A combined paleomagnetic, rock magnetic and magnetic fabrics study is conducted on the redbeds of Early Triassic Liujiagou Formation from Qinshui Basin, North China Block (NCB). The E/I (elongation/inclination) method indicates that the characteristic remanent magnetizations (ChRM) was significantly affected by inclination shallowing with a flattening factor f=0.6. Rock magnetic analysis indicates that hematite and magnetite are the main magnetic carriers. Anisotropy of magnetic susceptibility (AMS) result shows typical depositional fabrics in water. The chRM has been successfully isolated from 15 sites with tilt-corrected mean direction of D_s=318.8°, I_s=30.9° (α₉₅=6.9°) and a corresponding paleomagnetic pole at 49.3°N, 5.5°E (A₉₅=6.7°). The pole after E/I correction is 53.5°N, 18.0°E. Combining with published high quality Early Triassic pole (Q≥4) for NCB, the mean Early Triassic pole for NCB before and after inclination shallowing correction is 55.3°N, 357.1°E (A₉₅=5.5°) and 60.8°N, 13.4°E (A₉₅=5.8°), respectively. The paleolatitude of NCB is obviously higher at Early Triassic after inclination shallowing correction with ~10° increase. Furthermore, the corrected Early Triassic paleolatitude of NCB seems to be different from that of South China Block (SCB) at the east, which may result from the inclination shallowing error in Early Triassic limestone rocks of SCB instead of the real separation between the two blocks.

The authors bring forward seven types of semantic features related to opinion target extraction. They are evaluation-triggering words, evaluation-eliminating words, the words insulated from opinion target, the forward-orientated verbs, the backward-orientated verbs, the verbs of psychological movement and the attributive-directing verbs. Five types of semantic features related to polarity identification are also proposed. They are positive nouns, negative nouns, meaning-shifting nouns, measuring adjectives and semantic construction. The authors explain the twelve features from aspects of the necessity and their usages. The result shows that the application of semantic features improves the precision of the system.