We describe and evaluate hidden understanding models, a statistical learning approach to natural language understanding. Given a string of words, hidden understanding models determine the most likely meaning for the string. We discuss 1) the problem of representing meaning in this framework, 2) the structure of the statistical model, 3) the process of training the model, and 4) the process of u...

Performance in speech recognition systems has progressed to the point where it is now realistic to begin integrating speech with natural language systems to produce spoken language systems. Two factors have contributed to the advances in speech: statistical modeling of the input signal and language constraints. To produce spoken language systems, then, the grammar formalisms used in natural lan...

The novelty task consists of finding relevant and novel sentences in a ranking of documents given a query. In the literature, different techniques have been applied to address this problem. Nevertheless, little is known about Language Models for novelty detection and, especially, the effect of smoothing on the selection of novel sentences. Language Models can be used to study novelty and releva...

We describe the use of energy function optimisation in very shallow syntactic parsing. The approach can use linguistic rules and corpus-based statistics, so the strengths of both linguistic and statistical approaches to NLP can be combined in a single framework. The rules are contextual constraints for resolving syntactic ambiguities expressed as alternative tags, and the statistical language m...

In this paper, we describe improved alignment models for statistical machine translation. The statistical translation approach uses two types of information: a translation model and a language model. The language model used is a bigram or general m-gram model. The translation model is decomposed into a lexical and an alignment model. We describe two different approaches for statistical translat...

A new system for statistical natural language translation for languages with similar grammar is introduced. Specifically, it can be used with Romanic Languages, such as French, Spanish or Catalan. The statistical translation uses two sources of information: a language model and a translation model. The language model used is a standard trigram model. A new approach is defined in the translation...

Abstract: We present an overview of (geo-)statistical models, methods and techniques for the analysis and prediction of continuous spatio-temporal processes residing in continuous space. Various approaches exist for building statistical models for such processes, estimating their parameters and performing predictions. We cover the Gaussian process approach, very common in spatial statistics and...

We present a new statistical language model based on a combination of individual word language models. Each word model is built from an individual corpus which is formed by extracting those subsets of the entire training corpus which contain that significant word. We also present a novel way of combining language models called the “union model”, based on a logical union of intersections, and us...

In this paper we investigate whether more accurate modeling of differences in language in different types of conversations, e.g. formal presentations vs. spontaneous conversations can improve the quality of a language model. We also investigate whether the modeling of sentence lengths can improve a language model. A language model is an important component of statistical natural language proces...