Alex Spengler

Research Interests

Machine learning and (Bayesian) statistics, information extraction & visualization.

Short Biography

Alex Spengler studied Computer Science and Mathematics in Karlsruhe, Edinburgh and Paris. In April 2006 he began his PhD under the supervision of Professor Patrick Gallinari from the Laboratoire d'Informatique at Université Pierre et Marie Curie, Paris, France and Professor Bernhard Schölkopf from the Max Planck Institute for Biological Cybernetics, Tübingen, Germany. His research interests are centered on the intersection between machine learning and information extraction, focussing on structured prediction for document transformation.

Publications

Spengler, Alex and Gallinari, Patrick (2009): Learning to Extract Content from News Webpages. The 2009 IEEE International Symposium on Mining and Web (MAW2009). Bradford, England, UK.

We consider the problem of content extraction from online news webpages. To explore to what extent the syntactic markup and the visual structure of a webpage facilitate the extraction of its content, we compare two state-of-the-art classifiers as first instantiations of a general framework that allows for proper model comparison. To this end, we introduce the publicly available News600 corpus, a set of 604 real world news webpages which have been annotated with 30 semantic labels. An empirical analysis of the two models on this dataset shows that the inclusion of structural information is indeed advantageous.

@article{spengler2009learning,
     author = {Alex Spengler and Patrick Gallinari},
     title = {Learning to Extract Content from News Webpages},
     journal ={Advanced Information Networking and Applications Workshops, International Conference on},
     volume = {0},
     year = {2009},
     isbn = {978-0-7695-3639-2},
     pages = {709-714},
     doi = {http://doi.ieeecomputersociety.org/10.1109/WAINA.2009.97},
     publisher = {IEEE Computer Society},
     address = {Los Alamitos, CA, USA},
}
Spengler, Alex (2005): Maximum Margin Markov Networks for XML Tag Relabelling. MSc thesis, University of Karlsruhe (TH), Germany.

This thesis explores a number of different discriminative models for learning to label the segments of semi-structured documents. This is interesting as it allows to query heterogeneous data collections using predefined, homogeneous segment semantics. We focus on the recently introduced M3N framework, a parameter estimation technique for models with interdependent outputs which maximises the margin of the decision boundary. An instance of this framework, similar to Sequential Minimal Optimisation, is discussed. We hypothesise that this instance outperforms other models such as conditional random fields and multiclass Support Vector Machines. Experiments on a XML relabelling task underpin that this is indeed the case. Although high-dimensional output spaces and large tree-width Markov networks remain problematic, this is a highly promising result, indicating that these algorithms render maximum margin Markov networks (M3Ns) more than theoretically sound and probabilistically principled.

@mastersthesis{spengler2005maximum,
     author = {Spengler, Alex},
     title = {Maximum Margin {M}arkov Networks for {XML} Tag Relabelling},
     school = {{University of Karlsruhe (TH), Germany}},
     year = {2005},
     month = {December}
}
Spengler, Alex (2003): Neonatal Baby Monitoring. MSc thesis, University of Edinburgh, UK.

In this thesis we investigate the use of probabilistic graphical models for neonatal baby monitoring applications. In particular, we concentrate on detecting artefact patterns in physiological data using a conditional Gaussian approach. We describe a system that learns the necessary parameters from the given data and produces marginal posterior probabilities for the latent variables that have been used to model the artefact processes. It should be emphasised that the current system does not include the temporal evolution of the measured signals, but we indicate how this can be done within the presented framework. We also discuss our approach in the context of prior work and present ways to overcome identified problems.

@mastersthesis{spengler2003neonatal,
     author = {Spengler, Alex},
     title = {Neonatal Baby Monitoring},
     school = {{University of Edinburgh, UK}},
     year = {2003},
     month = {September}
}

Software & Corpora

Nabu - A Firefox/Firebug extension for visual HTML annotation.
News600 - A corpus of 604 semantically labeled news web pages for web content extraction.

Alex Spengler gratefully acknowledges support from Microsoft Research through its European PhD Scholarship Programme.