Knowledge discovery with CRF-based clustering of named entities without a priori classes

International audienceKnowledge discovery aims at bringing out coherent groups of entities. It is usually based on clustering which necessitates defining a notion of similarity between the relevant entities. In this paper, we propose to divert a supervised machine learning technique (namely Conditional Random Fields, widely used for supervised labeling tasks) in order to calculate, indirectly and without supervision, similarities among text sequences. Our approach consists in generating artificial labeling problems on the data to reveal regularities between entities through their labeling. We describe how this framework can be implemented and experiment it on two information extraction/discovery tasks. The results demonstrate the usefulness of this unsupervised approach, and open many avenues for defining similarities for complex representations of textual data

Simultaneous motion detection and background reconstruction with a conditional mixed-state markov random field

In this work we present a new way of simultaneously solving the problems of motion detection and background image reconstruction. An accurate estimation of the background is only possible if we locate the moving objects. Meanwhile, a correct motion detection is achieved if we have a good available background model. The key of our joint approach is to define a single random process that can take two types of values, instead of defining two different processes, one symbolic (motion detection) and one numeric (background intensity estimation). It thus allows to exploit the (spatio-temporal) interaction between a decision (motion detection) and an estimation (intensity reconstruction) problem. Consequently, the meaning of solving both tasks jointly, is to obtain a single optimal estimate of such a process. The intrinsic interaction and simultaneity between both problems is shown to be better modeled within the so-called mixed-state statistical framework, which is extended here to account for symbolic states and conditional random fields. Experiments on real sequences and comparisons with existing motion detection methods support our proposal. Further implications for video sequence inpainting will be also discussed. © 2011 Springer Science+Business Media, LLC.postprin