This paper presents a new risk assessment methodology for coal mine excavated slopes. This new empirical-statistical slope.stability assessment m. ethodology （SSAM! is intended for use by geotechnical engineers at bo...This paper presents a new risk assessment methodology for coal mine excavated slopes. This new empirical-statistical slope.stability assessment m. ethodology （SSAM! is intended for use by geotechnical engineers at both the design review and operational stages of a mine＇s life to categonse the risk of an excavated coal mine slope. A likelihood of failure is determined using a new slope stability classification system for excavated coal mine slopes developed using a database of 119 intact and failed case studies sourced from open cut coal mines in Australia. Consequence of failure is based on slope height and stand-off distance at the toe of the excavated slope. Results are presented in a new risk matrix, with slope risk being divided into low, medium and high categories. The SSAM is put forward as a new risk assess- ment methodology to assess the potential for, and consequence of, excavated coal mine slope failure. Unlike existing classification systems, assumptions about the likely failure mode or mechanism are not required. Instead, the SSAM applies an approach which compares the conditions present within the exca- vated slope face, with the known past performance of slopes with similar geotechnical and geometrical conditions, to estimate the slope＇s propensity for failure. The SSAM is novel in that it considers the depo- sitional history of strata in an excavated slope and how this sequence affects slope stability. It is further novel in that it does not require explicit measurements of intact rock, rock mass and/or defect strength to rapidly calculate a slope＇s likelihood of failure and overall risk. Ratings can be determined entirely from visual observations of the excavated slope face. The new SSAM is designed to be used in conjunction with existing slope stability assessment tools.展开更多
This paper focuses on how to improve aspect-level opinion mining for online customer reviews. We first propose a novel generative topic model, the Joint Aspect/Sentiment (JAS) model, to jointly extract aspects and asp...This paper focuses on how to improve aspect-level opinion mining for online customer reviews. We first propose a novel generative topic model, the Joint Aspect/Sentiment (JAS) model, to jointly extract aspects and aspect-dependent sentiment lexicons from online customer reviews. An aspect-dependent sentiment lexicon refers to the aspect-specific opinion words along with their aspect-aware sentiment polarities with respect to a specific aspect. We then apply the extracted aspectdependent sentiment lexicons to a series of aspect-level opinion mining tasks, including implicit aspect identification, aspect-based extractive opinion summarization, and aspect-level sentiment classification. Experimental results demonstrate the effectiveness of the JAS model in learning aspectdependent sentiment lexicons and the practical values of the extracted lexicons when applied to these practical tasks.展开更多
基金funded by the Australian Coal Association Research Program(ACARP)
文摘This paper presents a new risk assessment methodology for coal mine excavated slopes. This new empirical-statistical slope.stability assessment m. ethodology （SSAM! is intended for use by geotechnical engineers at both the design review and operational stages of a mine＇s life to categonse the risk of an excavated coal mine slope. A likelihood of failure is determined using a new slope stability classification system for excavated coal mine slopes developed using a database of 119 intact and failed case studies sourced from open cut coal mines in Australia. Consequence of failure is based on slope height and stand-off distance at the toe of the excavated slope. Results are presented in a new risk matrix, with slope risk being divided into low, medium and high categories. The SSAM is put forward as a new risk assess- ment methodology to assess the potential for, and consequence of, excavated coal mine slope failure. Unlike existing classification systems, assumptions about the likely failure mode or mechanism are not required. Instead, the SSAM applies an approach which compares the conditions present within the exca- vated slope face, with the known past performance of slopes with similar geotechnical and geometrical conditions, to estimate the slope＇s propensity for failure. The SSAM is novel in that it considers the depo- sitional history of strata in an excavated slope and how this sequence affects slope stability. It is further novel in that it does not require explicit measurements of intact rock, rock mass and/or defect strength to rapidly calculate a slope＇s likelihood of failure and overall risk. Ratings can be determined entirely from visual observations of the excavated slope face. The new SSAM is designed to be used in conjunction with existing slope stability assessment tools.
基金supported by National Natural Science Foundation of China under Grants No.61232010, No.60903139, No.60933005, No.61202215, No.61100083National 242 Project under Grant No.2011F65China Information Technology Security Evaluation Center Program under Grant No.Z1277
文摘This paper focuses on how to improve aspect-level opinion mining for online customer reviews. We first propose a novel generative topic model, the Joint Aspect/Sentiment (JAS) model, to jointly extract aspects and aspect-dependent sentiment lexicons from online customer reviews. An aspect-dependent sentiment lexicon refers to the aspect-specific opinion words along with their aspect-aware sentiment polarities with respect to a specific aspect. We then apply the extracted aspectdependent sentiment lexicons to a series of aspect-level opinion mining tasks, including implicit aspect identification, aspect-based extractive opinion summarization, and aspect-level sentiment classification. Experimental results demonstrate the effectiveness of the JAS model in learning aspectdependent sentiment lexicons and the practical values of the extracted lexicons when applied to these practical tasks.
