machine Translation System

Knowledge-based machine translation (KBMT) techniques yield high quality in domains with detailed semantic models, limited vocabulary, and controlled input grammar. Scaling up along these dimensions means acquiring large knowledge resources. It also means behaving reasonably when definitive knowledge is not yet available. This paper describes how we can fill various KBMT knowledge gaps, often using robust statistical techniques. We describe quantitative and qualitative results from JAPANGLOSS, a broad-coverage Japanese-English MT system.Comment: 7 pages, Compressed and uuencoded postscript. To appear: IJCAI-9

Integrating knowledge bases and statistics in MT

We summarize recent machine translation (MT) research at the Information Sciences Institute of USC, and we describe its application to the development of a Japanese-English newspaper MT system. Our work aims at scaling up grammar-based, knowledge-based MT techniques. This scale-up involves the use of statistical methods, both in acquiring effective knowledge resources and in making reasonable linguistic choices in the face of knowledge gaps

Modelling the cumulative impacts of future coal mining and coal seam gas extraction on river flows: applications of methodology

This manuscript presents examples of the modelling of the impacts of coal mining and coal seam gas extraction on streamflow in five study catchments in Australia. The manuscript includes details on data preparation and model set-up and calibration. The modelling methodology enables the prediction of cumulative impacts from multiple future coal resource developments and distributes these predictions at multiple locations in the landscape. It is framed in terms of a structured uncertainty analysis to provide information on the likelihoods and potential ranges of various impacts. Also included is a qualitative uncertainty analysis which subjectively assesses the likely impact on model results of various assumptions made during the modelling procedure. Model results suggest that, in the Australian context, maximum percentage reductions in annual streamflow are approximately commensurate with the proportion of coal mine coverage. In coal seam gas fields, reductions in annual streamflow are proportional to well density. The manuscript goes on to demonstrate how these modelling results can be used to identify a zone of potential hydrological change within a catchment. This zone delineates those parts of the landscape where water-dependent landscape classes and assets may be vulnerable to change associated with changes in the streamflow regime. A corollary of this is that any parts of the landscape outside the zone of potential hydrological change are unlikely to be affected by coal resource development