The aim of distributional semantics is to design computational techniques that can automatically learn the meanings of words from a body of text. The twin challenges are: how do we represent meaning, and how do we learn these representations? The current state of the art is to represent meanings as vectors - but vectors do not correspond to any traditional notion of meaning. In particular, there is no way to talk about 'truth', a crucial concept in logic and formal semantics. In this thesis, I develop a framework for distributional semantics which answers this challenge. The meaning of a word is not represented as a vector, but as a 'function', mapping entities (objects in the world) to probabilities of truth (the probability that the word is true of the entity). Such a function can be interpreted both in the machine learning sense of a classifier, and in the formal semantic sense of a truth-conditional function. This simultaneously allows both the use of machine learning techniques to exploit large datasets, and also the use of formal semantic techniques to manipulate the learnt representations. I define a probabilistic graphical model, which incorporates a probabilistic generalisation of model theory (allowing a strong connection with formal semantics), and which generates semantic dependency graphs (allowing it to be trained on a corpus). This graphical model provides a natural way to model logical inference, semantic composition, and context-dependent meanings, where Bayesian inference plays a crucial role. I demonstrate the feasibility of this approach by training a model on WikiWoods, a parsed version of the English Wikipedia, and evaluating it on three tasks. The results indicate that the model can learn information not captured by vector space models.

<p>In this thesis I have investigated verb frame divergences in a bilingual Head-driven Phrase Structure Grammar for machine translation. The purpose was threefold: (1) to describe and classify verb frame divergences (VFDs) between Swedish and English, (2) to practically implement a bilingual grammar that covered many of the identified VFDs and (3) to find out what cases of VFDs could be solved and implemented using a common semantic representation, or interlingua, for Swedish and English.</p><p>The implemented grammar, BiTSE, is a Head-driven Phrase Structure Grammar based on the LinGO Grammar Matrix, a language independent grammar base. BiTSE is a bilingual grammar containing both Swedish and English. The semantic representation used is Minimal Recursion Semantics (MRS). It is language independent, so generating from it gives all equivalent sentences in both Swedish and English. Both the core of the languages and a subset of the identified VFDs are successfully implemented in BiTSE. For other VFDs tentative solutions are discussed.</p><p>MRS have previously been proposed as suitable for semantic transfer machine translation. I have shown that VFDs can naturally be handled by an interlingual design in many cases, minimizing the need of transfer.</p><p>The main contributions of this thesis are: an inventory of English and Swedish verb frames and verb frame divergences; the bilingual grammar BiTSE and showing that it is possible in many cases to use MRS as an interlingua in machine translation.</p>

In this thesis I have investigated verb frame divergences in a bilingual Head-driven Phrase Structure Grammar for machine translation. The purpose was threefold: (1) to describe and classify verb frame divergences (VFDs) between Swedish and English, (2) to practically implement a bilingual grammar that covered many of the identified VFDs and (3) to find out what cases of VFDs could be solved and implemented using a common semantic representation, or interlingua, for Swedish and English. The implemented grammar, BiTSE, is a Head-driven Phrase Structure Grammar based on the LinGO Grammar Matrix, a language independent grammar base. BiTSE is a bilingual grammar containing both Swedish and English. The semantic representation used is Minimal Recursion Semantics (MRS). It is language independent, so generating from it gives all equivalent sentences in both Swedish and English. Both the core of the languages and a subset of the identified VFDs are successfully implemented in BiTSE. For other VFDs tentative solutions are discussed. MRS have previously been proposed as suitable for semantic transfer machine translation. I have shown that VFDs can naturally be handled by an interlingual design in many cases, minimizing the need of transfer. The main contributions of this thesis are: an inventory of English and Swedish verb frames and verb frame divergences; the bilingual grammar BiTSE and showing that it is possible in many cases to use MRS as an interlingua in machine translation.