Abstract. The June 28, 1992 (Mw=7.3) Landers, California, earthquake was the first earthquake to be surveyed by a continuously operating Global Positioning System (GPS) array. The coordinate time series of seven sites are evaluated for station displacements during an interval of 100 days centered on the day of the earthquake. We employ a new spatial filtering technique that removes common-mode errors from the coordinate time series. This approach provides precise estimates of site-specific displacements compared to the cumbersome method of analyzing baselines between pairs of stations. All sites indicate significant coseismic horizontal displacements of 5-65 mm with uncertainties of 1-2 mm. Horizontal displacements are in general agreement with elastic dislocation models, in particular for sites closer to the epicenter. Vertical displacements range from-13 to +7 mm with uncertainties of 2-4 mm. The observed vertical displacements in all cases show 5-10 mm more subsidence than expected from geodetic and seismic/geologic models. Significant postseismic horizontal displacements totaling 6+2 mm (10--20% of the coseismic displacement) are detected at the three sites closest to the epicenter. These displacements are modeled as a short-term exponential relaxation with a decay time of 22+10 days superimposed on a longer-term linear interseismic trend. Scaling the observed coseismic and postseismic displacements at one of the sites with the distance to the epicenter provides a measure of site strain, which agrees well with the direction and magnitude determined from more precise laser strain meter data. The time series do not show any detectable preseismic displacements.

A demo system is available at http://textmining.i2r.a-star.edu.sg/NLS/demo.htm. Technology license is available upon the bilateral agreement.

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The purpose of this research is to enhance an HMM-based named entity recognizer in the biomedical domain. First, we analyze the characteristics of biomedical named entities. Then, we propose a rich set of features, including orthographic, morphological, part-of-speech, and semantic trigger features. All these features are integrated via a Hidden Markov Model with back-off modeling. Furthermore, we propose a method for biomedical abbreviation recognition and two methods for cascaded named entity recognition. Evaluation on the GENIA V3.02 and V1.1 shows that our system achieves 66.5 and 62.5 F-measure, respectively, and outperforms the previous best published system by 8.1 F-measure on the same experimental setting. The major contribution of this paper lies in its rich feature set specially designed for biomedical domain and the effective methods for abbreviation and cascaded named entity recognition. To our best knowledge, our system is the first one that copes with the cascaded phenomena.