characterize how a user relates his query to the document that he intends to retrieve using a noisy channel model. We model a user profile as the probabilities of translation of query to document in this noisy channel using the relevance feedback obtained from the user. The user profile thus learnt is applied

In this paper, we present our linguisticallyaugmented statistical machine translation model from Bulgarian to English, which combines a statistical machine translation (SMT) system (as backbone) with deep linguistic features (as factors). The motivation is to take advantages of the robustness

This paper presents a set of experiments on Domain Adaptation of Statistical Machine Translation systems. The experiments focus on Chinese-English and two domain-specific corpora. The paper presents a novel approach for combining multiple domain-trained translation models to achieve improved

describes a synthetic synchronous grammar (SSG), which tentatively in this paper, integrates a synchronous context-free grammar (SCFG) and a synchronous tree sequence substitution grammar (STSSG) for statistical machine translation. The experimental results on NIST MT05 Chinese-to-English test set show

This paper proposes the use of a Deterministic Annealing Expectation-Maximization (DAEM) algorithm to estimate the wordalignments involved in the statistical translation process. This approach is aimed to overcome the problem of the local maxima in complex alignment models, thus making unnecessary

In recent years, the quantity of parallel training data available for statistical machine translation has increased far more rapidly than the performance of individual computers, resulting in a potentially serious impediment to progress. Parallelization of the modelbuilding algorithms that process

At this point, I would like to express my gratitude to all the people who supported and accompanied me during the progress of this work. First, I would like to thank my advisor Professor Dr.-Ing. Hermann Ney, head of the Chair of Computer Science 6 at the RWTH Aachen University. This thesis would not have been possible without his advice, continuous interest and support. I would also like to thank Professor Dr. Francisco Casacuberta from the Universidad Politecnica de Valencia for agreeing to review this thesis and for his interest in this work. All the people at the Chair of Computer Science 6 deserve my gratitude for many fruitful discussions, helpful feedback, and for the very good working atmosphere. I want to thank all those who helped me when writing this thesis by proofreading it, pointing out bad formulations and requesting clarifications. Furthermore, I would like to thank the secretaries and the system administrators for their continuous support. I am very thankful for the friendly atmosphere and the support I received at the Advanced Research Institute International, Kyoto, Japan during my stay in 2003. It was a very interesting and valuable experience. I would also like to thank all the people who made the CLSP summer research workshop on the open source SMT toolkit ”Moses ” possible: the organizers from CLSP/JHU and all members of both teams. It was a productive and fun environment. Grosser Dank gilt meinen Eltern, die mir das Studium der Informatik ermöglicht haben. Desweiteren möchte ich mich bei meiner Familie und Freunden für den angenehmen Ausgleich

Often, the training procedure for statistical machine translation models is based on maximum likelihood or related criteria. A general problem of this approach is that there is only a loose relation to the final translation quality on unseen text. In this paper, we analyze various training

Maximum entropy (ME) models has been successfully applied to many natural language problems. In this paper we present how to integrate efficiently ME models within a maximum likelihood trainig scheme of statistical machine translation models. Specifically, we define a set of context

This work proposes to adapt an existing general SMT model for the task of translating queries that are subsequently going to be used to retrieve information from a target language collection. In the scenario that we focus on access to the document collection itself is not available and changes