This paper describes part of a solution to the interpretation of human-readable policy documents into semi-automatic conformance checking. Using a socio-cognitively motivated representation of shared knowledge, and applying appropriate inference mechanisms from a normative perspective, a mechanism to automatically detect potentially non-conforming blog entries is detailed. Candidate non-conforming blog entries are flagged for a human to make a judgement on whether they should be published. Analysis of data from a public corporate blog is analysed and results suggest the methodology has merit.

and other research outputs Integrating multiple windows and document features for expert finding

Word Sense Disambiguation (WSD) often relies on a context model or vector constructed from the words that co-occur with the target word within the same text windows. In most cases, a fixed-sized window is used, which is determined by trial and error. In addition, words within the same window are weighted uniformly regardless to their distance to the target word. Intuitively, it seems more reasonable to assign a stronger weight to context words closer to the target word. However, it is difficult to manually define the optimal weighting function based on distance. In this paper, we propose a unsupervised method for determining the optimal weights for context words according to their distance. The general idea is that the optimal weights should maximize the similarity of two context models of the target word generated from two random samples. This principle is applied to both English and Japanese. The context models using the resulting weights are used in WSD tasks on Semeval data. Our experimental results showed that substantial improvements in WSD accuracy can be obtained using the automatically defined weighting schema. 1

The problem of representing text documents within an Information Retrieval system is formulated as an analogy to the problem of representing the quantum states of a physical system. Lexical measurements of text are proposed as a way of representing documents which are akin to physical measurements on quantum states. Consequently, the representation of the text is only known after measurements have been made, and because the process of measuring may destroy parts of the text, the document is characterised through erasure. The mathematical foundations of such a quantum representation of text are provided in this position paper as a starting point for indexing and retrieval within a “quantum like ” Information Retrieval system.

and other research outputs A latent variable model for query expansion using the

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and other research outputs An aspect query language model based on query decomposition and high-order contextual term associations.

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