Bayesian Information Extraction Network

Dynamic Bayesian networks (DBNs) offer an elegant way to integrate various aspects of language in one model. Many existing algorithms developed for learning and inference in DBNs are applicable to probabilistic language modeling. To demonstrate the potential of DBNs for natural language processing, we employ a DBN in an information extraction task. We show how to assemble wealth of emerging linguistic instruments for shallow parsing, syntactic and semantic tagging, morphological decomposition, named entity recognition etc. in order to incrementally build a robust information extraction system. Our method outperforms previously published results on an established benchmark domain.Comment: 6 page

Identifiability of parameters in latent structure models with many observed variables

While hidden class models of various types arise in many statistical applications, it is often difficult to establish the identifiability of their parameters. Focusing on models in which there is some structure of independence of some of the observed variables conditioned on hidden ones, we demonstrate a general approach for establishing identifiability utilizing algebraic arguments. A theorem of J. Kruskal for a simple latent-class model with finite state space lies at the core of our results, though we apply it to a diverse set of models. These include mixtures of both finite and nonparametric product distributions, hidden Markov models and random graph mixture models, and lead to a number of new results and improvements to old ones. In the parametric setting, this approach indicates that for such models, the classical definition of identifiability is typically too strong. Instead generic identifiability holds, which implies that the set of nonidentifiable parameters has measure zero, so that parameter inference is still meaningful. In particular, this sheds light on the properties of finite mixtures of Bernoulli products, which have been used for decades despite being known to have nonidentifiable parameters. In the nonparametric setting, we again obtain identifiability only when certain restrictions are placed on the distributions that are mixed, but we explicitly describe the conditions.Comment: Published in at http://dx.doi.org/10.1214/09-AOS689 the Annals of Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical Statistics (http://www.imstat.org

Studying Media Events through Spatio-Temporal Statistical Analysis

This report is written in the context of the ANR Geomedia and summarises the developement of methods of spatio-temporel statistical analysis of media events (delivrable 3.2).This documents presents on-going work on statistical modelling and statistical inference of the ANR GEOMEDIA corpus, that is a collection of international RSS news feeds. Central to this project, RSS news feeds are viewed as a representation of the information flow in geopolitical space. As such they allow us to study media events of global extent and how they affect international relations. Here we propose hidden Markov models (HMM) as an adequate modelling framework to study the evolution of media events in time. This set of models respect the characteristic properties of the data, such as temporal dependencies and correlations between feeds. Its specific structure corresponds well to our conceptualisation of media attention and media events. We specify the general model structure that we use for modelling an ensemble of RSS news feeds. Finally, we apply the proposed models to a case study dedicated to the analysis of the media attention for the Ebola epidemic which spread through West Africa in 2014.Ce document présente les résultats d'un travail en cours sur la modélisation statistique et l'inférence appliqué au corpus de l'ANR GEOMEDIA qui est une collection des flux RSS internationaux. Au coeur du projet, les flux RSS sont considérés comme un marqueur représentatif des flux d'information dans l'espace géopolitique mondial. En tant que tel, ils nous permettent d'étudier des événements médiatiques globaux et leur impact sur les relations internationales. Dans ce contexte, on émet l'hypothèse que les modèles Markoviens cachés (HMM) constituent un cadre méthodologique adapté pour modéliser et étudier l'évolution des événements médiatiques dans le temps. Ces modèles respectent les propriétés des données, comme les corrélations temporelles et les redondances entre flux. Leur structure caractéristique correspond à notre conceptualisation de l'attention médiatique et des événements médiatiques. Nous spécifions la structure général d'un modèle HMM qui peut être appliqué a la modélisation simultané d'un ensemble des flux RSS. Finalement, on teste l'intérêt des modèles proposés à l'aide d'une étude de cas dédié à l'analyse de l'attention médiatique pour l'épidémie d'Ebola en Afrique de l'Ouest en 2014