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I examine the difficulties that several philosophers of language are liable to encounter in their attempts to provide an account of the connection between truth and assertion. I then attempt to provide an account of this connection. The analysis is concerned chiefly with difficulties which consist in elucidating the conceptual connection between truth and assertion in a way that respects certain linguistic intuitions while at the same time rendering the concept of truth amenable to a semantic interpretation. The proposed view suggests one way in which we might go about meeting the theoretical demands implicit in addressing this concern, among others, demonstrating the extent to which a theory of truth should be regarded as belonging to the province of epistemology. Insofar as semantical considerations figure into such a theory, a more systematic investigation of the interface between epistemology and natural language semantics is recommended. The solution to many problems at this interface, I argue, lay in an analysis of judgment.

This thesis tackles the problem of modeling the semantics of natural language. Neural Network models are reviewed and a new Bayesian approach is developed and evaluated. As the performance of standard Monte Carlo algorithms proofed to be unsatisfactory for the developed models, the main focus lies on a new adaptive algorithm from the Sequential Monte Carlo (SMC) family. The Gradient Importance Sampling (GRIS) algorithm developed in the thesis is shown to give very good performance as compared to many adaptive Markov Chain Monte Carlo (MCMC) algorithms on a range of complex target distributions. Another advantage as compared to MCMC is that GRIS provides a straight forward estimate of model evidence. Finally, Sample Inflation is introduced as a means to reduce variance and speed up mode finding in Importance Sampling and SMC algorithms. Sample Inflation provides provably consistent estimates and is empirically found to improve convergence of integral estimates. / Diese Dissertation befasst sich mit der Modellierung der Semantik natürlicher Sprache. Eine Übersicht von Neuronalen Netzwerkmodellen wird gegeben und ein eigener Bayesscher Ansatz wird entwickelt und evaluiert. Da die Leistungsfähigkeit von Standardalgorithmen aus der Monte-Carlo-Familie auf dem entwickelten Model unbefriedigend ist, liegt der Hauptfokus der Arbeit auf neuen adaptiven Algorithmen im Rahmen von Sequential Monte Carlo (SMC). Es wird gezeigt, dass der in der Dissertation entwickelte Gradient Importance Sampling (GRIS) Algorithmus sehr leistungsfähig ist im Vergleich zu vielen Algorithmen des adaptiven Markov Chain Monte Carlo (MCMC), wobei komplexe und hochdimensionale Integrationsprobleme herangezogen werden. Ein weiterer Vorteil im Vergleich mit MCMC ist, dass GRIS einen Schätzer der Modelevidenz liefert. Schließlich wird Sample Inflation eingeführt als Ansatz zur Reduktion von Varianz und schnellerem auffinden von Modi in einer Verteilung, wenn Importance Sampling oder SMC verwendet werden. Sample Inflation ist beweisbar konsistent und es wird empirisch gezeigt, dass seine Anwendung die Konvergenz von Integralschätzern verbessert.

Cette thèse développe un formalisme théorique pour la sémantique du discours. Il s'appuie sur l'extension des grammaires de Montague, sur la notion de continuation et sur les mécanismes de levée et de traitement des exceptions. Le formalisme permet de traiter des phénomènes dynamiques tels que les anaphores d'une phrase à l'autre, les présuppositions déclenchées par des référents et les projections présuppositions. / This thesis develops a theoretical formalism that takes into account semantical discourse dynamics. It focuses on the extension of Montague semantic with the notion of continuation and an exception handling and raising mechanism. The formalism allows to handle dynamic phenomena such as cross-sentential anaphora, presuppositions triggered by referring expressions and presupposition projection.

This thesis develops a theoretical formalism of formal semantics of natural language in the spirit of Montague semantics. The developed framework satisfies the principle of compositionality in a simple and elegant way, by being as parsimonious as possible: completely new formalisms or extensions of existing formalisms with even more complex constructions to fit particular linguistic phenomena have been avoided; instead, the framework handles these linguistic phenomena using only basic and well-established formalisms, such as simply-typed lambda calculus and classical logic. Dynamics is achieved by employing a continuation-passing technique and an exception raising and handling mechanism. The context is explicitly represented by a term, and, therefore, can be easily accessed and manipulated. The framework successfully handles cross-sentential anaphora and presuppositions triggered by referring expressions and has potential to be extended for dealing with more complex dynamic phenomena, such as presuppositions triggered by factive verbs and conversational implicatures.

The meaning of spatial words can only be evaluated by establishing a reference to the properties of the environment in which the word is used. For example, in order to evaluate what is to the left of something or how fast is fast in a given context, we need to evaluate properties such as the position of objects in the scene, their typical function and behaviour, the size of the scene and the perspective from which the scene is viewed. Rather than encoding the semantic rules that define spatial expressions by hand, we developed a system where such rules are learned from descriptions produced by human commentators and information that a mobile robot has about itself and its environment. We concentrate on two scenarios and words that are used in them. In the first scenario, the robot is moving in an enclosed space and the descriptions refer to its motion ('You're going forward slowly' and 'Now you're turning right'). In the second scenario, the robot is static in an enclosed space which contains real-size objects such as desks, chairs and walls. Here we are primarily interested in prepositional phrases that describe relationships between objects ('The chair is to the left of you' and 'The table is further away than the chair'). The perspective can be varied by changing the location of the robot. Following the learning stage, which is performed offline, the system is able to use this domain specific knowledge to generate new descriptions in new environments or to 'understand' these expressions by providing feedback to the user, either linguistically or by performing motion actions. If a robot can be taught to 'understand' and use such expressions in a manner that would seem natural to a human observer, then we can be reasonably sure that we have captured at least something important about their semantics. Two kinds of evaluation were performed. First, the performance of machine learning classifiers was evaluated on independent test sets using 10-fold cross-validation. A comparison of classifier performance (in regard to their accuracy, the Kappa coefficient (κ), ROC and Precision-Recall graphs) is made between (a) the machine learning algorithms used to build them, (b) conditions under which the learning datasets were created and (c) the method by which data was structured into examples or instances for learning. Second, with some additional knowledge required to build a simple dialogue interface, the classifiers were tested live against human evaluators in a new environment. The results show that the system is able to learn semantics of spatial expressions from low level robotic data. For example, a group of human evaluators judged that the live system generated a correct description of motion in 93.47% of cases (the figure is averaged over four categories) and that it generated the correct description of object relation in 59.28% of cases.