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No description available.

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Statistical machine translation (SMT) should benefit from linguistic information to improve performance but current state-of-the-art models rely purely on data-driven models. There are several reasons why prior efforts to build linguistically annotated models have failed or not even been attempted. Firstly, the practical implementation often requires too much work to be cost effective. Where ad-hoc implementations have been created, they impose too strict constraints to be of general use. Lastly, many linguistically-motivated approaches are language dependent, tackling peculiarities in certain languages that do not apply to other languages. This thesis successfully integrates linguistic information about part-of-speech tags, lemmas and phrase structure to improve MT quality. The major contributions of this thesis are: 1. We enhance the phrase-based model to incorporate linguistic information as additional factors in the word representation. The factored phrase-based model allows us to make use of different types of linguistic information in a systematic way within the predefined framework. We show how this model improves translation by as much as 0.9 BLEU for small German-English training corpora, and 0.2 BLEU for larger corpora. 2. We extend the factored model to the factored template model to focus on improving reordering. We show that by generalising translation with part-of-speech tags, we can improve performance by as much as 1.1 BLEU on a small French- English system. 3. Finally, we switch from the phrase-based model to a syntax-based model with the mixed syntax model. This allows us to transition from the word-level approaches using factors to multiword linguistic information such as syntactic labels and shallow tags. The mixed syntax model uses source language syntactic information to inform translation. We show that the model is able to explain translation better, leading to a 0.8 BLEU improvement over the baseline hierarchical phrase-based model for a small German-English task. Also, the model requires only labels on continuous source spans, it is not dependent on a tree structure, therefore, other types of syntactic information can be integrated into the model. We experimented with a shallow parser and see a gain of 0.5 BLEU for the same dataset. Training with more training data, we improve translation by 0.6 BLEU (1.3 BLEU out-of-domain) over the hierarchical baseline. During the development of these three models, we discover that attempting to rigidly model translation as linguistic transfer process results in degraded performance. However, by combining the advantages of standard SMT models with linguistically-motivated models, we are able to achieve better translation performance. Our work shows the importance of balancing the specificity of linguistic information with the robustness of simpler models.

This thesis is motivated by the need for autonomous agents on the Semantic Web to be able to learn The Semantic Web is an effort for extending the existing Web with machine understandable information, thus enabling intelligent agents to understand the content of web-pages and help users carrying out tasks online. For such autonomous personal agents working on a world wide Semantic Web we make two observations. Firstly, every user is different and the Semantic Web will never cater for them all - - therefore, it is crucial for an agent to be able to learn from the user and the world around it to provide a personalised view of the web. Secondly, due to the immense amounts of information available on the world wide Semantic Web an agent cannot read and process all available data. We argue that to deal with the information overload a goal-directed approach is needed; an agent must be able to reason about the external world, the internal state and the actions available and only carry out the actions that help activate the current goal. In the first part of this thesis we explore the application of two machine learning techniques to Semantic Web data. Firstly, we investigate the classification of Semantic Web resources, we discuss the issues of mapping Semantic Web format to an input representation suitable for a selection of well-known algorithms, and outline the requirements for these algorithms to work well in a Semantic Web context. Secondly, we consider the clustering of Semantic Web resources. Here we focus on the definition of the similarity between two resources, and how we can determine what part of a large Semantic Web graph is relevant to a single resource. In the second part of the thesis we describe our goal-directed learning agent Smeagol. We present explicit definitions of the classification and clustering techniques devised in the first part of the thesis, allowing Smeagol to use a planning approach to create plans of actions that may fulfil a given top-level goal. We also investigate different ways that Smeagol can dynamically replan when steps within the initial plan fail and show that Smeagol can offer plausible learned answers to a given query, even when no explicit correct answer exists.

Global concerns about declining resources and climate change mean that industries must do their best to use energy as efficiently as possible. Energy is also an important component of a modern economy. The pulp and paper industry is one of the most energy-intensive industries round the world. In this study energy efficiency opportunities in pulp drying machines are identified and the saving potential is then quantified. The methodology followed was based on comparison of energy saving technologies and practices such as Turbo Vacuum blowers, Shoe Press, Heat pump, Use of low pressure steam etc. The data used for the calculations was taken from the mill data records. Six energy efficiency improvement projects were identified. The total proposed energy saving potential in the two Drying Machines studied in the thesis is 10511 MWh. Installation of the shoe press shows the highest saving potential followed by the turbo vacuum blowers. Although the accuracy of the results is heavily dependent upon the accuracy of the data records from the case study mill. The proposed savings would act as a reference point and depending upon the estimated savings potential, would help the mill to identify areas, projects that need more detailed measurements for further action.