Safety and health in mines: the rise of bureaucracy in the coal mining industry and its results as evidenced by the public enquiry into an accident at Houghton Main Colliery

This thesis is concerned with certain aspects of the Public Inquiry into the accident at Houghton Main Colliery in June 1975. It examines whether prior to the accident there existed at the Colliery a situation in which too much reliance was being placed upon state regulation and too Iittle upon personal responsibility. I study the phenomenon of state regulation. This is done (a) by analysis of selected writings on state regulation/intervention/interference/bureaucracy (the words are used synonymously) over the last two hundred years, specifically those of Marx on the 1866 Committee on Mines, and (b) by studying Chadwick and Tremenheere, leading and contrasting "bureaucrats" of the mid-nineteenth century. The bureaucratisation of the mining industry over the period 1835-1954 is described, and it is demonstrated that the industry obtained and now possesses those characteristics outlined by Max Weber in his model of bureaucracy. I analyse criticisms of the model and find them to be relevant, in that they facilitate understanding both of the circumstances of the accident and of the Inquiry . Further understanding of the circumstances and causes of the accident was gained by attendance at the lnquiry and by interviewing many of those involved in the Inquiry. I analyse many aspects of the Inquiry - its objectives. structure, procedure and conflicting interests - and find that, although the Inquiry had many of the symbols of bureaucracy, it suffered not from " too much" outside interference. but rather from the coal mining industry's shared belief in its ability to solve its own problems. I found nothing to suggest that, prior to the accident, colliery personnel relied. or were encouraged to rely, "too much" upon state regulation.

Extracting protein-protein interactions from MEDLINE using the hidden vector state model

A major challenge in text mining for biomedicine is automatically extracting protein-protein interactions from the vast amount of biomedical literature. We have constructed an information extraction system based on the Hidden Vector State (HVS) model for protein-protein interactions. The HVS model can be trained using only lightly annotated data whilst simultaneously retaining sufficient ability to capture the hierarchical structure. When applied in extracting protein-protein interactions, we found that it performed better than other established statistical methods and achieved 61.5% in F-score with balanced recall and precision values. Moreover, the statistical nature of the pure data-driven HVS model makes it intrinsically robust and it can be easily adapted to other domains.

Ontology-based protein-protein interactions extraction from literature using the hidden vector state model

This paper proposes a novel framework of incorporating protein-protein interactions (PPI) ontology knowledge into PPI extraction from biomedical literature in order to address the emerging challenges of deep natural language understanding. It is built upon the existing work on relation extraction using the Hidden Vector State (HVS) model. The HVS model belongs to the category of statistical learning methods. It can be trained directly from un-annotated data in a constrained way whilst at the same time being able to capture the underlying named entity relationships. However, it is difficult to incorporate background knowledge or non-local information into the HVS model. This paper proposes to represent the HVS model as a conditionally trained undirected graphical model in which non-local features derived from PPI ontology through inference would be easily incorporated. The seamless fusion of ontology inference with statistical learning produces a new paradigm to information extraction.

Joint sentiment/topic model for sentiment analysis

Sentiment analysis or opinion mining aims to use automated tools to detect subjective information such as opinions, attitudes, and feelings expressed in text. This paper proposes a novel probabilistic modeling framework based on Latent Dirichlet Allocation (LDA), called joint sentiment/topic model (JST), which detects sentiment and topic simultaneously from text. Unlike other machine learning approaches to sentiment classification which often require labeled corpora for classifier training, the proposed JST model is fully unsupervised. The model has been evaluated on the movie review dataset to classify the review sentiment polarity and minimum prior information have also been explored to further improve the sentiment classification accuracy. Preliminary experiments have shown promising results achieved by JST.