Accelerating the parsing process with an Application Specific VLSI RISC processor

This thesis investigates the topic of the design, implementation and potential use of specialised hardware used to accelerate the recognition and translation of computer programs expressed in a range of computer languages. This investigation focuses specifically on the twin processes of parsing and lexical analysis. The research described was carried out in two areas namely, the feasibility of designing a specialised instruction set for a RISC like processor able to accelerate the parsing and lexical analysis process, and the physical implementation of a RISC processor in CMOS VLSI technology able to execute the designed instruction set. The feasibility of mapping the process of language recognition onto the instruction set of a RISC processor is investigated. This involves an assessment of the suitability of the LL(1) and LALR(1) algorithms, both of which are used for parsing, and other associated algorithms, used for lexical analysis, as a basis for an appropriate instruction set architecture. The feasibility of an instruction set design which uses fixed size instructions with variable size data fields to ensure scaleable operation is also investigated. The appropriate software mechanisms used to validate the instruction set architecture are outlined. The practical implementation using CMOS technology of a RISC processor able to execute the new instruction set is investigated. In particular the feasibility of using bit-slice technology to implement the processor having fixed size instructions with variable size data-paths and address ranges is investigated. The combination of novel instruction set with variable data-widths and the fabricated devices able to activate semantic actions directly from hardware together form an original contribution to the field of parsing and lexical analysis

Knowledge Expansion of a Statistical Machine Translation System using Morphological Resources

Translation capability of a Phrase-Based Statistical Machine Translation (PBSMT) system mostly depends on parallel data and phrases that are not present in the training data are not correctly translated. This paper describes a method that efficiently expands the existing knowledge of a PBSMT system without adding more parallel data but using external morphological resources. A set of new phrase associations is added to translation and reordering models; each of them corresponds to a morphological variation of the source/target/both phrases of an existing association. New associations are generated using a string similarity score based on morphosyntactic information. We tested our approach on En-Fr and Fr-En translations and results showed improvements of the performance in terms of automatic scores (BLEU and Meteor) and reduction of out-of-vocabulary (OOV) words. We believe that our knowledge expansion framework is generic and could be used to add different types of information to the model.JRC.G.2-Global security and crisis managemen

The pragmatic formalization of computing systems relative to a given high-level language

Imperial Users onl

Corpus and sentiment analysis

EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Students´ language in computer-assisted tutoring of mathematical proofs

Truth and proof are central to mathematics. Proving (or disproving) seemingly simple statements often turns out to be one of the hardest mathematical tasks. Yet, doing proofs is rarely taught in the classroom. Studies on cognitive difficulties in learning to do proofs have shown that pupils and students not only often do not understand or cannot apply basic formal reasoning techniques and do not know how to use formal mathematical language, but, at a far more fundamental level, they also do not understand what it means to prove a statement or even do not see the purpose of proof at all. Since insight into the importance of proof and doing proofs as such cannot be learnt other than by practice, learning support through individualised tutoring is in demand. This volume presents a part of an interdisciplinary project, set at the intersection of pedagogical science, artificial intelligence, and (computational) linguistics, which investigated issues involved in provisioning computer-based tutoring of mathematical proofs through dialogue in natural language. The ultimate goal in this context, addressing the above-mentioned need for learning support, is to build intelligent automated tutoring systems for mathematical proofs. The research presented here has been focused on the language that students use while interacting with such a system: its linguistic propeties and computational modelling. Contribution is made at three levels: first, an analysis of language phenomena found in students´ input to a (simulated) proof tutoring system is conducted and the variety of students´ verbalisations is quantitatively assessed, second, a general computational processing strategy for informal mathematical language and methods of modelling prominent language phenomena are proposed, and third, the prospects for natural language as an input modality for proof tutoring systems is evaluated based on collected corpora

Inquiries into words, constraints and contexts : Festschrift in the honour of Kimmo Koskenniemi on his 60th birthday

