Colour in English: from metonymy to metaphor

Colour words abound with figurative meanings, expressing much more than visual signals. Some of these figurative properties are well known; in English, for example, black is associated with EVIL and blue with DEPRESSION. Colours themselves are also described in metaphorical terms using lexis from other domains of experience, such as when we talk of deep blue, drawing on the domain of spatial position. Both metaphor and colour are of central concern to semantic theory; moreover, colour is recognised as a highly productive metaphoric field. Despite this, comparatively few works have dealt with these topics in unison, and even those few have tended to focus on Basic Colour Terms (BCTs) rather than including non-BCTs. This thesis addresses the need for an integrated study of both BCTs and non-BCTs, and provides an overview of metaphor and metonymy within the semantic area of colour. Conducted as part of the Mapping Metaphor project, this research uses the unique data source of the Historical Thesaurus of English (HT) to identify areas of meaning that share vocabulary with colour and thus point to figurative uses. The lexicographic evidence is then compared to current language use, found in the British National Corpus (BNC) and the Corpus of Contemporary American (COCA), to test for currency and further developments or changes in meaning. First, terms for saturation, tone and brightness are discussed. This lexis often functions as hue modifiers and is found to transfer into COLOUR from areas such as LIFE, EMOTION, TRUTH and MORALITY. The evidence for cross-modal links between COLOUR with SOUND, TOUCH and DIMENSION is then presented. Each BCT is discussed in turn, along with a selection of non-BCTs, where it is revealed how frequently hue terms engage in figurative meanings. This includes the secondary BCTs, with the only exception being orange, and a number of non-BCTs. All of the evidence discussed confirms that figurative uses of colour originate through a process of metonymy, although these are often extended into metaphor.

Using interaction data for improving the offline and online evaluation of search engines

This thesis investigates how web search evaluation can be improved using historical interaction data. Modern search engines combine offline and online evaluation approaches in a sequence of steps that a tested change needs to pass through to be accepted as an improvement and subsequently deployed. We refer to such a sequence of steps as an evaluation pipeline. In this thesis, we consider the evaluation pipeline to contain three sequential steps: an offline evaluation step, an online evaluation scheduling step, and an online evaluation step. In this thesis we show that historical user interaction data can aid in improving the accuracy or efficiency of each of the steps of the web search evaluation pipeline. As a result of these improvements, the overall efficiency of the entire evaluation pipeline is increased. Firstly, we investigate how user interaction data can be used to build accurate offline evaluation methods for query auto-completion mechanisms. We propose a family of offline evaluation metrics for query auto-completion that represents the effort the user has to spend in order to submit their query. The parameters of our proposed metrics are trained against a set of user interactions recorded in the search engine’s query logs. From our experimental study, we observe that our proposed metrics are significantly more correlated with an online user satisfaction indicator than the metrics proposed in the existing literature. Hence, fewer changes will pass the offline evaluation step to be rejected after the online evaluation step. As a result, this would allow us to achieve a higher efficiency of the entire evaluation pipeline. Secondly, we state the problem of the optimised scheduling of online experiments. We tackle this problem by considering a greedy scheduler that prioritises the evaluation queue according to the predicted likelihood of success of a particular experiment. This predictor is trained on a set of online experiments, and uses a diverse set of features to represent an online experiment. Our study demonstrates that a higher number of successful experiments per unit of time can be achieved by deploying such a scheduler on the second step of the evaluation pipeline. Consequently, we argue that the efficiency of the evaluation pipeline can be increased. Next, to improve the efficiency of the online evaluation step, we propose the Generalised Team Draft interleaving framework. Generalised Team Draft considers both the interleaving policy (how often a particular combination of results is shown) and click scoring (how important each click is) as parameters in a data-driven optimisation of the interleaving sensitivity. Further, Generalised Team Draft is applicable beyond domains with a list-based representation of results, i.e. in domains with a grid-based representation, such as image search. Our study using datasets of interleaving experiments performed both in document and image search domains demonstrates that Generalised Team Draft achieves the highest sensitivity. A higher sensitivity indicates that the interleaving experiments can be deployed for a shorter period of time or use a smaller sample of users. Importantly, Generalised Team Draft optimises the interleaving parameters w.r.t. historical interaction data recorded in the interleaving experiments. Finally, we propose to apply the sequential testing methods to reduce the mean deployment time for the interleaving experiments. We adapt two sequential tests for the interleaving experimentation. We demonstrate that one can achieve a significant decrease in experiment duration by using such sequential testing methods. The highest efficiency is achieved by the sequential tests that adjust their stopping thresholds using historical interaction data recorded in diagnostic experiments. Our further experimental study demonstrates that cumulative gains in the online experimentation efficiency can be achieved by combining the interleaving sensitivity optimisation approaches, including Generalised Team Draft, and the sequential testing approaches. Overall, the central contributions of this thesis are the proposed approaches to improve the accuracy or efficiency of the steps of the evaluation pipeline: the offline evaluation frameworks for the query auto-completion, an approach for the optimised scheduling of online experiments, a general framework for the efficient online interleaving evaluation, and a sequential testing approach for the online search evaluation. The experiments in this thesis are based on massive real-life datasets obtained from Yandex, a leading commercial search engine. These experiments demonstrate the potential of the proposed approaches to improve the efficiency of the evaluation pipeline.