title: Improving the performance of Hierarchical Hidden Markov Models on Information Extraction tasks

authors: Chou, Lin-Yi

subjects: hidden Markov model

abstract: This thesis presents novel methods for creating and improving hierarchical hidden Markov models.  The work centers around transforming a traditional tree structured hierarchical hidden Markov model (HHMM) into an equivalent model that reuses repeated sub-trees.  This process temporarily breaks the tree structure constraint in order to leverage the benefits of combining repeated sub-trees. These benefits include lowered cost of testing and an increased accuracy of the final model-thus providing the model with greater performance.  The result is called a merged and simplified hierarchical hidden Markov model (MSHHMM).

The thesis goes on to detail four techniques for improving the performance of MSHHMMs when applied to information extraction tasks, in terms of accuracy and computational cost.  Briefly, these techniques are: a new formula for calculating the approximate probability of previously unseen events; pattern generalisation to transform observations, thus increasing testing speed and prediction accuracy; restructuring states to focus on state transitions; and an automated flattening technique for reducing the complexity of HHMMs.

The basic model and four improvements are evaluated by applying them to the well-known information extraction tasks of Reference Tagging and Text Chunking.  In both tasks, MSHHMMs show consistently good performance across varying sizes of training data. In the case of Reference Tagging, the accuracy of the MSHHMM is comparable to other methods. However, when the volume of training data is limited, MSHHMMs maintain high accuracy whereas other methods show a significant decrease.  These accuracy gains were achieved without any significant increase in processing time.  For the Text Chunking task the accuracy of the MSHHMM was again comparable to other methods. However, the other methods incurred much higher processing delays compared to the MSHHMM.  The results of these practical experiments demonstrate the benefits of the new method-increased accuracy, lower computation costs, and better performance.

text: Improving the performance of Hierarchical Hidden Markov Models on Information Extraction tasks This thesis presents novel methods for creating and improving hierarchical hidden Markov models.  The work centers around transforming a traditional tree structured hierarchical hidden Markov model (HHMM) into an equivalent model that reuses repeated sub-trees.  This process temporarily breaks the tree structure constraint in order to leverage the benefits of combining repeated sub-trees. These benefits include lowered cost of testing and an increased accuracy of the final model-thus providing the model with greater performance.  The result is called a merged and simplified hierarchical hidden Markov model (MSHHMM).

The thesis goes on to detail four techniques for improving the performance of MSHHMMs when applied to information extraction tasks, in terms of accuracy and computational cost.  Briefly, these techniques are: a new formula for calculating the approximate probability of previously unseen events; pattern generalisation to transform observations, thus increasing testing speed and prediction accuracy; restructuring states to focus on state transitions; and an automated flattening technique for reducing the complexity of HHMMs.

The basic model and four improvements are evaluated by applying them to the well-known information extraction tasks of Reference Tagging and Text Chunking.  In both tasks, MSHHMMs show consistently good performance across varying sizes of training data. In the case of Reference Tagging, the accuracy of the MSHHMM is comparable to other methods. However, when the volume of training data is limited, MSHHMMs maintain high accuracy whereas other methods show a significant decrease.  These accuracy gains were achieved without any significant increase in processing time.  For the Text Chunking task the accuracy of the MSHHMM was again comparable to other methods. However, the other methods incurred much higher processing delays compared to the MSHHMM.  The results of these practical experiments demonstrate the benefits of the new method-increased accuracy, lower computation costs, and better performance.

year: 2006

title: Hydrology

authors: Campbell, David I.

subjects: wetland

abstract: When applied to wetlands, the science of hydrology is concerned with how the storage and movement of water into and out of a wetland affects the plants and animals, and the soils on which they grow. Most wetland scientists agree that the single most important factor determining both wetland type and function is hydrology. Consequently, changes in hydrology are the leading causes of wetland degradation or destruction. The two case studies in this chapter illustrate how water was returned to a previously drained lowland swamp and a peat bog and the effects on the vegetation communities. Both sites had been drained as further dry land was desired for farming and urban development, a common scenario throughout New Zealand.

text: Hydrology When applied to wetlands, the science of hydrology is concerned with how the storage and movement of water into and out of a wetland affects the plants and animals, and the soils on which they grow. Most wetland scientists agree that the single most important factor determining both wetland type and function is hydrology. Consequently, changes in hydrology are the leading causes of wetland degradation or destruction. The two case studies in this chapter illustrate how water was returned to a previously drained lowland swamp and a peat bog and the effects on the vegetation communities. Both sites had been drained as further dry land was desired for farming and urban development, a common scenario throughout New Zealand.

year: 2010

title: Weaving kaupapa Māori and e-Learning

authors: Neal, Terry

subjects: New Zealand

abstract: This paper describes the creation and design of an online professional development course that aims to help educators in New Zealand to weave kaupapa Māori and e-Learning approaches together. The team sought to model weaving of kaupapa Māori and e-Learning approaches in how they worked together and the educational design for the material. The authors propose that educators with these skills are crucial if we are to effectively meet the needs of Māori learners, more than that, of all New Zealand learners, in the 21st century.

text: Weaving kaupapa Māori and e-Learning This paper describes the creation and design of an online professional development course that aims to help educators in New Zealand to weave kaupapa Māori and e-Learning approaches together. The team sought to model weaving of kaupapa Māori and e-Learning approaches in how they worked together and the educational design for the material. The authors propose that educators with these skills are crucial if we are to effectively meet the needs of Māori learners, more than that, of all New Zealand learners, in the 21st century.

year: 2006-09