Techniques To Incorporate The Benefits Of A Hierarchy In A Modified Hidden Markov Model
This paper explores techniques to take advantage of the fundamental difference in structure between hidden Markov models (HMM) and hierarchical hidden Markov models (HHMM). The HHMM structure allows repeated parts of the model to be merged together. A merged model takes advantage of the recurring patterns within the hierarchy, and the clusters that exist in some sequences of observations, in order to increase the extraction accuracy. This paper also presents a new technique for reconstructing grammar rules automatically. This work builds on the idea of combining a phrase extraction method with HHMM to expose patterns within English text. The reconstruction is then used to simplify the complex structure of an HHMM ::: ::: The models discussed here are evaluated by applying them to natural language tasks based on CoNLL-2004 and a sub-corpus of the Lancaster Treebank
This study attempts to address two ongoing theoretical and empirical questions in the strategic human resource management literature: (1) Do high performance work systems positively influence future firm performance with a control for past firm performance? - causality, in part, in the relationship between HPWS and firm performance and (2) What are the determinants of the adoption of HPWS? Investigating these two questions is critical to help better understand the theoretical and empirical linkage between HPWS and firm performance. This study contributes to the SHRM literature by providing supporting evidence that HPWS predicts future firm performance with a control for past firm performance. In addition, this study sheds lights on the fact that while multiple factors could influence the adoption of popular HPWS, organizational values on HRM are the dominating adoption factor of HPWS.