Supplementary information : weighted tree kernels for sequence analysis

Genomic sequences are fundamentally text documents, admitting various representations according to need and tokenization. Gene expression depends crucially on binding of enzymes to the DNA sequence at small, poorly conserved binding sites, limiting the utility of standard pattern search. However, one may exploit the regular syntactic structure of the enzyme's component proteins and the corresponding binding sites, framing the problem as one of detecting grammatically correct genomic phrases. In this paper we propose new kernels based on weighted tree structures, traversing the paths within them to capture the features which underpin the task. Experimentally, we and that these kernels provide performance comparable with state of the art approaches for this problem, while offering significant computational advantages over earlier methods. The methods proposed may be applied to a broad range of sequence or tree-structured data in molecular biology and other domains.

Are we there yet?

The political and bureaucratic discourse surrounding non-profit sector reform is centred on streamlining the regulatory framework. Phrases such as 'one-stop shop','reducing red tape' and 'duplicative, burdensome and unclear requirements' fill press releases, government reports and discussion papers. In this chapter, I examine quantitative measures of the current regulatory compliance burden facing non-profit organisations in Australia as a benchmark for measuring progress over the coming years. I focus on regulatory compliance estimates for four key stages of non-profit enterprise activity non-profit enterprise start-up and registrations; fundraising;grant paperwork; and regulation proportionate to the size of the non-profit enterprise.

Just dawn

Commissioned for the It’s Timely exhibition at the Blacktown Arts Centre, Just Dawn is a response to two speeches that former Australian Prime Minister Gough Whitlam delivered in Blacktown in 1972 and 1974. Throughout the video, a series of white words and phrases fade in and out as a virtual camera flies towards an abstract horizon line. The narrative thread of the text is directed towards an unnamed Whitlam through the repeated appearance of the words ‘you said’. As the video progresses, the colours of the animated background slowly brighten to resemble an emerging dawn, and the sound, text and camera movements build in frequency and intensity. As they do so, the once optimistic outlook becomes increasingly unsteady. In these ways, Just Dawn is equal parts homage and lament for the ideological acuity and ambition of Whitlam’s agenda. It explores how Whitlam’s words can become markers for the complexities of both his own specific transformative policies, and the character of the socially progressive movement more broadly.

Thoughts make the world

Thoughts Make the World is a synchronised two-channel video with sound. On the right-hand screen, a young man and woman exchange timeworn philosophical phrases and existential questions. On the left-hand screen, a guitarist plays a soundtrack that slowly builds over time. As the actors’ quest for meaning struggles toward an unresolved end, the guitarist launches into a climactic, liberating solo, eclipsing their somewhat-labored attempts to understand existence. By contrasting these verbal and non-verbal signifiers of self-reflection and self-expression, Thoughts Make the World questions how and where to grapple with enduring existential problems in a context dominated by the pre-packaged formats of popular culture and ironic modes of individualised response.

Injury narrative text classification : a preliminary study

Description of a patient's injuries is recorded in narrative text form by hospital emergency departments. For statistical reporting, this text data needs to be mapped to pre-defined codes. Existing research in this field uses the Naïve Bayes probabilistic method to build classifiers for mapping. In this paper, we focus on providing guidance on the selection of a classification method. We build a number of classifiers belonging to different classification families such as decision tree, probabilistic, neural networks, and instance-based, ensemble-based and kernel-based linear classifiers. An extensive pre-processing is carried out to ensure the quality of data and, in hence, the quality classification outcome. The records with a null entry in injury description are removed. The misspelling correction process is carried out by finding and replacing the misspelt word with a soundlike word. Meaningful phrases have been identified and kept, instead of removing the part of phrase as a stop word. The abbreviations appearing in many forms of entry are manually identified and only one form of abbreviations is used. Clustering is utilised to discriminate between non-frequent and frequent terms. This process reduced the number of text features dramatically from about 28,000 to 5000. The medical narrative text injury dataset, under consideration, is composed of many short documents. The data can be characterized as high-dimensional and sparse, i.e., few features are irrelevant but features are correlated with one another. Therefore, Matrix factorization techniques such as Singular Value Decomposition (SVD) and Non Negative Matrix Factorization (NNMF) have been used to map the processed feature space to a lower-dimensional feature space. Classifiers with these reduced feature space have been built. In experiments, a set of tests are conducted to reflect which classification method is best for the medical text classification. The Non Negative Matrix Factorization with Support Vector Machine method can achieve 93% precision which is higher than all the tested traditional classifiers. We also found that TF/IDF weighting which works well for long text classification is inferior to binary weighting in short document classification. Another finding is that the Top-n terms should be removed in consultation with medical experts, as it affects the classification performance.

A probabilistic framework for analysing the compositionality of conceptual combinations

Conceptual combination performs a fundamental role in creating the broad range of compound phrases utilised in everyday language. While the systematicity and productivity of language provide a strong argument in favour of assuming compositionality, this very assumption is still regularly questioned in both cognitive science and philosophy. This article provides a novel probabilistic framework for assessing whether the semantics of conceptual combinations are compositional, and so can be considered as a function of the semantics of the constituent concepts, or not. Rather than adjudicating between different grades of compositionality, the framework presented here contributes formal methods for determining a clear dividing line between compositional and non-compositional semantics. Compositionality is equated with a joint probability distribution modelling how the constituent concepts in the combination are interpreted. Marginal selectivity is emphasised as a pivotal probabilistic constraint for the application of the Bell/CH and CHSH systems of inequalities (referred to collectively as Bell-type). Non-compositionality is then equated with either a failure of marginal selectivity, or, in the presence of marginal selectivity, with a violation of Bell-type inequalities. In both non-compositional scenarios, the conceptual combination cannot be modelled using a joint probability distribution with variables corresponding to the interpretation of the individual concepts. The framework is demonstrated by applying it to an empirical scenario of twenty-four non-lexicalised conceptual combinations.

Find my office: Navigating real space from semantic descriptions

This paper shows that by using only symbolic language phrases, a mobile robot can purposefully navigate to specified rooms in previously unexplored environments. The robot intelligently organises a symbolic language description of the unseen environment and “imagines” a representative map, called the abstract map. The abstract map is an internal representation of the topological structure and spatial layout of symbolically defined locations. To perform goal-directed exploration, the abstract map creates a high-level semantic plan to reason about spaces beyond the robot’s known world. While completing the plan, the robot uses the metric guidance provided by a spatial layout, and grounded observations of door labels, to efficiently guide its navigation. The system is shown to complete exploration in unexplored spaces by travelling only 13.3% further than the optimal path.

Dialog

This work, commissioned by Campbelltown Arts Centre, was created as part of an online residency in Western Sydney. The residency took the form of an online survey of students in Western Sydney schools that queried participants on their favourite moments, characters and dialogue from film and television. Using this information as a starting point, the work used appropriated footage to weave together a 6-way cross-screen conversation. Spouting occasionally recognizable phrases that then devolve into meaningless cliché, the narrative content of this fragmented back-and-forth hovers somewhere in between familiarity and non-sense.