Constructing quality : the Hong Kong Housing Department journey

This paper explores the ‘journey’ along the ‘never ending quality road’ undertaken by the Hong Kong Housing Department over the last 15 years. It briefly covers the early history of public housing in Hong Kong, the catalytic effect brought about by the discovery of the infamous 26 sub-standard blocks in the mid-80s leading to the subsequent major improvements to process control and structural quality in the period 1985-1990. It then moves onto a discussion of initiatives taken since 1991, including the formation of the List of Building Contractors and the implementation of the Performance Assessment Scoring System (PASS). The paper ends with a discussion of the current status of quality issues within the Department and touches on future initiatives being developed to further enhance the quality of public housing in Hong Kong.

Calculating shades of meaning in semantic spaces

This thesis introduces the problem of conceptual ambiguity, or Shades of Meaning (SoM) that can exist around a term or entity. As an example consider President Ronald Reagan the ex-president of the USA, there are many aspects to him that are captured in text; the Russian missile deal, the Iran-contra deal and others. Simply finding documents with the word “Reagan” in them is going to return results that cover many different shades of meaning related to "Reagan". Instead it may be desirable to retrieve results around a specific shade of meaning of "Reagan", e.g., all documents relating to the Iran-contra scandal. This thesis investigates computational methods for identifying shades of meaning around a word, or concept. This problem is related to word sense ambiguity, but is more subtle and based less on the particular syntactic structures associated with or around an instance of the term and more with the semantic contexts around it. A particularly noteworthy difference from typical word sense disambiguation is that shades of a concept are not known in advance. It is up to the algorithm itself to ascertain these subtleties. It is the key hypothesis of this thesis that reducing the number of dimensions in the representation of concepts is a key part of reducing sparseness and thus also crucial in discovering their SoMwithin a given corpus.