Peer reviewe

Predicting the approximate functional behaviour of physical systems

This dissertation addresses the problem of the computer prediction of the approximate behaviour of physical systems describable by ordinary differential equations.Previous approaches to behavioural prediction have either focused on an exact mathematical description or on a qualitative account. We advocate a middle ground: a representation more coarse than an exact mathematical solution yet more specific than a qualitative one. What is required is a mathematical expression, simpler than the exact solution, whose qualitative features mirror those of the actual solution and whose functional form captures the principal parameter relationships underlying the behaviour of the real system. We term such a representation an approximate functional solution.Approximate functional solutions are superior to qualitative descriptions because they reveal specific functional relationships, restore a quantitative time scale to a process and support more sophisticated comparative analysis queries. Moreover, they can be superior to exact mathematical solutions by emphasizing comprehensibility, adequacy and practical utility over precision.Two strategies for constructing approximate functional solutions are proposed. The first abstracts the original equation, predicts behaviour in the abstraction space and maps this back to the approximate functional level. Specifically, analytic abduction exploits qualitative simulation to predict the qualitative properties of the solution and uses this knowledge to guide the selection of a parameterized trial function which is then tuned with respect to the differential equation. In order to limit the complexity of a proposed approximate functional solution, and hence maintain its comprehensibility, back-of-the-envelope reasoning is used to simplify overly complex expressions in a magnitude extreme. If no function is recognised which matches the predicted behaviour, segment calculus is called upon to find a composite function built from known primitives and a set of operators. At the very least, segment calculus identifies a plausible structure for the form of the solution (e.g. that it is a composition of two unknown functions). Equation parsing capitalizes on this partial information to look for a set of termwise interactions which, when interpreted, expose a particular solution of the equation.The second, and more direct, strategy for constructing an approximate functional solution is embodied in the closed form approximation technique. This extends approximation methods to equations which lack a closed form solution. This involves solving the differential equation exactly, as an infinite series, and obtaining an approximate functional solution by constructing a closed form function whose Taylor series is close to that of the exact solutionThe above techniques dovetail together to achieve a style of reasoning closer to that of an engineer or physicist rather than a mathematician. The key difference being to sacrifice the goal of finding the correct solution of the differential equation in favour of finding an approximation which is adequate for the purpose to which the knowledge will be put. Applications to Intelligent Tutoring and Design Support Systems are suggested

User modelling for robotic companions using stochastic context-free grammars

Creating models about others is a sophisticated human ability that robotic companions need to develop in order to have successful interactions. This thesis proposes user modelling frameworks to personalise the interaction between a robot and its user and devises novel scenarios where robotic companions may apply these user modelling techniques. We tackle the creation of user models in a hierarchical manner, using a streamlined version of the Hierarchical Attentive Multiple-Models for Execution and Recognition (HAMMER) architecture to detect low-level user actions and taking advantage of Stochastic Context-Free Grammars (SCFGs) to instantiate higher-level models which recognise uncertain and recursive sequences of low-level actions. We discuss a couple of distinct scenarios for robotic companions: a humanoid sidekick for power-wheelchair users and a companion of hospital patients. Next, we address the limitations of the previous scenarios by applying our user modelling techniques and designing two further scenarios that fully take advantage of the user model. These scenarios are: a wheelchair driving tutor which models the user abilities, and the musical collaborator which learns the preferences of its users. The methodology produced interesting results in all scenarios: users preferred the actual robot over a simulator as a wheelchair sidekick. Hospital patients rated positively their interactions with the companion independently of their age. Moreover, most users agreed that the music collaborator had become a better accompanist with our framework. Finally, we observed that users' driving performance improved when the robotic tutor instructed them to repeat a task. As our workforce ages and the care requirements in our society grow, robots will need to play a role in helping us lead better lives. This thesis shows that, through the use of SCFGs, adaptive user models may be generated which then can be used by robots to assist their users.Open Acces